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Using Research and Reason in Education: How Teachers Can Use Scientifically Based Research to Make Curricular & Instructional Decisions

Paula J. Stanovich and Keith E. Stanovich University of Toronto

Produced by RMC Research Corporation, Portsmouth, New Hampshire

This publication was produced under National Institute for Literacy Contract No. ED-00CO-0093 with RMC Research Corporation. Sandra Baxter served as the contracting officer's technical representative. The views expressed herein do not necessarily represent the policies of the National Institute for Literacy. No official endorsement by the National Institute for Literacy or any product, commodity, service, or enterprise is intended or should be inferred.

The National Institute for Literacy

Sandra Baxter, Interim Executive Director Lynn Reddy, Communications Director

To order copies of this booklet, contact the National Institute for Literacy at EdPubs, PO Box 1398, Jessup, MD 20794-1398. Call 800-228-8813 or email [email protected] .

The National Institute for Literacy, an independent federal organization, supports the development of high quality state, regional, and national literacy services so that all Americans can develop the literacy skills they need to succeed at work, at home, and in the community.

The Partnership for Reading, a project administered by the National Institute for Literacy, is a collaborative effort of the National Institute for Literacy, the National Institute of Child Health and Human Development, the U.S. Department of Education, and the U.S. Department of Health and Human Services to make evidence-based reading research available to educators, parents, policy makers, and others with an interest in helping all people learn to read well.

Editorial support provided by C. Ralph Adler and Elizabeth Goldman, and design/production support provided by Diane Draper and Bob Kozman, all of RMC Research Corporation.

Introduction

In the recent move toward standards-based reform in public education, many educational reform efforts require schools to demonstrate that they are achieving educational outcomes with students performing at a required level of achievement. Federal and state legislation, in particular, has codified this standards-based movement and tied funding and other incentives to student achievement.

At first, demonstrating student learning may seem like a simple task, but reflection reveals that it is a complex challenge requiring educators to use specific knowledge and skills. Standards-based reform has many curricular and instructional prerequisites. The curriculum must represent the most important knowledge, skills, and attributes that schools want their students to acquire because these learning outcomes will serve as the basis of assessment instruments. Likewise, instructional methods should be appropriate for the designed curriculum. Teaching methods should lead to students learning the outcomes that are the focus of the assessment standards.

Standards- and assessment-based educational reforms seek to obligate schools and teachers to supply evidence that their instructional methods are effective. But testing is only one of three ways to gather evidence about the effectiveness of instructional methods. Evidence of instructional effectiveness can come from any of the following sources:

  • Demonstrated student achievement in formal testing situations implemented by the teacher, school district, or state;
  • Published findings of research-based evidence that the instructional methods being used by teachers lead to student achievement; or
  • Proof of reason-based practice that converges with a research-based consensus in the scientific literature. This type of justification of educational practice becomes important when direct evidence may be lacking (a direct test of the instructional efficacy of a particular method is absent), but there is a theoretical link to research-based evidence that can be traced.

Each of these methods has its pluses and minuses. While testing seems the most straightforward, it is not necessarily the clear indicator of good educational practice that the public seems to think it is. The meaning of test results is often not immediately clear. For example, comparing averages or other indicators of overall performance from tests across classrooms, schools, or school districts takes no account of the resources and support provided to a school, school district, or individual professional. Poor outcomes do not necessarily indict the efforts of physicians in Third World countries who work with substandard equipment and supplies. Likewise, objective evidence of below-grade or below-standard mean performance of a group of students should not necessarily indict their teachers if essential resources and supports (e.g., curriculum materials, institutional aid, parental cooperation) to support teaching efforts were lacking. However, the extent to which children could learn effectively even in under-equipped schools is not known because evidence-based practices are, by and large, not implemented. That is, there is evidence that children experiencing academic difficulties can achieve more educationally if they are taught with effective methods; sadly, scientific research about what works does not usually find its way into most classrooms.

Testing provides a useful professional calibrator, but it requires great contextual sensitivity in interpretation. It is not the entire solution for assessing the quality of instructional efforts. This is why research-based and reason-based educational practice are also crucial for determining the quality and impact of programs. Teachers thus have the responsibility to be effective users and interpreters of research. Providing a survey and synthesis of the most effective practices for a variety of key curriculum goals (such as literacy and numeracy) would seem to be a helpful idea, but no document could provide all of that information. (Many excellent research syntheses exist, such as the National Reading Panel, 2000; Snow, Burns, & Griffin, 1998; Swanson, 1999, but the knowledge base about effective educational practices is constantly being updated, and many issues remain to be settled.)

As professionals, teachers can become more effective and powerful by developing the skills to recognize scientifically based practice and, when the evidence is not available, use some basic research concepts to draw conclusions on their own. This paper offers a primer for those skills that will allow teachers to become independent evaluators of educational research.

The Formal Scientific Method and Scientific Thinking in Educational Practice

When you go to your family physician with a medical complaint, you expect that the recommended treatment has proven to be effective with many other patients who have had the same symptoms. You may even ask why a particular medication is being recommended for you. The doctor may summarize the background knowledge that led to that recommendation and very likely will cite summary evidence from the drug's many clinical trials and perhaps even give you an overview of the theory behind the drug's success in treating symptoms like yours.

All of this discussion will probably occur in rather simple terms, but that does not obscure the fact that the doctor has provided you with data to support a theory about your complaint and its treatment. The doctor has shared knowledge of medical science with you. And while everyone would agree that the practice of medicine has its "artful" components (for example, the creation of a healing relationship between doctor and patient), we have come to expect and depend upon the scientific foundation that underpins even the artful aspects of medical treatment. Even when we do not ask our doctors specifically for the data, we assume it is there, supporting our course of treatment.

Actually, Vaughn and Dammann (2001) have argued that the correct analogy is to say that teaching is in part a craft, rather than an art. They point out that craft knowledge is superior to alternative forms of knowledge such as superstition and folklore because, among other things, craft knowledge is compatible with scientific knowledge and can be more easily integrated with it. One could argue that in this age of education reform and accountability, educators are being asked to demonstrate that their craft has been integrated with science--that their instructional models, methods, and materials can be likened to the evidence a physician should be able to produce showing that a specific treatment will be effective. As with medicine, constructing teaching practice on a firm scientific foundation does not mean denying the craft aspects of teaching.

Architecture is another professional practice that, like medicine and education, grew from being purely a craft to a craft based firmly on a scientific foundation. Architects wish to design beautiful buildings and environments, but they must also apply many foundational principles of engineering and adhere to structural principles. If they do not, their buildings, however beautiful they may be, will not stand. Similarly, a teacher seeks to design lessons that stimulate students and entice them to learn--lessons that are sometimes a beauty to behold. But if the lessons are not based in the science of pedagogy, they, like poorly constructed buildings, will fail.

Education is informed by formal scientific research through the use of archival research-based knowledge such as that found in peer-reviewed educational journals. Preservice teachers are first exposed to the formal scientific research in their university teacher preparation courses (it is hoped), through the instruction received from their professors, and in their course readings (e.g., textbooks, journal articles). Practicing teachers continue their exposure to the results of formal scientific research by subscribing to and reading professional journals, by enrolling in graduate programs, and by becoming lifelong learners.

Scientific thinking in practice is what characterizes reflective teachers--those who inquire into their own practice and who examine their own classrooms to find out what works best for them and their students. What follows in this document is, first, a "short course" on how to become an effective consumer of the archival literature that results from the conduct of formal scientific research in education and, second, a section describing how teachers can think scientifically in their ongoing reflection about their classroom practice.

Being able to access mechanisms that evaluate claims about teaching methods and to recognize scientific research and its findings is especially important for teachers because they are often confronted with the view that "anything goes" in the field of education--that there is no such thing as best practice in education, that there are no ways to verify what works best, that teachers should base their practice on intuition, or that the latest fad must be the best way to teach, please a principal, or address local school reform. The "anything goes" mentality actually represents a threat to teachers' professional autonomy. It provides a fertile environment for gurus to sell untested educational "remedies" that are not supported by an established research base.

Teachers as independent evaluators of research evidence

One factor that has impeded teachers from being active and effective consumers of educational science has been a lack of orientation and training in how to understand the scientific process and how that process results in the cumulative growth of knowledge that leads to validated educational practice. Educators have only recently attempted to resolve educational disputes scientifically, and teachers have not yet been armed with the skills to evaluate disputes on their own.

Educational practice has suffered greatly because its dominant model for resolving or adjudicating disputes has been more political (with its corresponding factions and interest groups) than scientific. The field's failure to ground practice in the attitudes and values of science has made educators susceptible to the "authority syndrome" as well as fads and gimmicks that ignore evidence-based practice.

When our ancestors needed information about how to act, they would ask their elders and other wise people. Contemporary society and culture are much more complex. Mass communication allows virtually anyone (on the Internet, through self-help books) to proffer advice, to appear to be a "wise elder." The current problem is how to sift through the avalanche of misguided and uninformed advice to find genuine knowledge. Our problem is not information; we have tons of information. What we need are quality control mechanisms.

Peer-reviewed research journals in various disciplines provide those mechanisms. However, even with mechanisms like these in behavioral science and education, it is all too easy to do an "end run" around the quality control they provide. Powerful information dissemination outlets such as publishing houses and mass media frequently do not discriminate between good and bad information. This provides a fertile environment for gurus to sell untested educational "remedies" that are not supported by an established research base and, often, to discredit science, scientific evidence, and the notion of research-based best practice in education. As Gersten (2001) notes, both seasoned and novice teachers are "deluged with misinformation" (p. 45).

We need tools for evaluating the credibility of these many and varied sources of information; the ability to recognize research-based conclusions is especially important. Acquiring those tools means understanding scientific values and learning methods for making inferences from the research evidence that arises through the scientific process. These values and methods were recently summarized by a panel of the National Academy of Sciences convened on scientific inquiry in education (Shavelson & Towne, 2002), and our discussion here will be completely consistent with the conclusions of that NAS panel.

The scientific criteria for evaluating knowledge claims are not complicated and could easily be included in initial teacher preparation programs, but they usually are not (which deprives teachers from an opportunity to become more efficient and autonomous in their work right at the beginning of their careers). These criteria include:

  • the publication of findings in refereed journals (scientific publications that employ a process of peer review),
  • the duplication of the results by other investigators, and
  • a consensus within a particular research community on whether there is a critical mass of studies that point toward a particular conclusion.

In their discussion of the evolution of the American Educational Research Association (AERA) conference and the importance of separating research evidence from opinion when making decisions about instructional practice, Levin and O'Donnell (2000) highlight the importance of enabling teachers to become independent evaluators of research evidence. Being aware of the importance of research published in peer-reviewed scientific journals is only the first step because this represents only the most minimal of criteria. Following is a review of some of the principles of research-based evaluation that teachers will find useful in their work.

Publicly verifiable research conclusions: Replication and Peer Review

Source credibility: the consumer protection of peer reviewed journals..

The front line of defense for teachers against incorrect information in education is the existence of peer-reviewed journals in education, psychology, and other related social sciences. These journals publish empirical research on topics relevant to classroom practice and human cognition and learning. They are the first place that teachers should look for evidence of validated instructional practices.

As a general quality control mechanism, peer review journals provide a "first pass" filter that teachers can use to evaluate the plausibility of educational claims. To put it more concretely, one ironclad criterion that will always work for teachers when presented with claims of uncertain validity is the question: Have findings supporting this method been published in recognized scientific journals that use some type of peer review procedure? The answer to this question will almost always separate pseudoscientific claims from the real thing.

In a peer review, authors submit a paper to a journal for publication, where it is critiqued by several scientists. The critiques are reviewed by an editor (usually a scientist with an extensive history of work in the specialty area covered by the journal). The editor then decides whether the weight of opinion warrants immediate publication, publication after further experimentation and statistical analysis, or rejection because the research is flawed or does not add to the knowledge base. Most journals carry a statement of editorial policy outlining their exact procedures for publication, so it is easy to check whether a journal is in fact, peer-reviewed.

Peer review is a minimal criterion, not a stringent one. Not all information in peer-reviewed scientific journals is necessarily correct, but it has at the very least undergone a cycle of peer criticism and scrutiny. However, it is because the presence of peer-reviewed research is such a minimal criterion that its absence becomes so diagnostic. The failure of an idea, a theory, an educational practice, behavioral therapy, or a remediation technique to have adequate documentation in the peer-reviewed literature of a scientific discipline is a very strong indication to be wary of the practice.

The mechanisms of peer review vary somewhat from discipline to discipline, but the underlying rationale is the same. Peer review is one way (replication of a research finding is another) that science institutionalizes the attitudes of objectivity and public criticism. Ideas and experimentation undergo a honing process in which they are submitted to other critical minds for evaluation. Ideas that survive this critical process have begun to meet the criterion of public verifiability. The peer review process is far from perfect, but it really is the only external consumer protection that teachers have.

The history of reading instruction illustrates the high cost that is paid when the peer-reviewed literature is ignored, when the normal processes of scientific adjudication are replaced with political debates and rhetorical posturing. A vast literature has been generated on best practices that foster children's reading acquisition (Adams, 1990; Anderson, Hiebert, Scott, & Wilkinson, 1985; Chard & Osborn, 1999; Cunningham & Allington, 1994; Ehri, Nunes, Stahl, & Willows, 2001; Moats, 1999; National Reading Panel, 2000; Pearson, 1993; Pressley, 1998; Pressley, Rankin, & Yokol, 1996; Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2002; Reading Coherence Initiative, 1999; Snow, Burns, & Griffin, 1998; Spear-Swerling & Sternberg, 2001). Yet much of this literature remains unknown to many teachers, contributing to the frustrating lack of clarity about accepted, scientifically validated findings and conclusions on reading acquisition.

Teachers should also be forewarned about the difference between professional education journals that are magazines of opinion in contrast to journals where primary reports of research, or reviews of research, are peer reviewed. For example, the magazines Phi Delta Kappan and Educational Leadership both contain stimulating discussions of educational issues, but neither is a peer-reviewed journal of original research. In contrast, the American Educational Research Journal (a flagship journal of the AERA) and the Journal of Educational Psychology (a flagship journal of the American Psychological Association) are both peer-reviewed journals of original research. Both are main sources for evidence on validated techniques of reading instruction and for research on aspects of the reading process that are relevant to a teacher's instructional decisions.

This is true, too, of presentations at conferences of educational organizations. Some are data-based presentations of original research. Others are speeches reflecting personal opinion about educational problems. While these talks can be stimulating and informative, they are not a substitute for empirical research on educational effectiveness.

Replication and the importance of public verifiability.

Research-based conclusions about educational practice are public in an important sense: they do not exist solely in the mind of a particular individual but have been submitted to the scientific community for criticism and empirical testing by others. Knowledge considered "special"--the province of the thought of an individual and immune from scrutiny and criticism by others--can never have the status of scientific knowledge. Research-based conclusions, when published in a peer reviewed journal, become part of the public realm, available to all, in a way that claims of "special expertise" are not.

Replication is the second way that science uses to make research-based conclusions concrete and "public." In order to be considered scientific, a research finding must be presented to other researchers in the scientific community in a way that enables them to attempt the same experiment and obtain the same results. When the same results occur, the finding has been replicated . This process ensures that a finding is not the result of the errors or biases of a particular investigator. Replicable findings become part of the converging evidence that forms the basis of a research-based conclusion about educational practice.

John Donne told us that "no man is an island." Similarly, in science, no researcher is an island. Each investigator is connected to the research community and its knowledge base. This interconnection enables science to grow cumulatively and for research-based educational practice to be built on a convergence of knowledge from a variety of sources. Researchers constantly build on previous knowledge in order to go beyond what is currently known. This process is possible only if research findings are presented in such a way that any investigator can use them to build on.

Philosopher Daniel Dennett (1995) has said that science is "making mistakes in public. Making mistakes for all to see, in the hopes of getting the others to help with the corrections" (p. 380). We might ask those proposing an educational innovation for the evidence that they have in fact "made some mistakes in public." Legitimate scientific disciplines can easily provide such evidence. For example, scientists studying the psychology of reading once thought that reading difficulties were caused by faulty eye movements. This hypothesis has been shown to be in error, as has another that followed it, that so-called visual reversal errors were a major cause of reading difficulty. Both hypotheses were found not to square with the empirical evidence (Rayner, 1998; Share & Stanovich, 1995). The hypothesis that reading difficulties can be related to language difficulties at the phonological level has received much more support (Liberman, 1999; National Reading Panel, 2000; Rayner, Foorman, Perfetti, Pesetsky, & Seidenberg, 2002; Shankweiler, 1999; Stanovich, 2000).

After making a few such "errors" in public, reading scientists have begun, in the last 20 years, to get it right. But the only reason teachers can have confidence that researchers are now "getting it right" is that researchers made it open, public knowledge when they got things wrong. Proponents of untested and pseudoscientific educational practices will never point to cases where they "got it wrong" because they are not committed to public knowledge in the way that actual science is. These proponents do not need, as Dennett says, "to get others to help in making the corrections" because they have no intention of correcting their beliefs and prescriptions based on empirical evidence.

Education is so susceptible to fads and unproven practices because of its tacit endorsement of a personalistic view of knowledge acquisition--one that is antithetical to the scientific value of the public verifiability of knowledge claims. Many educators believe that knowledge resides within particular individuals--with particularly elite insights--who then must be called upon to dispense this knowledge to others. Indeed, some educators reject public, depersonalized knowledge in social science because they believe it dehumanizes people. Science, however, with its conception of publicly verifiable knowledge, actually democratizes knowledge. It frees practitioners and researchers from slavish dependence on authority.

Subjective, personalized views of knowledge degrade the human intellect by creating conditions that subjugate it to an elite whose "personal" knowledge is not accessible to all (Bronowski, 1956, 1977; Dawkins, 1998; Gross, Levitt, & Lewis, 1997; Medawar, 1982, 1984, 1990; Popper, 1972; Wilson, 1998). Empirical science, by generating knowledge and moving it into the public domain, is a liberating force. Teachers can consult the research and decide for themselves whether the state of the literature is as the expert portrays it. All teachers can benefit from some rudimentary grounding in the most fundamental principles of scientific inference. With knowledge of a few uncomplicated research principles, such as control, manipulation, and randomization, anyone can enter the open, public discourse about empirical findings. In fact, with the exception of a few select areas such as the eye movement research mentioned previously, much of the work described in noted summaries of reading research (e.g., Adams, 1990; Snow, Burns, & Griffin, 1998) could easily be replicated by teachers themselves.

There are many ways that the criteria of replication and peer review can be utilized in education to base practitioner training on research-based best practice. Take continuing teacher education in the form of inservice sessions, for example. Teachers and principals who select speakers for professional development activities should ask speakers for the sources of their conclusions in the form of research evidence in peer-reviewed journals. They should ask speakers for bibliographies of the research evidence published on the practices recommended in their presentations.

The science behind research-based practice relies on systematic empiricism

Empiricism is the practice of relying on observation. Scientists find out about the world by examining it. The refusal by some scientists to look into Galileo's telescope is an example of how empiricism has been ignored at certain points in history. It was long believed that knowledge was best obtained through pure thought or by appealing to authority. Galileo claimed to have seen moons around the planet Jupiter. Another scholar, Francesco Sizi, attempted to refute Galileo, not with observations, but with the following argument:

There are seven windows in the head, two nostrils, two ears, two eyes and a mouth; so in the heavens there are two favorable stars, two unpropitious, two luminaries, and Mercury alone undecided and indifferent. From which and many other similar phenomena of nature such as the seven metals, etc., which it were tedious to enumerate, we gather that the number of planets is necessarily seven...ancient nations, as well as modern Europeans, have adopted the division of the week into seven days, and have named them from the seven planets; now if we increase the number of planets, this whole system falls to the ground...moreover, the satellites are invisible to the naked eye and therefore can have no influence on the earth and therefore would be useless and therefore do not exist. (Holton & Roller, 1958, p. 160)

Three centuries of the demonstrated power of the empirical approach give us an edge on poor Sizi. Take away those years of empiricism, and many of us might have been there nodding our heads and urging him on. In fact, the empirical approach is not necessarily obvious, which is why we often have to teach it, even in a society that is dominated by science.

Empiricism pure and simple is not enough, however. Observation itself is fine and necessary, but pure, unstructured observation of the natural world will not lead to scientific knowledge. Write down every observation you make from the time you get up in the morning to the time you go to bed on a given day. When you finish, you will have a great number of facts, but you will not have a greater understanding of the world. Scientific observation is termed systematic because it is structured so that the results of the observation reveal something about the underlying causal structure of events in the world. Observations are structured so that, depending upon the outcome of the observation, some theories of the causes of the outcome are supported and others rejected.

Teachers can benefit by understanding two things about research and causal inferences. The first is the simple (but sometimes obscured) fact that statements about best instructional practices are statements that contain a causal claim. These statements claim that one type of method or practice causes superior educational outcomes. Second, teachers must understand how the logic of the experimental method provides the critical support for making causal inferences.

Science addresses testable questions

Science advances by positing theories to account for particular phenomena in the world, by deriving predictions from these theories, by testing the predictions empirically, and by modifying the theories based on the tests (the sequence is typically theory -> prediction -> test -> theory modification). What makes a theory testable? A theory must have specific implications for observable events in the natural world.

Science deals only with a certain class of problem: the kind that is empirically solvable. That does not mean that different classes of problems are inherently solvable or unsolvable and that this division is fixed forever. Quite the contrary: some problems that are currently unsolvable may become solvable as theory and empirical techniques become more sophisticated. For example, decades ago historians would not have believed that the controversial issue of whether Thomas Jefferson had a child with his slave Sally Hemings was an empirically solvable question. Yet, by 1998, this problem had become solvable through advances in genetic technology, and a paper was published in the journal Nature (Foster, Jobling, Taylor, Donnelly, Deknijeff, Renemieremet, Zerjal, & Tyler-Smith, 1998) on the question.

The criterion of whether a problem is "testable" is called the falsifiability criterion: a scientific theory must always be stated in such a way that the predictions derived from it can potentially be shown to be false. The falsifiability criterion states that, for a theory to be useful, the predictions drawn from it must be specific. The theory must go out on a limb, so to speak, because in telling us what should happen, the theory must also imply that certain things will not happen. If these latter things do happen, it is a clear signal that something is wrong with the theory. It may need to be modified, or we may need to look for an entirely new theory. Either way, we will end up with a theory that is closer to the truth.

In contrast, if a theory does not rule out any possible observations, then the theory can never be changed, and we are frozen into our current way of thinking with no possibility of progress. A successful theory cannot posit or account for every possible happening. Such a theory robs itself of any predictive power.

What we are talking about here is a certain type of intellectual honesty. In science, the proponent of a theory is always asked to address this question before the data are collected: "What data pattern would cause you to give up, or at least to alter, this theory?" In the same way, the falsifiability criterion is a useful consumer protection for the teacher when evaluating claims of educational effectiveness. Proponents of an educational practice should be asked for evidence; they should also be willing to admit that contrary data will lead them to abandon the practice. True scientific knowledge is held tentatively and is subject to change based on contrary evidence. Educational remedies not based on scientific evidence will often fail to put themselves at risk by specifying what data patterns would prove them false.

Objectivity and intellectual honesty

Objectivity, another form of intellectual honesty in research, means that we let nature "speak for itself" without imposing our wishes on it--that we report the results of experimentation as accurately as we can and that we interpret them as fairly as possible. (The fact that this goal is unattainable for any single human being should not dissuade us from holding objectivity as a value.)

In the language of the general public, open-mindedness means being open to possible theories and explanations for a particular phenomenon. But in science it means that and something more. Philosopher Jonathan Adler (1998) teaches us that science values another aspect of open-mindedness even more highly: "What truly marks an open-minded person is the willingness to follow where evidence leads. The open-minded person is willing to defer to impartial investigations rather than to his own predilections...Scientific method is attunement to the world, not to ourselves" (p. 44).

Objectivity is critical to the process of science, but it does not mean that such attitudes must characterize each and every scientist for science as a whole to work. Jacob Bronowski (1973, 1977) often argued that the unique power of science to reveal knowledge about the world does not arise because scientists are uniquely virtuous (that they are completely objective or that they are never biased in interpreting findings, for example). It arises because fallible scientists are immersed in a process of checks and balances --a process in which scientists are always there to criticize and to root out errors. Philosopher Daniel Dennett (1999/2000) points out that "scientists take themselves to be just as weak and fallible as anybody else, but recognizing those very sources of error in themselvesÉthey have devised elaborate systems to tie their own hands, forcibly preventing their frailties and prejudices from infecting their results" (p. 42). More humorously, psychologist Ray Nickerson (1998) makes the related point that the vanities of scientists are actually put to use by the scientific process, by noting that it is "not so much the critical attitude that individual scientists have taken with respect to their own ideas that has given science its success...but more the fact that individual scientists have been highly motivated to demonstrate that hypotheses that are held by some other scientists are false" (p. 32). These authors suggest that the strength of scientific knowledge comes not because scientists are virtuous, but from the social process where scientists constantly cross-check each others' knowledge and conclusions.

The public criteria of peer review and replication of findings exist in part to keep checks on the objectivity of individual scientists. Individuals cannot hide bias and nonobjectivity by personalizing their claims and keeping them from public scrutiny. Science does not accept findings that have failed the tests of replication and peer review precisely because it wants to ensure that all findings in science are in the public domain, as defined above. Purveyors of pseudoscientific educational practices fail the test of objectivity and are often identifiable by their attempts to do an "end run" around the public mechanisms of science by avoiding established peer review mechanisms and the information-sharing mechanisms that make replication possible. Instead, they attempt to promulgate their findings directly to consumers, such as teachers.

The principle of converging evidence

The principle of converging evidence has been well illustrated in the controversies surrounding the teaching of reading. The methods of systematic empiricism employed in the study of reading acquisition are many and varied. They include case studies, correlational studies, experimental studies, narratives, quasi-experimental studies, surveys, epidemiological studies and many others. The results of many of these studies have been synthesized in several important research syntheses (Adams, 1990; Ehri et al., 2001; National Reading Panel, 2000; Pressley, 1998; Rayner et al., 2002; Reading Coherence Initiative, 1999; Share & Stanovich, 1995; Snow, Burns, & Griffin, 1998; Snowling, 2000; Spear-Swerling & Sternberg, 2001; Stanovich, 2000). These studies were used in a process of establishing converging evidence, a principle that governs the drawing of the conclusion that a particular educational practice is research-based.

The principle of converging evidence is applied in situations requiring a judgment about where the "preponderance of evidence" points. Most areas of science contain competing theories. The extent to which a particular study can be seen as uniquely supporting one particular theory depends on whether other competing explanations have been ruled out. A particular experimental result is never equally relevant to all competing theories. An experiment may be a very strong test of one or two alternative theories but a weak test of others. Thus, research is considered highly convergent when a series of experiments consistently supports a given theory while collectively eliminating the most important competing explanations. Although no single experiment can rule out all alternative explanations, taken collectively, a series of partially diagnostic experiments can lead to a strong conclusion if the data converge.

Contrast this idea of converging evidence with the mistaken view that a problem in science can be solved with a single, crucial experiment, or that a single critical insight can advance theory and overturn all previous knowledge. This view of scientific progress fits nicely with the operation of the news media, in which history is tracked by presenting separate, disconnected "events" in bite-sized units. This is a gross misunderstanding of scientific progress and, if taken too seriously, leads to misconceptions about how conclusions are reached about research-based practices.

One experiment rarely decides an issue, supporting one theory and ruling out all others. Issues are most often decided when the community of scientists gradually begins to agree that the preponderance of evidence supports one alternative theory rather than another. Scientists do not evaluate data from a single experiment that has finally been designed in the perfect way. They most often evaluate data from dozens of experiments, each containing some flaws but providing part of the answer.

Although there are many ways in which an experiment can go wrong (or become confounded ), a scientist with experience working on a particular problem usually has a good idea of what most of the critical factors are, and there are usually only a few. The idea of converging evidence tells us to examine the pattern of flaws running through the research literature because the nature of this pattern can either support or undermine the conclusions that we might draw.

For example, suppose that the findings from a number of different experiments were largely consistent in supporting a particular conclusion. Given the imperfect nature of experiments, we would evaluate the extent and nature of the flaws in these studies. If all the experiments were flawed in a similar way, this circumstance would undermine confidence in the conclusions drawn from them because the consistency of the outcome may simply have resulted from a particular, consistent flaw. On the other hand, if all the experiments were flawed in different ways, our confidence in the conclusions increases because it is less likely that the consistency in the results was due to a contaminating factor that confounded all the experiments. As Anderson and Anderson (1996) note, "When a conceptual hypothesis survives many potential falsifications based on different sets of assumptions, we have a robust effect." (p. 742).

Suppose that five different theoretical summaries (call them A, B, C, D, and E) of a given set of phenomena exist at one time and are investigated in a series of experiments. Suppose that one set of experiments represents a strong test of theories A, B, and C, and that the data largely refute theories A and B and support C. Imagine also that another set of experiments is a particularly strong test of theories C, D, and E, and that the data largely refute theories D and E and support C. In such a situation, we would have strong converging evidence for theory C. Not only do we have data supportive of theory C, but we have data that contradict its major competitors. Note that no one experiment tests all the theories, but taken together, the entire set of experiments allows a strong inference.

In contrast, if the two sets of experiments each represent strong tests of B, C, and E, and the data strongly support C and refute B and E, the overall support for theory C would be less strong than in our previous example. The reason is that, although data supporting theory C have been generated, there is no strong evidence ruling out two viable alternative theories (A and D). Thus research is highly convergent when a series of experiments consistently supports a given theory while collectively eliminating the most important competing explanations. Although no single experiment can rule out all alternative explanations, taken collectively, a series of partially diagnostic experiments can lead to a strong conclusion if the data converge in the manner of our first example.

Increasingly, the combining of evidence from disparate studies to form a conclusion is being done more formally by the use of the statistical technique termed meta-analysis (Cooper & Hedges, 1994; Hedges & Olkin, 1985; Hunter & Schmidt, 1990; Rosenthal, 1995; Schmidt, 1992; Swanson, 1999) which has been used extensively to establish whether various medical practices are research based. In a medical context, meta-analysis:

involves adding together the data from many clinical trials to create a single pool of data big enough to eliminate much of the statistical uncertainty that plagues individual trials...The great virtue of meta-analysis is that clear findings can emerge from a group of studies whose findings are scattered all over the map. (Plotkin,1996, p. 70)

The use of meta-analysis for determining the research validation of educational practices is just the same as in medicine. The effects obtained when one practice is compared against another are expressed in a common statistical metric that allows comparison of effects across studies. The findings are then statistically amalgamated in some standard ways (Cooper & Hedges, 1994; Hedges & Olkin, 1985; Swanson, 1999) and a conclusion about differential efficacy is reached if the amalgamation process passes certain statistical criteria. In some cases, of course, no conclusion can be drawn with confidence, and the result of the meta-analysis is inconclusive.

More and more commentators on the educational research literature are calling for a greater emphasis on meta-analysis as a way of dampening the contentious disputes about conflicting studies that plague education and other behavioral sciences (Kavale & Forness, 1995; Rosnow & Rosenthal, 1989; Schmidt, 1996; Stanovich, 2001; Swanson, 1999). The method is useful for ending disputes that seem to be nothing more than a "he-said, she-said" debate. An emphasis on meta-analysis has often revealed that we actually have more stable and useful findings than is apparent from a perusal of the conflicts in our journals.

The National Reading Panel (2000) found just this in their meta-analysis of the evidence surrounding several issues in reading education. For example, they concluded that the results of a meta-analysis of the results of 66 comparisons from 38 different studies indicated "solid support for the conclusion that systematic phonics instruction makes a bigger contribution to children's growth in reading than alternative programs providing unsystematic or no phonics instruction" (p. 2-84). In another section of their report, the National Reading Panel reported that a meta-analysis of 52 studies of phonemic awareness training indicated that "teaching children to manipulate the sounds in language helps them learn to read. Across the various conditions of teaching, testing, and participant characteristics, the effect sizes were all significantly greater than chance and ranged from large to small, with the majority in the moderate range. Effects of phonemic awareness training on reading lasted well beyond the end of training" (p. 2-5).

A statement by a task force of the American Psychological Association (Wilkinson, 1999) on statistical methods in psychology journals provides an apt summary for this section. The task force stated that investigators should not "interpret a single study's results as having importance independent of the effects reported elsewhere in the relevant literature" (p. 602). Science progresses by convergence upon conclusions. The outcomes of one study can only be interpreted in the context of the present state of the convergence on the particular issue in question.

The logic of the experimental method

Scientific thinking is based on the ideas of comparison, control, and manipulation . In a true experimental study, these characteristics of scientific investigation must be arranged to work in concert.

Comparison alone is not enough to justify a causal inference. In methodology texts, correlational investigations (which involve comparison only) are distinguished from true experimental investigations that warrant much stronger causal inferences because they involve comparison, control, and manipulation. The mere existence of a relationship between two variables does not guarantee that changes in one are causing changes in the other. Correlation does not imply causation.

There are two potential problems with drawing causal inferences from correlational evidence. The first is called the third-variable problem. It occurs when the correlation between the two variables does not indicate a direct causal path between them but arises because both variables are related to a third variable that has not even been measured.

The second reason is called the directionality problem. It creates potential interpretive difficulties because even if two variables have a direct causal relationship, the direction of that relationship is not indicated by the mere presence of the correlation. In short, a correlation between variables A and B could arise because changes in A are causing changes in B or because changes in B are causing changes in A. The mere presence of the correlation does not allow us to decide between these two possibilities.

The heart of the experimental method lies in manipulation and control. In contrast to a correlational study, where the investigator simply observes whether the natural fluctuation in two variables displays a relationship, the investigator in a true experiment manipulates the variable thought to be the cause (the independent variable) and looks for an effect on the variable thought to be the effect (the dependent variable ) while holding all other variables constant by control and randomization. This method removes the third-variable problem because, in the natural world, many different things are related. The experimental method may be viewed as a way of prying apart these naturally occurring relationships. It does so because it isolates one particular variable (the hypothesized cause) by manipulating it and holding everything else constant (control).

When manipulation is combined with a procedure known as random assignment (in which the subjects themselves do not determine which experimental condition they will be in but, instead, are randomly assigned to one of the experimental groups), scientists can rule out alternative explanations of data patterns. By using manipulation, experimental control, and random assignment, investigators construct stronger comparisons so that the outcome eliminates alternative theories and explanations.

The need for both correlational methods and true experiments

As strong as they are methodologically, studies employing true experimental logic are not the only type that can be used to draw conclusions. Correlational studies have value. The results from many different types of investigation, including correlational studies, can be amalgamated to derive a general conclusion. The basis for conclusion rests on the convergence observed from the variety of methods used. This is most certainly true in classroom and curriculum research. It is necessary to amalgamate the results from not only experimental investigations, but correlational studies, nonequivalent control group studies, time series designs, and various other quasi-experimental designs and multivariate correlational designs, All have their strengths and weaknesses. For example, it is often (but not always) the case that experimental investigations are high in internal validity, but limited in external validity, whereas correlational studies are often high in external validity, but low in internal validity.

Internal validity concerns whether we can infer a causal effect for a particular variable. The more a study employs the logic of a true experiment (i.e., includes manipulation, control, and randomization), the more we can make a strong causal inference. External validity concerns the generalizability of the conclusion to the population and setting of interest. Internal and external validity are often traded off across different methodologies. Experimental laboratory investigations are high in internal validity but may not fully address concerns about external validity. Field classroom investigations, on the other hand, are often quite high in external validity but because of the logistical difficulties involved in carrying them out, they are often quite low in internal validity. That is why we need to look for a convergence of results, not just consistency from one method. Convergence increases our confidence in the external and internal validity of our conclusions.

Again, this underscores why correlational studies can contribute to knowledge. First, some variables simply cannot be manipulated for ethical reasons (for instance, human malnutrition or physical disabilities). Other variables, such as birth order, sex, and age, are inherently correlational because they cannot be manipulated, and therefore the scientific knowledge concerning them must be based on correlational evidence. Finally, logistical difficulties in classroom and curriculum research often make it impossible to achieve the logic of the true experiment. However, this circumstance is not unique to educational or psychological research. Astronomers obviously cannot manipulate all the variables affecting the objects they study, yet they are able to arrive at conclusions.

Complex correlational techniques are essential in the absence of experimental research because complex correlational statistics such as multiple regression, path analysis, and structural equation modeling that allow for the partial control of third variables when those variables can be measured. These statistics allow us to recalculate the correlation between two variables after the influence of other variables is removed. If a potential third variable can be measured, complex correlational statistics can help us determine whether that third variable is determining the relationship. These correlational statistics and designs help to rule out certain causal hypotheses, even if they cannot demonstrate the true causal relation definitively.

Stages of scientific investigation: The Role of Case Studies and Qualitative Investigations

The educational literature includes many qualitative investigations that focus less on issues of causal explanation and variable control and more on thick description , in the manner of the anthropologist (Geertz, 1973, 1979). The context of a person's behavior is described as much as possible from the standpoint of the participant. Many different fields (e.g., anthropology, psychology, education) contain case studies where the focus is detailed description and contextualization of the situation of a single participant (or very few participants).

The usefulness of case studies and qualitative investigations is strongly determined by how far scientific investigation has advanced in a particular area. The insights gained from case studies or qualitative investigations may be quite useful in the early stages of an investigation of a certain problem. They can help us determine which variables deserve more intense study by drawing attention to heretofore unrecognized aspects of a person's behavior and by suggesting how understanding of behavior might be sharpened by incorporating the participant's perspective.

However, when we move from the early stages of scientific investigation, where case studies may be very useful, to the more mature stages of theory testing--where adjudicating between causal explanations is the main task--the situation changes drastically. Case studies and qualitative description are not useful at the later stages of scientific investigation because they cannot be used to confirm or disconfirm a particular causal theory. They lack the comparative information necessary to rule out alternative explanations.

Where qualitative investigations are useful relates strongly to a distinction in philosophy of science between the context of discovery and the context of justification . Qualitative research, case studies, and clinical observations support a context of discovery where, as Levin and O'Donnell (2000) note in an educational context, such research must be regarded as "preliminary/exploratory, observational, hypothesis generating" (p. 26). They rightly point to the essential importance of qualitative investigations because "in the early stages of inquiry into a research topic, one has to look before one can leap into designing interventions, making predictions, or testing hypotheses" (p. 26). The orientation provided by qualitative investigations is critical in such cases. Even more important, the results of quantitative investigations--which must sometimes abstract away some of the contextual features of a situation--are often contextualized by the thick situational description provided by qualitative work.

However, in the context of justification, variables must be measured precisely, large groups must be tested to make sure the conclusion generalizes and, most importantly, many variables must be controlled because alternative causal explanations must be ruled out. Gersten (2001) summarizes the value of qualitative research accurately when he says that "despite the rich insights they often provide, descriptive studies cannot be used as evidence for an intervention's efficacy...descriptive research can only suggest innovative strategies to teach students and lay the groundwork for development of such strategies" (p. 47). Qualitative research does, however, help to identify fruitful directions for future experimental studies.

Nevertheless, here is why the sole reliance on qualitative techniques to determine the effectiveness of curricula and instructional strategies has become problematic. As a researcher, you desire to do one of two things.

Objective A

The researcher wishes to make some type of statement about a relationship, however minimal. That is, you at least want to use terms like greater than, or less than, or equal to. You want to say that such and such an educational program or practice is better than another. "Better than" and "worse than" are, of course, quantitative statements--and, in the context of issues about what leads to or fosters greater educational achievement, they are causal statements as well . As quantitative causal statements, the support for such claims obviously must be found in the experimental logic that has been outlined above. To justify such statements, you must adhere to the canons of quantitative research logic.

Objective B

The researcher seeks to adhere to an exclusively qualitative path that abjures statements about relationships and never uses comparative terms of magnitude. The investigator desires to simply engage in thick description of a domain that may well prompt hypotheses when later work moves on to the more quantitative methods that are necessary to justify a causal inference.

Investigators pursuing Objective B are doing essential work. They provide quantitative information with suggestions for richer hypotheses to study. In education, however, investigators sometimes claim to be pursuing Objective B but slide over into Objective A without realizing they have made a crucial switch. They want to make comparative, or quantitative, statements, but have not carried out the proper types of investigation to justify them. They want to say that a certain educational program is better than another (that is, it causes better school outcomes). They want to give educational strictures that are assumed to hold for a population of students, not just to the single or few individuals who were the objects of the qualitative study. They want to condemn an educational practice (and, by inference, deem an alternative quantitatively and causally better). But instead of taking the necessary course of pursuing Objective A, they carry out their investigation in the manner of Objective B.

Let's recall why the use of single case or qualitative description as evidence in support of a particular causal explanation is inappropriate. The idea of alternative explanations is critical to an understanding of theory testing. The goal of experimental design is to structure events so that support of one particular explanation simultaneously disconfirms other explanations. Scientific progress can occur only if the data that are collected rule out some explanations. Science sets up conditions for the natural selection of ideas. Some survive empirical testing and others do not.

This is the honing process by which ideas are sifted so that those that contain the most truth are found. But there must be selection in this process: data collected as support for a particular theory must not leave many other alternative explanations as equally viable candidates. For this reason, scientists construct control or comparison groups in their experimentation. These groups are formed so that, when their results are compared with those from an experimental group, some alternative explanations are ruled out.

Case studies and qualitative description lack the comparative information necessary to prove that a particular theory or educational practice is superior, because they fail to test an alternative; they rule nothing out. Take the seminal work of Jean Piaget for example. His case studies were critical in pointing developmental psychology in new and important directions, but many of his theoretical conclusions and causal explanations did not hold up in controlled experiments (Bjorklund, 1995; Goswami, 1998; Siegler, 1991).

In summary, as educational psychologist Richard Mayer (2000) notes, "the domain of science includes both some quantitative and qualitative methodologies" (p. 39), and the key is to use each where it is most effective (see Kamil, 1995). Likewise, in their recent book on research-based best practices in comprehension instruction, Block and Pressley (2002) argue that future progress in understanding how comprehension works will depend on a healthy interaction between qualitative and quantitative approaches. They point out that getting an initial idea of the comprehension processes involved in hypertext and Web-based environments will involve detailed descriptive studies using think-alouds and assessments of qualitative decision making. Qualitative studies of real reading environments will set the stage for more controlled investigations of causal hypotheses.

The progression to more powerful methods

A final useful concept is the progression to more powerful research methods ("more powerful" in this context meaning more diagnostic of a causal explanation). Research on a particular problem often proceeds from weaker methods (ones less likely to yield a causal explanation) to ones that allow stronger causal inferences. For example, interest in a particular hypothesis may originally emerge from a particular case study of unusual interest. This is the proper role for case studies: to suggest hypotheses for further study with more powerful techniques and to motivate scientists to apply more rigorous methods to a research problem. Thus, following the case studies, researchers often undertake correlational investigations to verify whether the link between variables is real rather than the result of the peculiarities of a few case studies. If the correlational studies support the relationship between relevant variables, then researchers will attempt experiments in which variables are manipulated in order to isolate a causal relationship between the variables.

Summary of principles that support research-based inferences about best practice

Our sketch of the principles that support research-based inferences about best practice in education has revealed that:

  • Science progresses by investigating solvable, or testable, empirical problems.
  • To be testable, a theory must yield predictions that could possible be shown to be wrong.
  • The concepts in the theories in science evolve as evidence accumulates. Scientific knowledge is not infallible knowledge, but knowledge that has at least passed some minimal tests. The theories behind research-based practice can be proven wrong, and therefore they contain a mechanism for growth and advancement.
  • Theories are tested by systematic empiricism. The data obtained from empirical research are in the public domain in the sense that they are presented in a manner that allows replication and criticism by other scientists.
  • Data and theories in science are considered in the public domain only after publication in peer-reviewed scientific journals.
  • Empiricism is systematic because it strives for the logic of control and manipulation that characterizes a true experiment.
  • Correlational techniques are helpful when the logic of an experiment cannot be approximated, but because these techniques only help rule out hypotheses, they are considered weaker than true experimental methods.
  • Researchers use many different methods to arrive at their conclusions, and the strengths and weaknesses of these methods vary. Most often, conclusions are drawn only after a slow accumulation of data from many studies.

Scientific thinking in educational practice: Reason-based practice in the absence of direct evidence

Some areas in educational research, to date, lack a research-based consensus, for a number of reasons. Perhaps the problem or issue has not been researched extensively. Perhaps research into the issue is in the early stages of investigation, where descriptive studies are suggesting interesting avenues, but no controlled research justifying a causal inference has been completed. Perhaps many correlational studies and experiments have been conducted on the issue, but the research evidence has not yet converged in a consistent direction.

Even if teachers know the principles of scientific evaluation described earlier, the research literature sometimes fails to give them clear direction. They will have to fall back on their own reasoning processes as informed by their own teaching experiences. In those cases, teachers still have many ways of reasoning scientifically.

Tracing the link from scientific research to scientific thinking in practice

Scientific thinking in can be done in several ways. Earlier we discussed different types of professional publications that teachers can read to improve their practice. The most important defining feature of these outlets is whether they are peer reviewed. Another defining feature is whether the publication contains primary research rather than presenting opinion pieces or essays on educational issues. If a journal presents primary research, we can evaluate the research using the formal scientific principles outlined above.

If the journal is presenting opinion pieces about what constitutes best practice, we need to trace the link between those opinions and archival peer-reviewed research. We would look to see whether the authors have based their opinions on peer-reviewed research by reading the reference list. Do the authors provide a significant amount of original research citations (is their opinion based on more than one study)? Do the authors cite work other than their own (have the results been replicated)? Are the cited journals peer-reviewed? For example, in the case of best practice for reading instruction, if we came across an article in an opinion-oriented journal such as Intervention in School and Clinic, we might look to see if the authors have cited work that has appeared in such peer-reviewed journals as Journal of Educational Psychology , Elementary School Journal , Journal of Literacy Research , Scientific Studies of Reading , or the Journal of Learning Disabilities .

These same evaluative criteria can be applied to presenters at professional development workshops or papers given at conferences. Are they conversant with primary research in the area on which they are presenting? Can they provide evidence for their methods and does that evidence represent a scientific consensus? Do they understand what is required to justify causal statements? Are they open to the possibility that their claims could be proven false? What evidence would cause them to shift their thinking?

An important principle of scientific evaluation--the connectivity principle (Stanovich, 2001)--can be generalized to scientific thinking in the classroom. Suppose a teacher comes upon a new teaching method, curriculum component, or process. The method is advertised as totally new, which provides an explanation for the lack of direct empirical evidence for the method. A lack of direct empirical evidence should be grounds for suspicion, but should not immediately rule it out. The principle of connectivity means that the teacher now has another question to ask: "OK, there is no direct evidence for this method, but how is the theory behind it (the causal model of the effects it has) connected to the research consensus in the literature surrounding this curriculum area?" Even in the absence of direct empirical evidence on a particular method or technique, there could be a theoretical link to the consensus in the existing literature that would support the method.

For further tips on translating research into classroom practice, see Warby, Greene, Higgins, & Lovitt (1999). They present a format for selecting, reading, and evaluating research articles, and then importing the knowledge gained into the classroom.

Let's take an imaginary example from the domain of treatments for children with extreme reading difficulties. Imagine two treatments have been introduced to a teacher. No direct empirical tests of efficacy have been carried out using either treatment. The first, Treatment A, is a training program to facilitate the awareness of the segmental nature of language at the phonological level. The second, Treatment B, involves giving children training in vestibular sensitivity by having them walk on balance beams while blindfolded. Treatment A and B are equal in one respect--neither has had a direct empirical test of its efficacy, which reflects badly on both. Nevertheless, one of the treatments has the edge when it comes to the principle of connectivity. Treatment A makes contact with a broad consensus in the research literature that children with extraordinary reading difficulties are hampered because of insufficiently developed awareness of the segmental structure of language. Treatment B is not connected to any corresponding research literature consensus. Reason dictates that Treatment A is a better choice, even though neither has been directly tested.

Direct connections with research-based evidence and use of the connectivity principle when direct empirical evidence is absent give us necessary cross-checks on some of the pitfalls that arise when we rely solely on personal experience. Drawing upon personal experience is necessary and desirable in a veteran teacher, but it is not sufficient for making critical judgments about the effectiveness of an instructional strategy or curriculum. The insufficiency of personal experience becomes clear if we consider that the educational judgments--even of veteran teachers--often are in conflict. That is why we have to adjudicate conflicting knowledge claims using the scientific method.

Let us consider two further examples that demonstrate why we need controlled experimentation to verify even the most seemingly definitive personal observations. In the 1990s, considerable media and professional attention were directed at a method for aiding the communicative capacity of autistic individuals. This method is called facilitated communication. Autistic individuals who had previously been nonverbal were reported to have typed highly literate messages on a keyboard when their hands and arms were supported over the typewriter by a so-called facilitator. These startlingly verbal performances by autistic children who had previously shown very limited linguistic behavior raised incredible hopes among many parents of autistic children.

Unfortunately, claims for the efficacy of facilitated communication were disseminated by many media outlets before any controlled studies had been conducted. Since then, many studies have appeared in journals in speech science, linguistics, and psychology and each study has unequivocally demonstrated the same thing: the autistic child's performance is dependent upon tactile cueing from the facilitator. In the experiments, it was shown that when both child and facilitator were looking at the same drawing, the child typed the correct name of the drawing. When the viewing was occluded so that the child and the facilitator were shown different drawings, the child typed the name of the facilitator's drawing, not the one that the child herself was looking at (Beck & Pirovano, 1996; Burgess, Kirsch, Shane, Niederauer, Graham, & Bacon, 1998; Hudson, Melita, & Arnold, 1993; Jacobson, Mulick, & Schwartz, 1995; Wheeler, Jacobson, Paglieri, & Schwartz, 1993). The experimental studies directly contradicted the extensive case studies of the experiences of the facilitators of the children. These individuals invariably deny that they have inadvertently cued the children. Their personal experience, honest and heartfelt though it is, suggests the wrong model for explaining this outcome. The case study evidence told us something about the social connections between the children and their facilitators. But that is something different than what we got from the controlled experimental studies, which provided direct tests of the claim that the technique unlocks hidden linguistic skills in these children. Even if the claim had turned out to be true, the verification of the proof of its truth would not have come from the case studies or personal experiences, but from the necessary controlled studies.

Another example of the need for controlled experimentation to test the insights gleaned from personal experience is provided by the concept of learning styles--the idea that various modality preferences (or variants of this theme in terms of analytic/holistic processing or "learning styles") will interact with instructional methods, allowing teachers to individualize learning. The idea seems to "feel right" to many of us. It does seem to have some face validity, but it has never been demonstrated to work in practice. Its modern incarnation (see Gersten, 2001, Spear-Swerling & Sternberg, 2001) takes a particularly harmful form, one where students identified as auditory learners are matched with phonics instruction and visual and/or kinesthetic learners matched with holistic instruction. The newest form is particularly troublesome because the major syntheses of reading research demonstrate that many children can benefit from phonics-based instruction, not just "auditory" learners (National Reading Panel, 2000; Rayner et al., 2002; Stanovich, 2000). Excluding students identified as "visual/kinesthetic" learners from effective phonics instruction is a bad instructional practice--bad because it is not only not research based, it is actually contradicted by research.

A thorough review of the literature by Arter and Jenkins (1979) found no consistent evidence for the idea that modality strengths and weaknesses could be identified in a reliable and valid way that warranted differential instructional prescriptions. A review of the research evidence by Tarver and Dawson (1978) found likewise that the idea of modality preferences did not hold up to empirical scrutiny. They concluded, "This review found no evidence supporting an interaction between modality preference and method of teaching reading" (p. 17). Kampwirth and Bates (1980) confirmed the conclusions of the earlier reviews, although they stated their conclusions a little more baldly: "Given the rather general acceptance of this idea, and its common-sense appeal, one would presume that there exists a body of evidence to support it. UnfortunatelyÉno such firm evidence exists" (p. 598).

More recently, the idea of modality preferences (also referred to as learning styles, holistic versus analytic processing styles, and right versus left hemispheric processing) has again surfaced in the reading community. The focus of the recent implementations refers more to teaching to strengths, as opposed to remediating weaknesses (the latter being more the focus of the earlier efforts in the learning disabilities field). The research of the 1980s was summarized in an article by Steven Stahl (1988). His conclusions are largely negative because his review of the literature indicates that the methods that have been used in actual implementations of the learning styles idea have not been validated. Stahl concludes: "As intuitively appealing as this notion of matching instruction with learning style may be, past research has turned up little evidence supporting the claim that different teaching methods are more or less effective for children with different reading styles" (p. 317).

Obviously, such research reviews cannot prove that there is no possible implementation of the idea of learning styles that could work. However, the burden of proof in science rests on the investigator who is making a new claim about the nature of the world. It is not incumbent upon critics of a particular claim to show that it "couldn't be true." The question teachers might ask is, "Have the advocates for this new technique provided sufficient proof that it works?" Their burden of responsibility is to provide proof that their favored methods work. Teachers should not allow curricular advocates to avoid this responsibility by introducing confusion about where the burden of proof lies. For example, it is totally inappropriate and illogical to ask "Has anyone proved that it can't work?" One does not "prove a negative" in science. Instead, hypotheses are stated, and then must be tested by those asserting the hypotheses.

Reason-based practice in the classroom

Effective teachers engage in scientific thinking in their classrooms in a variety of ways: when they assess and evaluate student performance, develop Individual Education Plans (IEPs) for their students with disabilities, reflect on their practice, or engage in action research. For example, consider the assessment and evaluation activities in which teachers engage. The scientific mechanisms of systematic empiricism--iterative testing of hypotheses that are revised after the collection of data--can be seen when teachers plan for instruction: they evaluate their students' previous knowledge, develop hypotheses about the best methods for attaining lesson objectives, develop a teaching plan based on those hypotheses, observe the results, and base further instruction on the evidence collected.

This assessment cycle looks even more like the scientific method when teachers (as part of a multidisciplinary team) are developing and implementing an IEP for a student with a disability. The team must assess and evaluate the student's learning strengths and difficulties, develop hypotheses about the learning problems, select curriculum goals and objectives, base instruction on the hypotheses and the goals selected, teach, and evaluate the outcomes of that teaching. If the teaching is successful (goals and objectives are attained), the cycle continues with new goals. If the teaching has been unsuccessful (goals and objectives have not been achieved), the cycle begins again with new hypotheses. We can also see the principle of converging evidence here. No one piece of evidence might be decisive, but collectively the evidence might strongly point in one direction.

Scientific thinking in practice occurs when teachers engage in action research. Action research is research into one's own practice that has, as its main aim, the improvement of that practice. Stokes (1997) discusses how many advances in science came about as a result of "use-inspired research" which draws upon observations in applied settings. According to McNiff, Lomax, and Whitehead (1996), action research shares several characteristics with other types of research: "it leads to knowledge, it provides evidence to support this knowledge, it makes explicit the process of enquiry through which knowledge emerges, and it links new knowledge with existing knowledge" (p. 14). Notice the links to several important concepts: systematic empiricism, publicly verifiable knowledge, converging evidence, and the connectivity principle.

Teachers and Research Commonality in a "what works" epistemology

Many educational researchers have drawn attention to the epistemological commonalities between researchers and teachers (Gersten, Vaughn, Deshler, & Schiller, 1997; Stanovich, 1993/1994). A "what works" epistemology is a critical source of underlying unity in the world views of educators and researchers (Gersten & Dimino, 2001; Gersten, Chard, & Baker, 2000). Empiricism, broadly construed (as opposed to the caricature of white coats, numbers, and test tubes that is often used to discredit scientists) is about watching the world, manipulating it when possible, observing outcomes, and trying to associate outcomes with features observed and with manipulations. This is what the best teachers do. And this is true despite the grain of truth in the statement that "teaching is an art." As Berliner (1987) notes: "No one I know denies the artistic component to teaching. I now think, however, that such artistry should be research-based. I view medicine as an art, but I recognize that without its close ties to science it would be without success, status, or power in our society. Teaching, like medicine, is an art that also can be greatly enhanced by developing a close relationship to science (p. 4)."

In his review of the work of the Committee on the Prevention of Reading Difficulties for the National Research Council of the National Academy of Sciences (Snow, Burns, & Griffin, 1998), Pearson (1999) warned educators that resisting evaluation by hiding behind the "art of teaching" defense will eventually threaten teacher autonomy. Teachers need creativity, but they also need to demonstrate that they know what evidence is, and that they recognize that they practice in a profession based in behavioral science. While making it absolutely clear that he opposes legislative mandates, Pearson (1999) cautions:

We have a professional responsibility to forge best practice out of the raw materials provided by our most current and most valid readings of research...If professional groups wish to retain the privileges of teacher prerogative and choice that we value so dearly, then the price we must pay is constant attention to new knowledge as a vehicle for fine-tuning our individual and collective views of best practice. This is the path that other professions, such as medicine, have taken in order to maintain their professional prerogative, and we must take it, too. My fear is that if the professional groups in education fail to assume this responsibility squarely and openly, then we will find ourselves victims of the most onerous of legislative mandates (p. 245).

Those hostile to a research-based approach to educational practice like to imply that the insights of teachers and those of researchers conflict. Nothing could be farther from the truth. Take reading, for example. Teachers often do observe exactly what the research shows--that most of their children who are struggling with reading have trouble decoding words. In an address to the Reading Hall of Fame at the 1996 meeting of the International Reading Association, Isabel Beck (1996) illustrated this point by reviewing her own intellectual history (see Beck, 1998, for an archival version). She relates her surprise upon coming as an experienced teacher to the Learning Research and Development Center at the University of Pittsburgh and finding "that there were some people there (psychologists) who had not taught anyone to read, yet they were able to describe phenomena that I had observed in the course of teaching reading" (Beck, 1996, p. 5). In fact, what Beck was observing was the triangulation of two empirical approaches to the same issue--two perspectives on the same underlying reality. And she also came to appreciate how these two perspectives fit together: "What I knew were a number of whats--what some kids, and indeed adults, do in the early course of learning to read. And what the psychologists knew were some whys--why some novice readers might do what they do" (pp. 5-6).

Beck speculates on why the disputes about early reading instruction have dragged on so long without resolution and posits that it is due to the power of a particular kind of evidence--evidence from personal observation. The determination of whole language advocates is no doubt sustained because "people keep noticing the fact that some children or perhaps many children--in any event a subset of children--especially those who grow up in print-rich environments, don't seem to need much more of a boost in learning to read than to have their questions answered and to point things out to them in the course of dealing with books and various other authentic literacy acts" (Beck, 1996, p. 8). But Beck points out that it is equally true that proponents of the importance of decoding skills are also fueled by personal observation: "People keep noticing the fact that some children or perhaps many children--in any event a subset of children--don't seem to figure out the alphabetic principle, let alone some of the intricacies involved without having the system directly and systematically presented" (p. 8). But clearly we have lost sight of the basic fact that the two observations are not mutually exclusive--one doesn't negate the other. This is just the type of situation for which the scientific method was invented: a situation requiring a consensual view, triangulated across differing observations by different observers.

Teachers, like scientists, are ruthless pragmatists (Gersten & Dimino, 2001; Gersten, Chard, & Baker, 2000). They believe that some explanations and methods are better than others. They think there is a real world out there--a world in flux, obviously--but still one that is trackable by triangulating observations and observers. They believe that there are valid, if fallible, ways of finding out which educational practices are best. Teachers believe in a world that is predictable and controllable by manipulations that they use in their professional practice, just as scientists do. Researchers and educators are kindred spirits in their approach to knowledge, an important fact that can be used to forge a coalition to bring hard-won research knowledge to light in the classroom.

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Date Published: 2003 Date Posted: March 2010

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Position Statement

The Role of Research on Science Teaching and Learning

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Introduction

Research on science teaching and learning plays an important role in helping all students become proficient in science and making science education more equitable and inclusive, two goals called for in the   Framework for K–12 Science Education   (NRC 2012). NSTA promotes a research agenda that is focused on the goal of enhancing student learning through effective and equitable teaching practices that are based on current research. NSTA encourages ALL stakeholders in science education, including K–16 teachers of science and administrators, informal science educators, and school board members to recognize the importance of educational research, promote more research in schools, and participate in research when possible.

NSTA considers a broad range of activities to be within the scope of research, including research conducted by teachers that can lead to immediate classroom changes as well as research that contributes to a larger body of knowledge such as long-term or large-scale studies. Research on science teaching and learning involves identifying and asking appropriate questions, designing and conducting investigations, collecting evidence, drawing conclusions, and communicating and defending findings (NSTA 2004).

To produce research that has meaningful outcomes and the ability to improve the teaching and learning of science, NSTA advocates that research and practice be linked and support compatible goals. This synergistic relationship between research and practice includes teachers and researchers communicating goals, activities, and findings with the greater science education community in ways that make research accessible, understandable, meaningful, and relevant to teachers, administrators, and policy makers.

The process of research is the essence of the scientific enterprise and of scientific inquiry. Science education builds on the best of research in both worlds—science and education. By engaging in continual inquiry into teaching and learning, we can promote scientific literacy for students in the 21st century.

NSTA makes the following recommendations to promote effective research on science teaching and learning.

Declarations

Regarding the focus of research on science teaching and learning, NSTA recommends those conducting research

  • examine questions that are relevant to enhancing science teaching and learning for all learners;
  • focus on ways to make science education more equitable and inclusive;
  • address areas that have either been insufficiently investigated or not investigated at all and have the potential to improve what is known about science teaching and learning; and
  • extend theories of science teaching and learning in order to contribute to a coherent body of knowledge.

Regarding the practice of research on science teaching and learning, NSTA recommends those conducting research

  • draw and build upon previous research that may exist in the area of study;
  • focus on studies that build on promising areas of research and link to a larger body of work;
  • form collaborations and partnerships among those involved in science education (e.g., teachers, administrators, college faculty, informal science educators) as they examine science teaching and learning;
  • demonstrate, when possible, the degree to which student learning is affected;
  • engage in rigorous peer review that challenges the status quo and values varying perspectives on research pertaining to science teaching and learning;
  • view everyday experiences as opportunities to conduct research that yields findings to improve teaching practices and student learning;
  • support the participants in research with ample professional development to enhance their ability to design, conduct, interpret, and apply science education research; and
  • share research results with the wider science education community inside and outside the classroom.

Regarding the use of research on science teaching and learning, NSTA recommends

  • researchers communicate about research in ways that can be understood and embraced by science educators, administrators, policy makers, and others in the science education community;
  • researchers make research readily accessible by disseminating it to teachers and other decision makers using many forms of communication, including practitioner journals, professional conferences, websites, and social media;
  • researchers recognize and state the limitations of their research;
  • researchers and consumers of research discuss, critique, and apply findings;
  • school researchers have ample administrative support, time, and resources to conduct research in the classroom, share their findings with colleagues, and implement results to improve student learning; and
  • science educators embrace a culture of inquiry grounded in research that focuses on examining practice and improving student outcomes.

— Adopted by the NSTA Board of Directors, September 2010 Revised, October 2017

National Research Council (NRC). 2012.   A framework for K–12 science education: Practices, crosscutting concepts, and core ideas . Washington, DC: National Academies Press.

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Learning by doing helps students perform better in science

Students who physically experience scientific concepts understand them more deeply and score better on science tests, according to a new UChicago-led study.

Brain scans showed that students who took a hands-on approach to learning had activation in sensory and motor-related parts of the brain when they later thought about concepts such as angular momentum and torque. Activation of these brain areas was associated with better quiz performance by college physics students who participated in the research.

The study , published online April 24 in Psychological Science, comes from the Department of Psychology’s Human Performance Lab, directed by Prof. Sian Beilock , an internationally known expert on the mind–body connection and author of the book How the Body Knows Its Mind .

Beilock and her co-authors, Prof. Susan Fischer at DePaul University, UChicago graduate student Carly Kontra and postdoctoral scholar Dan Lyons, explain that hands-on experiences may benefit students more than previously realized, particularly in the world of virtual laboratories and online learning, This may be especially true for the initial stages of learning and in areas of science education that lend themselves to physical experiences.

“This gives new meaning to the idea of learning,” said Beilock. “When we’re thinking about math or physics, getting students to actually physically experience some of the concepts they’re learning about changes how they process the information, which could lead to better performance on a test.”

The study included experiments in the laboratory involving student behavior and brain imaging and one randomized trial in a college physics classroom. The hands-on studies used a system of two bicycle wheels that spun independently on a single axle, which allowed students to understand the concept of angular momentum—at work when a moving bicycle appears more stable than a stationary one. To experience angular momentum, students held the wheels by the axle and were instructed to tilt the axle from horizontal to vertical, while attempting to keep a laser pointer on a target line on the wall. When the axle tilted, the students experienced torque—the resistive force that causes objects to rotate.

The students were divided into groups, with some of the students tilting a set of bicycle wheel, while the other group simply observed. A post-test showed that those who had actively participated in the experiment outperformed the observation group.

The researchers used functional magnetic resonance imaging to see what regions of the brain were activated when students reasoned through the concepts of angular momentum and torque. While in the brain scanner, the students looked at animated pictures of an avatar spinning bicycle wheels—similar to the wheels they spun or watched other students spin. Later students took a quiz on the material.

“When students have a physical experience moving the wheels, they are more likely to activate sensory and motor areas of the brain when they are later thinking about the science concepts they learned about,” said Beilock. “These sensory and motor-related brain areas are known to be important for our ability to make sense of forces, angles and trajectories.

“Those students who physically experience difficult science concepts learn them better, perform better in class and on quizzes the next day, and the effect seems to play out weeks later, as well,” Beilock added.

A final experiment took place in a college-level physics class, to study whether the benefits of action experience could be seen on quizzes and homework taken days later. Students were randomly assigned to either the action or observation roles. Overall, the action group earned quiz grades that were about 7 percent higher than the observation group, even though they had fairly matched grades on other quizzes during the quarter.

For Beilock, the findings stressed the importance of classroom practices that physically engage students in the learning process, especially for math and science.

“In many situations, when we allow our bodies to become part of the learning process, we understand better,” Beilock said. “Reading about a concept in a textbook or even seeing a demonstration in class is not the same as physically experiencing what you are learning about. We need to rethink how we are teaching math and science because our actions matter for how and what we learn.”

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Study shows students in ‘active learning’ classrooms learn more than they think

For decades, there has been evidence that classroom techniques designed to get students to participate in the learning process produces better educational outcomes at virtually all levels.

And a new Harvard study suggests it may be important to let students know it.

The study , published Sept. 4 in the Proceedings of the National Academy of Sciences, shows that, though students felt as if they learned more through traditional lectures, they actually learned more when taking part in classrooms that employed so-called active-learning strategies.

Lead author Louis Deslauriers , the director of science teaching and learning and senior physics preceptor, knew that students would learn more from active learning. He published a key study in Science in 2011 that showed just that. But many students and faculty remained hesitant to switch to it.

“Often, students seemed genuinely to prefer smooth-as-silk traditional lectures,” Deslauriers said. “We wanted to take them at their word. Perhaps they actually felt like they learned more from lectures than they did from active learning.”

In addition to Deslauriers, the study is authored by director of sciences education and physics lecturer Logan McCarty , senior preceptor in applied physics Kelly Miller, preceptor in physics Greg Kestin , and Kristina Callaghan, now a physics lecturer at the University of California, Merced.

The question of whether students’ perceptions of their learning matches with how well they’re actually learning is particularly important, Deslauriers said, because while students eventually see the value of active learning, initially it can feel frustrating.

“Deep learning is hard work. The effort involved in active learning can be misinterpreted as a sign of poor learning,” he said. “On the other hand, a superstar lecturer can explain things in such a way as to make students feel like they are learning more than they actually are.”

To understand that dichotomy, Deslauriers and his co-authors designed an experiment that would expose students in an introductory physics class to both traditional lectures and active learning.

For the first 11 weeks of the 15-week class, students were taught using standard methods by an experienced instructor. In the 12th week, half the class was randomly assigned to a classroom that used active learning, while the other half attended highly polished lectures. In a subsequent class, the two groups were reversed. Notably, both groups used identical class content and only active engagement with the material was toggled on and off.

Following each class, students were surveyed on how much they agreed or disagreed with statements such as “I feel like I learned a lot from this lecture” and “I wish all my physics courses were taught this way.” Students were also tested on how much they learned in the class with 12 multiple-choice questions.

When the results were tallied, the authors found that students felt as if they learned more from the lectures, but in fact scored higher on tests following the active learning sessions. “Actual learning and feeling of learning were strongly anticorrelated,” Deslauriers said, “as shown through the robust statistical analysis by co-author Kelly Miller, who is an expert in educational statistics and active learning.”

Those results, the study authors are quick to point out, shouldn’t be interpreted as suggesting students dislike active learning. In fact, many studies have shown students quickly warm to the idea, once they begin to see the results. “In all the courses at Harvard that we’ve transformed to active learning,” Deslauriers said, “the overall course evaluations went up.”

bar chart

Co-author Kestin, who in addition to being a physicist is a video producer with PBS’ NOVA, said, “It can be tempting to engage the class simply by folding lectures into a compelling ‘story,’ especially when that’s what students seem to like. I show my students the data from this study on the first day of class to help them appreciate the importance of their own involvement in active learning.”

McCarty, who oversees curricular efforts across the sciences, hopes this study will encourage more of his colleagues to embrace active learning.

“We want to make sure that other instructors are thinking hard about the way they’re teaching,” he said. “In our classes, we start each topic by asking students to gather in small groups to solve some problems. While they work, we walk around the room to observe them and answer questions. Then we come together and give a short lecture targeted specifically at the misconceptions and struggles we saw during the problem-solving activity. So far we’ve transformed over a dozen classes to use this kind of active-learning approach. It’s extremely efficient — we can cover just as much material as we would using lectures.”

A pioneer in work on active learning, Balkanski Professor of Physics and Applied Physics Eric Mazur hailed the study as debunking long-held beliefs about how students learn.

“This work unambiguously debunks the illusion of learning from lectures,” he said. “It also explains why instructors and students cling to the belief that listening to lectures constitutes learning. I recommend every lecturer reads this article.”

Dean of Science Christopher Stubbs , Samuel C. Moncher Professor of Physics and of Astronomy, was an early convert. “When I first switched to teaching using active learning, some students resisted that change. This research confirms that faculty should persist and encourage active learning. Active engagement in every classroom, led by our incredible science faculty, should be the hallmark of residential undergraduate education at Harvard.”

Ultimately, Deslauriers said, the study shows that it’s important to ensure that neither instructors nor students are fooled into thinking that lectures are the best learning option. “Students might give fabulous evaluations to an amazing lecturer based on this feeling of learning, even though their actual learning isn’t optimal,” he said. “This could help to explain why study after study shows that student evaluations seem to be completely uncorrelated with actual learning.”

This research was supported with funding from the Harvard FAS Division of Science.

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Teaching the science of learning

Yana weinstein.

1 Department of Psychology, University of Massachusetts Lowell, Lowell, MA USA

Christopher R. Madan

2 Department of Psychology, Boston College, Chestnut Hill, MA USA

3 School of Psychology, University of Nottingham, Nottingham, UK

Megan A. Sumeracki

4 Department of Psychology, Rhode Island College, Providence, RI USA

Associated Data

Not applicable.

The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.

Significance

Education does not currently adhere to the medical model of evidence-based practice (Roediger, 2013 ). However, over the past few decades, our field has made significant advances in applying cognitive processes to education. From this work, specific recommendations can be made for students to maximize their learning efficiency (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013 ; Roediger, Finn, & Weinstein, 2012 ). In particular, a review published 10 years ago identified a limited number of study techniques that have received solid evidence from multiple replications testing their effectiveness in and out of the classroom (Pashler et al., 2007 ). A recent textbook analysis (Pomerance, Greenberg, & Walsh, 2016 ) took the six key learning strategies from this report by Pashler and colleagues, and found that very few teacher-training textbooks cover any of these six principles – and none cover them all, suggesting that these strategies are not systematically making their way into the classroom. This is the case in spite of multiple recent academic (e.g., Dunlosky et al., 2013 ) and general audience (e.g., Dunlosky, 2013 ) publications about these strategies. In this tutorial review, we present the basic science behind each of these six key principles, along with more recent research on their effectiveness in live classrooms, and suggest ideas for pedagogical implementation. The target audience of this review is (a) educators who might be interested in integrating the strategies into their teaching practice, (b) science of learning researchers who are looking for open questions to help determine future research priorities, and (c) researchers in other subfields who are interested in the ways that principles from cognitive psychology have been applied to education.

While the typical teacher may not be exposed to this research during teacher training, a small cohort of teachers intensely interested in cognitive psychology has recently emerged. These teachers are mainly based in the UK, and, anecdotally (e.g., Dennis (2016), personal communication), appear to have taken an interest in the science of learning after reading Make it Stick (Brown, Roediger, & McDaniel, 2014 ; see Clark ( 2016 ) for an enthusiastic review of this book on a teacher’s blog, and “Learning Scientists” ( 2016c ) for a collection). In addition, a grassroots teacher movement has led to the creation of “researchED” – a series of conferences on evidence-based education (researchED, 2013 ). The teachers who form part of this network frequently discuss cognitive psychology techniques and their applications to education on social media (mainly Twitter; e.g., Fordham, 2016 ; Penfound, 2016 ) and on their blogs, such as Evidence Into Practice ( https://evidenceintopractice.wordpress.com/ ), My Learning Journey ( http://reflectionsofmyteaching.blogspot.com/ ), and The Effortful Educator ( https://theeffortfuleducator.com/ ). In general, the teachers who write about these issues pay careful attention to the relevant literature, often citing some of the work described in this review.

These informal writings, while allowing teachers to explore their approach to teaching practice (Luehmann, 2008 ), give us a unique window into the application of the science of learning to the classroom. By examining these blogs, we can not only observe how basic cognitive research is being applied in the classroom by teachers who are reading it, but also how it is being misapplied, and what questions teachers may be posing that have gone unaddressed in the scientific literature. Throughout this review, we illustrate each strategy with examples of how it can be implemented (see Table  1 and Figs.  1 , ​ ,2, 2 , ​ ,3, 3 , ​ ,4, 4 , ​ ,5, 5 , ​ ,6 6 and ​ and7), 7 ), as well as with relevant teacher blog posts that reflect on its application, and draw upon this work to pin-point fruitful avenues for further basic and applied research.

Six strategies for effective learning, each illustrated with an implementation example from the biological bases of behavior

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Spaced practice schedule for one week. This schedule is designed to represent a typical timetable of a high-school student. The schedule includes four one-hour study sessions, one longer study session on the weekend, and one rest day. Notice that each subject is studied one day after it is covered in school, to create spacing between classes and study sessions. Copyright note: this image was produced by the authors

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a Blocked practice and interleaved practice with fraction problems. In the blocked version, students answer four multiplication problems consecutively. In the interleaved version, students answer a multiplication problem followed by a division problem and then an addition problem, before returning to multiplication. For an experiment with a similar setup, see Patel et al. ( 2016 ). Copyright note: this image was produced by the authors. b Illustration of interleaving and spacing. Each color represents a different homework topic. Interleaving involves alternating between topics, rather than blocking. Spacing involves distributing practice over time, rather than massing. Interleaving inherently involves spacing as other tasks naturally “fill” the spaces between interleaved sessions. Copyright note: this image was produced by the authors, adapted from Rohrer ( 2012 )

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Concept map illustrating the process and resulting benefits of retrieval practice. Retrieval practice involves the process of withdrawing learned information from long-term memory into working memory, which requires effort. This produces direct benefits via the consolidation of learned information, making it easier to remember later and causing improvements in memory, transfer, and inferences. Retrieval practice also produces indirect benefits of feedback to students and teachers, which in turn can lead to more effective study and teaching practices, with a focus on information that was not accurately retrieved. Copyright note: this figure originally appeared in a blog post by the first and third authors ( http://www.learningscientists.org/blog/2016/4/1-1 )

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Illustration of “how” and “why” questions (i.e., elaborative interrogation questions) students might ask while studying the physics of flight. To help figure out how physics explains flight, students might ask themselves the following questions: “How does a plane take off?”; “Why does a plane need an engine?”; “How does the upward force (lift) work?”; “Why do the wings have a curved upper surface and a flat lower surface?”; and “Why is there a downwash behind the wings?”. Copyright note: the image of the plane was downloaded from Pixabay.com and is free to use, modify, and share

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Three examples of physics problems that would be categorized differently by novices and experts. The problems in ( a ) and ( c ) look similar on the surface, so novices would group them together into one category. Experts, however, will recognize that the problems in ( b ) and ( c ) both relate to the principle of energy conservation, and so will group those two problems into one category instead. Copyright note: the figure was produced by the authors, based on figures in Chi et al. ( 1981 )

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Example of how to enhance learning through use of a visual example. Students might view this visual representation of neural communications with the words provided, or they could draw a similar visual representation themselves. Copyright note: this figure was produced by the authors

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Example of word properties associated with visual, verbal, and motor coding for the word “SPOON”. A word can evoke multiple types of representation (“codes” in dual coding theory). Viewing a word will automatically evoke verbal representations related to its component letters and phonemes. Words representing objects (i.e., concrete nouns) will also evoke visual representations, including information about similar objects, component parts of the object, and information about where the object is typically found. In some cases, additional codes can also be evoked, such as motor-related properties of the represented object, where contextual information related to the object’s functional intention and manipulation action may also be processed automatically when reading the word. Copyright note: this figure was produced by the authors and is based on Aylwin ( 1990 ; Fig.  2 ) and Madan and Singhal ( 2012a , Fig.  3 )

Spaced practice

The benefits of spaced (or distributed) practice to learning are arguably one of the strongest contributions that cognitive psychology has made to education (Kang, 2016 ). The effect is simple: the same amount of repeated studying of the same information spaced out over time will lead to greater retention of that information in the long run, compared with repeated studying of the same information for the same amount of time in one study session. The benefits of distributed practice were first empirically demonstrated in the 19 th century. As part of his extensive investigation into his own memory, Ebbinghaus ( 1885/1913 ) found that when he spaced out repetitions across 3 days, he could almost halve the number of repetitions necessary to relearn a series of 12 syllables in one day (Chapter 8). He thus concluded that “a suitable distribution of [repetitions] over a space of time is decidedly more advantageous than the massing of them at a single time” (Section 34). For those who want to read more about Ebbinghaus’s contribution to memory research, Roediger ( 1985 ) provides an excellent summary.

Since then, hundreds of studies have examined spacing effects both in the laboratory and in the classroom (Kang, 2016 ). Spaced practice appears to be particularly useful at large retention intervals: in the meta-analysis by Cepeda, Pashler, Vul, Wixted, and Rohrer ( 2006 ), all studies with a retention interval longer than a month showed a clear benefit of distributed practice. The “new theory of disuse” (Bjork & Bjork, 1992 ) provides a helpful mechanistic explanation for the benefits of spacing to learning. This theory posits that memories have both retrieval strength and storage strength. Whereas retrieval strength is thought to measure the ease with which a memory can be recalled at a given moment, storage strength (which cannot be measured directly) represents the extent to which a memory is truly embedded in the mind. When studying is taking place, both retrieval strength and storage strength receive a boost. However, the extent to which storage strength is boosted depends upon retrieval strength, and the relationship is negative: the greater the current retrieval strength, the smaller the gains in storage strength. Thus, the information learned through “cramming” will be rapidly forgotten due to high retrieval strength and low storage strength (Bjork & Bjork, 2011 ), whereas spacing out learning increases storage strength by allowing retrieval strength to wane before restudy.

Teachers can introduce spacing to their students in two broad ways. One involves creating opportunities to revisit information throughout the semester, or even in future semesters. This does involve some up-front planning, and can be difficult to achieve, given time constraints and the need to cover a set curriculum. However, spacing can be achieved with no great costs if teachers set aside a few minutes per class to review information from previous lessons. The second method involves putting the onus to space on the students themselves. Of course, this would work best with older students – high school and above. Because spacing requires advance planning, it is crucial that the teacher helps students plan their studying. For example, teachers could suggest that students schedule study sessions on days that alternate with the days on which a particular class meets (e.g., schedule review sessions for Tuesday and Thursday when the class meets Monday and Wednesday; see Fig.  1 for a more complete weekly spaced practice schedule). It important to note that the spacing effect refers to information that is repeated multiple times, rather than the idea of studying different material in one long session versus spaced out in small study sessions over time. However, for teachers and particularly for students planning a study schedule, the subtle difference between the two situations (spacing out restudy opportunities, versus spacing out studying of different information over time) may be lost. Future research should address the effects of spacing out studying of different information over time, whether the same considerations apply in this situation as compared to spacing out restudy opportunities, and how important it is for teachers and students to understand the difference between these two types of spaced practice.

It is important to note that students may feel less confident when they space their learning (Bjork, 1999 ) than when they cram. This is because spaced learning is harder – but it is this “desirable difficulty” that helps learning in the long term (Bjork, 1994 ). Students tend to cram for exams rather than space out their learning. One explanation for this is that cramming does “work”, if the goal is only to pass an exam. In order to change students’ minds about how they schedule their studying, it might be important to emphasize the value of retaining information beyond a final exam in one course.

Ideas for how to apply spaced practice in teaching have appeared in numerous teacher blogs (e.g., Fawcett, 2013 ; Kraft, 2015 ; Picciotto, 2009 ). In England in particular, as of 2013, high-school students need to be able to remember content from up to 3 years back on cumulative exams (General Certificate of Secondary Education (GCSE) and A-level exams; see CIFE, 2012 ). A-levels in particular determine what subject students study in university and which programs they are accepted into, and thus shape the path of their academic career. A common approach for dealing with these exams has been to include a “revision” (i.e., studying or cramming) period of a few weeks leading up to the high-stakes cumulative exams. Now, teachers who follow cognitive psychology are advocating a shift of priorities to spacing learning over time across the 3 years, rather than teaching a topic once and then intensely reviewing it weeks before the exam (Cox, 2016a ; Wood, 2017 ). For example, some teachers have suggested using homework assignments as an opportunity for spaced practice by giving students homework on previous topics (Rose, 2014 ). However, questions remain, such as whether spaced practice can ever be effective enough to completely alleviate the need or utility of a cramming period (Cox, 2016b ), and how one can possibly figure out the optimal lag for spacing (Benney, 2016 ; Firth, 2016 ).

There has been considerable research on the question of optimal lag, and much of it is quite complex; two sessions neither too close together (i.e., cramming) nor too far apart are ideal for retention. In a large-scale study, Cepeda, Vul, Rohrer, Wixted, and Pashler ( 2008 ) examined the effects of the gap between study sessions and the interval between study and test across long periods, and found that the optimal gap between study sessions was contingent on the retention interval. Thus, it is not clear how teachers can apply the complex findings on lag to their own classrooms.

A useful avenue of research would be to simplify the research paradigms that are used to study optimal lag, with the goal of creating a flexible, spaced-practice framework that teachers could apply and tailor to their own teaching needs. For example, an Excel macro spreadsheet was recently produced to help teachers plan for lagged lessons (Weinstein-Jones & Weinstein, 2017 ; see Weinstein & Weinstein-Jones ( 2017 ) for a description of the algorithm used in the spreadsheet), and has been used by teachers to plan their lessons (Penfound, 2017 ). However, one teacher who found this tool helpful also wondered whether the more sophisticated plan was any better than his own method of manually selecting poorly understood material from previous classes for later review (Lovell, 2017 ). This direction is being actively explored within personalized online learning environments (Kornell & Finn, 2016 ; Lindsey, Shroyer, Pashler, & Mozer, 2014 ), but teachers in physical classrooms might need less technologically-driven solutions to teach cohorts of students.

It seems teachers would greatly appreciate a set of guidelines for how to implement spacing in the curriculum in the most effective, but also the most efficient manner. While the cognitive field has made great advances in terms of understanding the mechanisms behind spacing, what teachers need more of are concrete evidence-based tools and guidelines for direct implementation in the classroom. These could include more sophisticated and experimentally tested versions of the software described above (Weinstein-Jones & Weinstein, 2017 ), or adaptable templates of spaced curricula. Moreover, researchers need to evaluate the effectiveness of these tools in a real classroom environment, over a semester or academic year, in order to give pedagogically relevant evidence-based recommendations to teachers.

Interleaving

Another scheduling technique that has been shown to increase learning is interleaving. Interleaving occurs when different ideas or problem types are tackled in a sequence, as opposed to the more common method of attempting multiple versions of the same problem in a given study session (known as blocking). Interleaving as a principle can be applied in many different ways. One such way involves interleaving different types of problems during learning, which is particularly applicable to subjects such as math and physics (see Fig.  2 a for an example with fractions, based on a study by Patel, Liu, & Koedinger, 2016 ). For example, in a study with college students, Rohrer and Taylor ( 2007 ) found that shuffling math problems that involved calculating the volume of different shapes resulted in better test performance 1 week later than when students answered multiple problems about the same type of shape in a row. This pattern of results has also been replicated with younger students, for example 7 th grade students learning to solve graph and slope problems (Rohrer, Dedrick, & Stershic, 2015 ). The proposed explanation for the benefit of interleaving is that switching between different problem types allows students to acquire the ability to choose the right method for solving different types of problems rather than learning only the method itself, and not when to apply it.

Do the benefits of interleaving extend beyond problem solving? The answer appears to be yes. Interleaving can be helpful in other situations that require discrimination, such as inductive learning. Kornell and Bjork ( 2008 ) examined the effects of interleaving in a task that might be pertinent to a student of the history of art: the ability to match paintings to their respective painters. Students who studied different painters’ paintings interleaved at study were more successful on a later identification test than were participants who studied the paintings blocked by painter. Birnbaum, Kornell, Bjork, and Bjork ( 2013 ) proposed the discriminative-contrast hypothesis to explain that interleaving enhances learning by allowing the comparison between exemplars of different categories. They found support for this hypothesis in a set of experiments with bird categorization: participants benefited from interleaving and also from spacing, but not when the spacing interrupted side-by-side comparisons of birds from different categories.

Another type of interleaving involves the interleaving of study and test opportunities. This type of interleaving has been applied, once again, to problem solving, whereby students alternate between attempting a problem and viewing a worked example (Trafton & Reiser, 1993 ); this pattern appears to be superior to answering a string of problems in a row, at least with respect to the amount of time it takes to achieve mastery of a procedure (Corbett, Reed, Hoffmann, MacLaren, & Wagner, 2010 ). The benefits of interleaving study and test opportunities – rather than blocking study followed by attempting to answer problems or questions – might arise due to a process known as “test-potentiated learning”. That is, a study opportunity that immediately follows a retrieval attempt may be more fruitful than when that same studying was not preceded by retrieval (Arnold & McDermott, 2013 ).

For problem-based subjects, the interleaving technique is straightforward: simply mix questions on homework and quizzes with previous materials (which takes care of spacing as well); for languages, mix vocabulary themes rather than blocking by theme (Thomson & Mehring, 2016 ). But interleaving as an educational strategy ought to be presented to teachers with some caveats. Research has focused on interleaving material that is somewhat related (e.g., solving different mathematical equations, Rohrer et al., 2015 ), whereas students sometimes ask whether they should interleave material from different subjects – a practice that has not received empirical support (Hausman & Kornell, 2014 ). When advising students how to study independently, teachers should thus proceed with caution. Since it is easy for younger students to confuse this type of unhelpful interleaving with the more helpful interleaving of related information, it may be best for teachers of younger grades to create opportunities for interleaving in homework and quiz assignments rather than putting the onus on the students themselves to make use of the technique. Technology can be very helpful here, with apps such as Quizlet, Memrise, Anki, Synap, Quiz Champ, and many others (see also “Learning Scientists”, 2017 ) that not only allow instructor-created quizzes to be taken by students, but also provide built-in interleaving algorithms so that the burden does not fall on the teacher or the student to carefully plan which items are interleaved when.

An important point to consider is that in educational practice, the distinction between spacing and interleaving can be difficult to delineate. The gap between the scientific and classroom definitions of interleaving is demonstrated by teachers’ own writings about this technique. When they write about interleaving, teachers often extend the term to connote a curriculum that involves returning to topics multiple times throughout the year (e.g., Kirby, 2014 ; see “Learning Scientists” ( 2016a ) for a collection of similar blog posts by several other teachers). The “interleaving” of topics throughout the curriculum produces an effect that is more akin to what cognitive psychologists call “spacing” (see Fig.  2 b for a visual representation of the difference between interleaving and spacing). However, cognitive psychologists have not examined the effects of structuring the curriculum in this way, and open questions remain: does repeatedly circling back to previous topics throughout the semester interrupt the learning of new information? What are some effective techniques for interleaving old and new information within one class? And how does one determine the balance between old and new information?

Retrieval practice

While tests are most often used in educational settings for assessment, a lesser-known benefit of tests is that they actually improve memory of the tested information. If we think of our memories as libraries of information, then it may seem surprising that retrieval (which happens when we take a test) improves memory; however, we know from a century of research that retrieving knowledge actually strengthens it (see Karpicke, Lehman, & Aue, 2014 ). Testing was shown to strengthen memory as early as 100 years ago (Gates, 1917 ), and there has been a surge of research in the last decade on the mnemonic benefits of testing, or retrieval practice . Most of the research on the effectiveness of retrieval practice has been done with college students (see Roediger & Karpicke, 2006 ; Roediger, Putnam, & Smith, 2011 ), but retrieval-based learning has been shown to be effective at producing learning for a wide range of ages, including preschoolers (Fritz, Morris, Nolan, & Singleton, 2007 ), elementary-aged children (e.g., Karpicke, Blunt, & Smith, 2016 ; Karpicke, Blunt, Smith, & Karpicke, 2014 ; Lipko-Speed, Dunlosky, & Rawson, 2014 ; Marsh, Fazio, & Goswick, 2012 ; Ritchie, Della Sala, & McIntosh, 2013 ), middle-school students (e.g., McDaniel, Thomas, Agarwal, McDermott, & Roediger, 2013 ; McDermott, Agarwal, D’Antonio, Roediger, & McDaniel, 2014 ), and high-school students (e.g., McDermott et al., 2014 ). In addition, the effectiveness of retrieval-based learning has been extended beyond simple testing to other activities in which retrieval practice can be integrated, such as concept mapping (Blunt & Karpicke, 2014 ; Karpicke, Blunt, et al., 2014 ; Ritchie et al., 2013 ).

A debate is currently ongoing as to the effectiveness of retrieval practice for more complex materials (Karpicke & Aue, 2015 ; Roelle & Berthold, 2017 ; Van Gog & Sweller, 2015 ). Practicing retrieval has been shown to improve the application of knowledge to new situations (e.g., Butler, 2010 ; Dirkx, Kester, & Kirschner, 2014 ); McDaniel et al., 2013 ; Smith, Blunt, Whiffen, & Karpicke, 2016 ); but see Tran, Rohrer, and Pashler ( 2015 ) and Wooldridge, Bugg, McDaniel, and Liu ( 2014 ), for retrieval practice studies that showed limited or no increased transfer compared to restudy. Retrieval practice effects on higher-order learning may be more sensitive than fact learning to encoding factors, such as the way material is presented during study (Eglington & Kang, 2016 ). In addition, retrieval practice may be more beneficial for higher-order learning if it includes more scaffolding (Fiechter & Benjamin, 2017 ; but see Smith, Blunt, et al., 2016 ) and targeted practice with application questions (Son & Rivas, 2016 ).

How does retrieval practice help memory? Figure  3 illustrates both the direct and indirect benefits of retrieval practice identified by the literature. The act of retrieval itself is thought to strengthen memory (Karpicke, Blunt, et al., 2014 ; Roediger & Karpicke, 2006 ; Smith, Roediger, & Karpicke, 2013 ). For example, Smith et al. ( 2013 ) showed that if students brought information to mind without actually producing it (covert retrieval), they remembered the information just as well as if they overtly produced the retrieved information (overt retrieval). Importantly, both overt and covert retrieval practice improved memory over control groups without retrieval practice, even when feedback was not provided. The fact that bringing information to mind in the absence of feedback or restudy opportunities improves memory leads researchers to conclude that it is the act of retrieval – thinking back to bring information to mind – that improves memory of that information.

The benefit of retrieval practice depends to a certain extent on successful retrieval (see Karpicke, Lehman, et al., 2014 ). For example, in Experiment 4 of Smith et al. ( 2013 ), students successfully retrieved 72% of the information during retrieval practice. Of course, retrieving 72% of the information was compared to a restudy control group, during which students were re-exposed to 100% of the information, creating a bias in favor of the restudy condition. Yet retrieval led to superior memory later compared to the restudy control. However, if retrieval success is extremely low, then it is unlikely to improve memory (e.g., Karpicke, Blunt, et al., 2014 ), particularly in the absence of feedback. On the other hand, if retrieval-based learning situations are constructed in such a way that ensures high levels of success, the act of bringing the information to mind may be undermined, thus making it less beneficial. For example, if a student reads a sentence and then immediately covers the sentence and recites it out loud, they are likely not retrieving the information but rather just keeping the information in their working memory long enough to recite it again (see Smith, Blunt, et al., 2016 for a discussion of this point). Thus, it is important to balance success of retrieval with overall difficulty in retrieving the information (Smith & Karpicke, 2014 ; Weinstein, Nunes, & Karpicke, 2016 ). If initial retrieval success is low, then feedback can help improve the overall benefit of practicing retrieval (Kang, McDermott, & Roediger, 2007 ; Smith & Karpicke, 2014 ). Kornell, Klein, and Rawson ( 2015 ), however, found that it was the retrieval attempt and not the correct production of information that produced the retrieval practice benefit – as long as the correct answer was provided after an unsuccessful attempt, the benefit was the same as for a successful retrieval attempt in this set of studies. From a practical perspective, it would be helpful for teachers to know when retrieval attempts in the absence of success are helpful, and when they are not. There may also be additional reasons beyond retrieval benefits that would push teachers towards retrieval practice activities that produce some success amongst students; for example, teachers may hesitate to give students retrieval practice exercises that are too difficult, as this may negatively affect self-efficacy and confidence.

In addition to the fact that bringing information to mind directly improves memory for that information, engaging in retrieval practice can produce indirect benefits as well (see Roediger et al., 2011 ). For example, research by Weinstein, Gilmore, Szpunar, and McDermott ( 2014 ) demonstrated that when students expected to be tested, the increased test expectancy led to better-quality encoding of new information. Frequent testing can also serve to decrease mind-wandering – that is, thoughts that are unrelated to the material that students are supposed to be studying (Szpunar, Khan, & Schacter, 2013 ).

Practicing retrieval is a powerful way to improve meaningful learning of information, and it is relatively easy to implement in the classroom. For example, requiring students to practice retrieval can be as simple as asking students to put their class materials away and try to write out everything they know about a topic. Retrieval-based learning strategies are also flexible. Instructors can give students practice tests (e.g., short-answer or multiple-choice, see Smith & Karpicke, 2014 ), provide open-ended prompts for the students to recall information (e.g., Smith, Blunt, et al., 2016 ) or ask their students to create concept maps from memory (e.g., Blunt & Karpicke, 2014 ). In one study, Weinstein et al. ( 2016 ) looked at the effectiveness of inserting simple short-answer questions into online learning modules to see whether they improved student performance. Weinstein and colleagues also manipulated the placement of the questions. For some students, the questions were interspersed throughout the module, and for other students the questions were all presented at the end of the module. Initial success on the short-answer questions was higher when the questions were interspersed throughout the module. However, on a later test of learning from that module, the original placement of the questions in the module did not matter for performance. As with spaced practice, where the optimal gap between study sessions is contingent on the retention interval, the optimum difficulty and level of success during retrieval practice may also depend on the retention interval. Both groups of students who answered questions performed better on the delayed test compared to a control group without question opportunities during the module. Thus, the important thing is for instructors to provide opportunities for retrieval practice during learning. Based on previous research, any activity that promotes the successful retrieval of information should improve learning.

Retrieval practice has received a lot of attention in teacher blogs (see “Learning Scientists” ( 2016b ) for a collection). A common theme seems to be an emphasis on low-stakes (Young, 2016 ) and even no-stakes (Cox, 2015 ) testing, the goal of which is to increase learning rather than assess performance. In fact, one well-known charter school in the UK has an official homework policy grounded in retrieval practice: students are to test themselves on subject knowledge for 30 minutes every day in lieu of standard homework (Michaela Community School, 2014 ). The utility of homework, particularly for younger children, is often a hotly debated topic outside of academia (e.g., Shumaker, 2016 ; but see Jones ( 2016 ) for an opposing viewpoint and Cooper ( 1989 ) for the original research the blog posts were based on). Whereas some research shows clear links between homework and academic achievement (Valle et al., 2016 ), other researchers have questioned the effectiveness of homework (Dettmers, Trautwein, & Lüdtke, 2009 ). Perhaps amending homework to involve retrieval practice might make it more effective; this remains an open empirical question.

One final consideration is that of test anxiety. While retrieval practice can be very powerful at improving memory, some research shows that pressure during retrieval can undermine some of the learning benefit. For example, Hinze and Rapp ( 2014 ) manipulated pressure during quizzing to create high-pressure and low-pressure conditions. On the quizzes themselves, students performed equally well. However, those in the high-pressure condition did not perform as well on a criterion test later compared to the low-pressure group. Thus, test anxiety may reduce the learning benefit of retrieval practice. Eliminating all high-pressure tests is probably not possible, but instructors can provide a number of low-stakes retrieval opportunities for students to help increase learning. The use of low-stakes testing can serve to decrease test anxiety (Khanna, 2015 ), and has recently been shown to negate the detrimental impact of stress on learning (Smith, Floerke, & Thomas, 2016 ). This is a particularly important line of inquiry to pursue for future research, because many teachers who are not familiar with the effectiveness of retrieval practice may be put off by the implied pressure of “testing”, which evokes the much maligned high-stakes standardized tests (e.g., McHugh, 2013 ).

Elaboration

Elaboration involves connecting new information to pre-existing knowledge. Anderson ( 1983 , p.285) made the following claim about elaboration: “One of the most potent manipulations that can be performed in terms of increasing a subject’s memory for material is to have the subject elaborate on the to-be-remembered material.” Postman ( 1976 , p. 28) defined elaboration most parsimoniously as “additions to nominal input”, and Hirshman ( 2001 , p. 4369) provided an elaboration on this definition (pun intended!), defining elaboration as “A conscious, intentional process that associates to-be-remembered information with other information in memory.” However, in practice, elaboration could mean many different things. The common thread in all the definitions is that elaboration involves adding features to an existing memory.

One possible instantiation of elaboration is thinking about information on a deeper level. The levels (or “depth”) of processing framework, proposed by Craik and Lockhart ( 1972 ), predicts that information will be remembered better if it is processed more deeply in terms of meaning, rather than shallowly in terms of form. The leves of processing framework has, however, received a number of criticisms (Craik, 2002 ). One major problem with this framework is that it is difficult to measure “depth”. And if we are not able to actually measure depth, then the argument can become circular: is it that something was remembered better because it was studied more deeply, or do we conclude that it must have been studied more deeply because it is remembered better? (See Lockhart & Craik, 1990 , for further discussion of this issue).

Another mechanism by which elaboration can confer a benefit to learning is via improvement in organization (Bellezza, Cheesman, & Reddy, 1977 ; Mandler, 1979 ). By this view, elaboration involves making information more integrated and organized with existing knowledge structures. By connecting and integrating the to-be-learned information with other concepts in memory, students can increase the extent to which the ideas are organized in their minds, and this increased organization presumably facilitates the reconstruction of the past at the time of retrieval.

Elaboration is such a broad term and can include so many different techniques that it is hard to claim that elaboration will always help learning. There is, however, a specific technique under the umbrella of elaboration for which there is relatively strong evidence in terms of effectiveness (Dunlosky et al., 2013 ; Pashler et al., 2007 ). This technique is called elaborative interrogation, and involves students questioning the materials that they are studying (Pressley, McDaniel, Turnure, Wood, & Ahmad, 1987 ). More specifically, students using this technique would ask “how” and “why” questions about the concepts they are studying (see Fig.  4 for an example on the physics of flight). Then, crucially, students would try to answer these questions – either from their materials or, eventually, from memory (McDaniel & Donnelly, 1996 ). The process of figuring out the answer to the questions – with some amount of uncertainty (Overoye & Storm, 2015 ) – can help learning. When using this technique, however, it is important that students check their answers with their materials or with the teacher; when the content generated through elaborative interrogation is poor, it can actually hurt learning (Clinton, Alibali, & Nathan, 2016 ).

Students can also be encouraged to self-explain concepts to themselves while learning (Chi, De Leeuw, Chiu, & LaVancher, 1994 ). This might involve students simply saying out loud what steps they need to perform to solve an equation. Aleven and Koedinger ( 2002 ) conducted two classroom studies in which students were either prompted by a “cognitive tutor” to provide self-explanations during a problem-solving task or not, and found that the self-explanations led to improved performance. According to the authors, this approach could scale well to real classrooms. If possible and relevant, students could even perform actions alongside their self-explanations (Cohen, 1981 ; see also the enactment effect, Hainselin, Picard, Manolli, Vankerkore-Candas, & Bourdin, 2017 ). Instructors can scaffold students in these types of activities by providing self-explanation prompts throughout to-be-learned material (O’Neil et al., 2014 ). Ultimately, the greatest potential benefit of accurate self-explanation or elaboration is that the student will be able to transfer their knowledge to a new situation (Rittle-Johnson, 2006 ).

The technical term “elaborative interrogation” has not made it into the vernacular of educational bloggers (a search on https://educationechochamberuncut.wordpress.com , which consolidates over 3,000 UK-based teacher blogs, yielded zero results for that term). However, a few teachers have blogged about elaboration more generally (e.g., Hobbiss, 2016 ) and deep questioning specifically (e.g., Class Teaching, 2013 ), just without using the specific terminology. This strategy in particular may benefit from a more open dialog between researchers and teachers to facilitate the use of elaborative interrogation in the classroom and to address possible barriers to implementation. In terms of advancing the scientific understanding of elaborative interrogation in a classroom setting, it would be informative to conduct a larger-scale intervention to see whether having students elaborate during reading actually helps their understanding. It would also be useful to know whether the students really need to generate their own elaborative interrogation (“how” and “why”) questions, versus answering questions provided by others. How long should students persist to find the answers? When is the right time to have students engage in this task, given the levels of expertise required to do it well (Clinton et al., 2016 )? Without knowing the answers to these questions, it may be too early for us to instruct teachers to use this technique in their classes. Finally, elaborative interrogation takes a long time. Is this time efficiently spent? Or, would it be better to have the students try to answer a few questions, pool their information as a class, and then move to practicing retrieval of the information?

Concrete examples

Providing supporting information can improve the learning of key ideas and concepts. Specifically, using concrete examples to supplement content that is more conceptual in nature can make the ideas easier to understand and remember. Concrete examples can provide several advantages to the learning process: (a) they can concisely convey information, (b) they can provide students with more concrete information that is easier to remember, and (c) they can take advantage of the superior memorability of pictures relative to words (see “Dual Coding”).

Words that are more concrete are both recognized and recalled better than abstract words (Gorman, 1961 ; e.g., “button” and “bound,” respectively). Furthermore, it has been demonstrated that information that is more concrete and imageable enhances the learning of associations, even with abstract content (Caplan & Madan, 2016 ; Madan, Glaholt, & Caplan, 2010 ; Paivio, 1971 ). Following from this, providing concrete examples during instruction should improve retention of related abstract concepts, rather than the concrete examples alone being remembered better. Concrete examples can be useful both during instruction and during practice problems. Having students actively explain how two examples are similar and encouraging them to extract the underlying structure on their own can also help with transfer. In a laboratory study, Berry ( 1983 ) demonstrated that students performed well when given concrete practice problems, regardless of the use of verbalization (akin to elaborative interrogation), but that verbalization helped students transfer understanding from concrete to abstract problems. One particularly important area of future research is determining how students can best make the link between concrete examples and abstract ideas.

Since abstract concepts are harder to grasp than concrete information (Paivio, Walsh, & Bons, 1994 ), it follows that teachers ought to illustrate abstract ideas with concrete examples. However, care must be taken when selecting the examples. LeFevre and Dixon ( 1986 ) provided students with both concrete examples and abstract instructions and found that when these were inconsistent, students followed the concrete examples rather than the abstract instructions, potentially constraining the application of the abstract concept being taught. Lew, Fukawa-Connelly, Mejí-Ramos, and Weber ( 2016 ) used an interview approach to examine why students may have difficulty understanding a lecture. Responses indicated that some issues were related to understanding the overarching topic rather than the component parts, and to the use of informal colloquialisms that did not clearly follow from the material being taught. Both of these issues could have potentially been addressed through the inclusion of a greater number of relevant concrete examples.

One concern with using concrete examples is that students might only remember the examples – especially if they are particularly memorable, such as fun or gimmicky examples – and will not be able to transfer their understanding from one example to another, or more broadly to the abstract concept. However, there does not seem to be any evidence that fun relevant examples actually hurt learning by harming memory for important information. Instead, fun examples and jokes tend to be more memorable, but this boost in memory for the joke does not seem to come at a cost to memory for the underlying concept (Baldassari & Kelley, 2012 ). However, two important caveats need to be highlighted. First, to the extent that the more memorable content is not relevant to the concepts of interest, learning of the target information can be compromised (Harp & Mayer, 1998 ). Thus, care must be taken to ensure that all examples and gimmicks are, in fact, related to the core concepts that the students need to acquire, and do not contain irrelevant perceptual features (Kaminski & Sloutsky, 2013 ).

The second issue is that novices often notice and remember the surface details of an example rather than the underlying structure. Experts, on the other hand, can extract the underlying structure from examples that have divergent surface features (Chi, Feltovich, & Glaser, 1981 ; see Fig.  5 for an example from physics). Gick and Holyoak ( 1983 ) tried to get students to apply a rule from one problem to another problem that appeared different on the surface, but was structurally similar. They found that providing multiple examples helped with this transfer process compared to only using one example – especially when the examples provided had different surface details. More work is also needed to determine how many examples are sufficient for generalization to occur (and this, of course, will vary with contextual factors and individual differences). Further research on the continuum between concrete/specific examples and more abstract concepts would also be informative. That is, if an example is not concrete enough, it may be too difficult to understand. On the other hand, if the example is too concrete, that could be detrimental to generalization to the more abstract concept (although a diverse set of very concrete examples may be able to help with this). In fact, in a controversial article, Kaminski, Sloutsky, and Heckler ( 2008 ) claimed that abstract examples were more effective than concrete examples. Later rebuttals of this paper contested whether the abstract versus concrete distinction was clearly defined in the original study (see Reed, 2008 , for a collection of letters on the subject). This ideal point along the concrete-abstract continuum might also interact with development.

Finding teacher blog posts on concrete examples proved to be more difficult than for the other strategies in this review. One optimistic possibility is that teachers frequently use concrete examples in their teaching, and thus do not think of this as a specific contribution from cognitive psychology; the one blog post we were able to find that discussed concrete examples suggests that this might be the case (Boulton, 2016 ). The idea of “linking abstract concepts with concrete examples” is also covered in 25% of teacher-training textbooks used in the US, according to the report by Pomerance et al. ( 2016 ); this is the second most frequently covered of the six strategies, after “posing probing questions” (i.e., elaborative interrogation). A useful direction for future research would be to establish how teachers are using concrete examples in their practice, and whether we can make any suggestions for improvement based on research into the science of learning. For example, if two examples are better than one (Bauernschmidt, 2017 ), are additional examples also needed, or are there diminishing returns from providing more examples? And, how can teachers best ensure that concrete examples are consistent with prior knowledge (Reed, 2008 )?

Dual coding

Both the memory literature and folk psychology support the notion of visual examples being beneficial—the adage of “a picture is worth a thousand words” (traced back to an advertising slogan from the 1920s; Meider, 1990 ). Indeed, it is well-understood that more information can be conveyed through a simple illustration than through several paragraphs of text (e.g., Barker & Manji, 1989 ; Mayer & Gallini, 1990 ). Illustrations can be particularly helpful when the described concept involves several parts or steps and is intended for individuals with low prior knowledge (Eitel & Scheiter, 2015 ; Mayer & Gallini, 1990 ). Figure  6 provides a concrete example of this, illustrating how information can flow through neurons and synapses.

In addition to being able to convey information more succinctly, pictures are also more memorable than words (Paivio & Csapo, 1969 , 1973 ). In the memory literature, this is referred to as the picture superiority effect , and dual coding theory was developed in part to explain this effect. Dual coding follows from the notion of text being accompanied by complementary visual information to enhance learning. Paivio ( 1971 , 1986 ) proposed dual coding theory as a mechanistic account for the integration of multiple information “codes” to process information. In this theory, a code corresponds to a modal or otherwise distinct representation of a concept—e.g., “mental images for ‘book’ have visual, tactual, and other perceptual qualities similar to those evoked by the referent objects on which the images are based” (Clark & Paivio, 1991 , p. 152). Aylwin ( 1990 ) provides a clear example of how the word “dog” can evoke verbal, visual, and enactive representations (see Fig.  7 for a similar example for the word “SPOON”, based on Aylwin, 1990 (Fig.  2 ) and Madan & Singhal, 2012a (Fig.  3 )). Codes can also correspond to emotional properties (Clark & Paivio, 1991 ; Paivio, 2013 ). Clark and Paivio ( 1991 ) provide a thorough review of dual coding theory and its relation to education, while Paivio ( 2007 ) provides a comprehensive treatise on dual coding theory. Broadly, dual coding theory suggests that providing multiple representations of the same information enhances learning and memory, and that information that more readily evokes additional representations (through automatic imagery processes) receives a similar benefit.

Paivio and Csapo ( 1973 ) suggest that verbal and imaginal codes have independent and additive effects on memory recall. Using visuals to improve learning and memory has been particularly applied to vocabulary learning (Danan, 1992 ; Sadoski, 2005 ), but has also shown success in other domains such as in health care (Hartland, Biddle, & Fallacaro, 2008 ). To take advantage of dual coding, verbal information should be accompanied by a visual representation when possible. However, while the studies discussed all indicate that the use of multiple representations of information is favorable, it is important to acknowledge that each representation also increases cognitive load and can lead to over-saturation (Mayer & Moreno, 2003 ).

Given that pictures are generally remembered better than words, it is important to ensure that the pictures students are provided with are helpful and relevant to the content they are expected to learn. McNeill, Uttal, Jarvin, and Sternberg ( 2009 ) found that providing visual examples decreased conceptual errors. However, McNeill et al. also found that when students were given visually rich examples, they performed more poorly than students who were not given any visual example, suggesting that the visual details can at times become a distraction and hinder performance. Thus, it is important to consider that images used in teaching are clear and not ambiguous in their meaning (Schwartz, 2007 ).

Further broadening the scope of dual coding theory, Engelkamp and Zimmer ( 1984 ) suggest that motor movements, such as “turning the handle,” can provide an additional motor code that can improve memory, linking studies of motor actions (enactment) with dual coding theory (Clark & Paivio, 1991 ; Engelkamp & Cohen, 1991 ; Madan & Singhal, 2012c ). Indeed, enactment effects appear to primarily occur during learning, rather than during retrieval (Peterson & Mulligan, 2010 ). Along similar lines, Wammes, Meade, and Fernandes ( 2016 ) demonstrated that generating drawings can provide memory benefits beyond what could otherwise be explained by visual imagery, picture superiority, and other memory enhancing effects. Providing convergent evidence, even when overt motor actions are not critical in themselves, words representing functional objects have been shown to enhance later memory (Madan & Singhal, 2012b ; Montefinese, Ambrosini, Fairfield, & Mammarella, 2013 ). This indicates that motoric processes can improve memory similarly to visual imagery, similar to memory differences for concrete vs. abstract words. Further research suggests that automatic motor simulation for functional objects is likely responsible for this memory benefit (Madan, Chen, & Singhal, 2016 ).

When teachers combine visuals and words in their educational practice, however, they may not always be taking advantage of dual coding – at least, not in the optimal manner. For example, a recent discussion on Twitter centered around one teacher’s decision to have 7 th Grade students replace certain words in their science laboratory report with a picture of that word (e.g., the instructions read “using a syringe …” and a picture of a syringe replaced the word; Turner, 2016a ). Other teachers argued that this was not dual coding (Beaven, 2016 ; Williams, 2016 ), because there were no longer two different representations of the information. The first teacher maintained that dual coding was preserved, because this laboratory report with pictures was to be used alongside the original, fully verbal report (Turner, 2016b ). This particular implementation – having students replace individual words with pictures – has not been examined in the cognitive literature, presumably because no benefit would be expected. In any case, we need to be clearer about implementations for dual coding, and more research is needed to clarify how teachers can make use of the benefits conferred by multiple representations and picture superiority.

Critically, dual coding theory is distinct from the notion of “learning styles,” which describe the idea that individuals benefit from instruction that matches their modality preference. While this idea is pervasive and individuals often subjectively feel that they have a preference, evidence indicates that the learning styles theory is not supported by empirical findings (e.g., Kavale, Hirshoren, & Forness, 1998 ; Pashler, McDaniel, Rohrer, & Bjork, 2008 ; Rohrer & Pashler, 2012 ). That is, there is no evidence that instructing students in their preferred learning style leads to an overall improvement in learning (the “meshing” hypothesis). Moreover, learning styles have come to be described as a myth or urban legend within psychology (Coffield, Moseley, Hall, & Ecclestone, 2004 ; Hattie & Yates, 2014 ; Kirschner & van Merriënboer, 2013 ; Kirschner, 2017 ); skepticism about learning styles is a common stance amongst evidence-informed teachers (e.g., Saunders, 2016 ). Providing evidence against the notion of learning styles, Kraemer, Rosenberg, and Thompson-Schill ( 2009 ) found that individuals who scored as “verbalizers” and “visualizers” did not perform any better on experimental trials matching their preference. Instead, it has recently been shown that learning through one’s preferred learning style is associated with elevated subjective judgements of learning, but not objective performance (Knoll, Otani, Skeel, & Van Horn, 2017 ). In contrast to learning styles, dual coding is based on providing additional, complementary forms of information to enhance learning, rather than tailoring instruction to individuals’ preferences.

Genuine educational environments present many opportunities for combining the strategies outlined above. Spacing can be particularly potent for learning if it is combined with retrieval practice. The additive benefits of retrieval practice and spacing can be gained by engaging in retrieval practice multiple times (also known as distributed practice; see Cepeda et al., 2006 ). Interleaving naturally entails spacing if students interleave old and new material. Concrete examples can be both verbal and visual, making use of dual coding. In addition, the strategies of elaboration, concrete examples, and dual coding all work best when used as part of retrieval practice. For example, in the concept-mapping studies mentioned above (Blunt & Karpicke, 2014 ; Karpicke, Blunt, et al., 2014 ), creating concept maps while looking at course materials (e.g., a textbook) was not as effective for later memory as creating concept maps from memory. When practicing elaborative interrogation, students can start off answering the “how” and “why” questions they pose for themselves using class materials, and work their way up to answering them from memory. And when interleaving different problem types, students should be practicing answering them rather than just looking over worked examples.

But while these ideas for strategy combinations have empirical bases, it has not yet been established whether the benefits of the strategies to learning are additive, super-additive, or, in some cases, incompatible. Thus, future research needs to (a) better formalize the definition of each strategy (particularly critical for elaboration and dual coding), (b) identify best practices for implementation in the classroom, (c) delineate the boundary conditions of each strategy, and (d) strategically investigate interactions between the six strategies we outlined in this manuscript.

Acknowledgements

YW and MAS were partially supported by a grant from The IDEA Center.

Availability of data and materials

Authors’ contributions.

YW took the lead on writing the “Spaced practice”, “Interleaving”, and “Elaboration” sections. CRM took the lead on writing the “Concrete examples” and “Dual coding” sections. MAS took the lead on writing the “Retrieval practice” section. All authors edited each others’ sections. All authors were involved in the conception and writing of the manuscript. All authors gave approval of the final version.

Ethics approval and consent to participate

Consent for publication, competing interests.

YW and MAS run a blog, “The Learning Scientists Blog”, which is cited in the tutorial review. The blog does not make money. Free resources on the strategies described in this tutorial review are provided on the blog. Occasionally, YW and MAS are invited by schools/school districts to present research findings from cognitive psychology applied to education.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yana Weinstein, Email: ude.lmu@nietsnieW_anaY .

Christopher R. Madan, Email: [email protected] .

Megan A. Sumeracki, Email: ude.cir@ikcaremusm .

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  • Partnerships

Effective teaching and its relation to our scientific understanding of learning

Effective teaching and its relation to our scientific understanding of learning

Effective teaching

Executive summary

  • A simple framework for thinking and talking about classroom learning might consist of: engagement for learning, building of new knowledge and understanding, and consolidation of learning.
  • In teacher training and development , we now have sufficient knowledge to begin explaining and promoting core classroom learning practices using scientifically informed concepts of learning .
  • help increase the focus on learning
  • provide the first defence against myths
  • provide an authentic foundation for insight and practice
  • inform classroom implementation of reform
  • support professionalism by empowering teachers with a scientific understanding of teaching and learning

A scientific focus on learning

On a global level, while many countries have increased their spending on education and increased the number of children attending school, the goal of ensuring the quality of provision has been more elusive 1 . For example, around half or more of children completing primary schooling in many countries (including India, Bangladesh, Pakistan, Kenya, and Tanzania) cannot read even the simplest texts or perform simple arithmetic. One economist estimates that, at the current rate of progress, it will be well over 100 years before students in developing countries can produce similar results in science exams as today’s students in developed countries 2 . In the last 1-2 decades, governments, scientists, and educators have become increasingly interested in developing a 21 st -century education system supported by more concrete evidence of how we learn. The different names attached to this interdisciplinary work include “Mind, Brain, and Education,” “Science of Learning,” “Neuroeducation,” and “Educational Neuroscience.” These names reflect how neuroscience, psychology, genetics, and many other disciplines are becoming increasingly relevant for our emerging scientific understanding of learning.

There are at least three practical ways in which a scientific understanding of learning can benefit teachers and students in the classroom:

It appears self-evident that a more scientific understanding of learning amongst teachers would help dissipate the many neuromyths , or unscientific ideas about the brain, that are prevalent in education. Many neuromyths are associated with poor practice, and they make a strong argument for including a basic understanding of the scientific principles of learning in teacher training and development 3,4 .

Despite increasing public interest, the public’s awareness of neuroscience is never likely to keep pace with our accelerating growth in understanding the brain. This suggests the problem of neuromyths is likely to grow, at least in countries where teacher training and development continue to omit a scientific understanding of learning. At present, much of the information that reaches teachers about the brain is from the public media 5 , making it impractical to consider “protecting” teachers from ideas related to neuroscience—even if this could be morally justified.

  • Interventions

Scientific research is applying new technologies and ideas to uncover fresh insights into how we learn. Neuroimaging, for example, allows us to study brain function while adults and children are acquiring skills such as mathematics and reading. By understanding underlying learning processes, we can better develop new interventions to enhance children’s achievements. There have been several experimental studies indicating promising ways to improve classroom learning. In some cases, we have scientifically based ideas for interventions well-tested in classrooms and shown to provide benefits. These include the spacing out and interleaving of learning sessions, testing regimes, and new reading approaches. Others have shown great promise under controlled laboratory conditions or small-scale educational applications, such as interventions based on exercise, sleep, or on providing particular schedules of reward. Large-scale trials are now underway in schools that are focused on these and other ideas 6 .

  • Informing the day-to-day practice of a teacher

Perhaps, however, the most significant benefit of underpinning education with a scientific understanding of learning may be its influence on the day-to-day practice of teachers. Ultimately, most would agree that a key determinant of a student achieving his or her potential is the quality of their teacher’s practice. Can a scientific understanding of learning help with this quality?

This brief will particularly focus on this last issue. It considers the readiness of our scientific knowledge to explain and provide insight into core principles in teaching that have been established elsewhere as benefiting learning outcomes.

Teaching quality and the understanding of learning

A recent review 7 of the international literature identified six attributes of good teaching, of which only two were supported by the strongest evidence:

  • (Pedagogical) content knowledge (Strong evidence of impact on student outcomes)

The most effective teachers have deep knowledge of the subjects they teach. When teachers’ knowledge falls below a certain level, it is a significant impediment to students’ learning. As well as a strong understanding of the material being taught, teachers must also understand the ways students think about the content, evaluate the thinking behind students’ own methods, and identify students’ common misconceptions.

  • Quality of instruction (Strong evidence of impact on student outcomes)

Quality of instruction includes elements such as effective questioning and use of assessment by teachers. Specific practices like reviewing previous learning, providing model responses for students, giving adequate time for practice to embed skills securely, and progressively introducing new learning (scaffolding) are also elements of high-quality instruction. 7

Knowledge of learning processes cannot substitute for knowledge of the topic being taught, but the definition of “pedagogical content knowledge” above includes both. It makes specific reference to understanding how students think about the content. In terms of “quality of instruction,” the Coe et al. (2014) report emphasizes elements such as effective questioning and use of assessment by teachers. High-quality instruction is considered to include specific practices, such as reviewing previous learning, providing model responses for students, giving adequate time for practice to embed skills securely, and progressively scaffolding new learning. The report also concludes there is moderate evidence for the effects of classroom climate and management, and some evidence for the impact of teacher beliefs and professional behaviors.

In short, current research demonstrates that student outcomes are significantly influenced by the understanding of the teacher and his/her evaluation of student thinking and learning. On this basis, informed consideration of students’ learning processes may be key to improving outcomes. Also, although specific practices can be prescribed to teachers, their effective implementation is likely to rely on understanding how they are supposed to operate. Simply identifying and prescribing “what works” may not be sufficient for ensuring the success of top-down educational reform. Indeed, rather than implement a “one size fits all” approach, teachers continuously adapt their teaching to the learner and the context, applying their own theory about their students’ mental processes and considering how they can influence these processes to scaffold learning 8 . It has been said that trying to teach without understanding learning is a bit like trying to fix a washing machine without knowing how it works 9 . Of course, teachers support learning behaviors that are much more complex than a washing machine. On this basis, the literature reviewed by Coe et al. (2014) and others support the notion that students benefit from teachers who understand learning processes.

A scientific understanding of learning is also particularly important for ensuring educational reform in a culturally diverse world. Respect for cultural diversity is emphasized by the Education-2030 targets (Target 4.7 10 ), and attention to diversity requires teachers to adapt teaching strategy. Indeed, teachers’ response to top-down reform is itself a process of cultural adaptation 11 , with practitioners integrating their own reflections, attitudes, and behaviors with the recommended changes 12 . In other words, teachers may take what they are given, but they will make it their own. This undermines any sense that a purely prescriptive approach to educational reform can ever be entirely successful. The success of reform will always rely, in large part, on teachers being sufficiently empowered with an understanding of learning. They will then be better positioned to interpret appropriately the processes by which learning practices are supposed to achieve their goals and to understand how these ideas may be adapted for their own students while preserving these processes.

The neuroscience of learning—with synaptic plasticity as the basis of learning and memory—can provide an inherently proactive and hopeful message. There is already some empirical evidence to suggest that teacher development within the neuroscience of learning can motivate teachers and their students to attend and participate more in the learning process. After receiving one such programme of development, secondary school teachers were more self-aware of how their own teaching behaviors had the capacity to change students’ brains as students experienced, modeled, utilized, and constructed their own knowledge 13 . When awareness of the brain’s plasticity is passed on to students, this can improve student awareness of their role in constructing their own abilities, which has been shown to improve their growth mindset and resilience in their academic studies, reduce dropout rates 14 , and improve self-concept and academic outcomes 15 .

What sort of knowledge might benefit teacher understanding, teaching quality, and student outcomes?

Given the above justification for all teachers to know more about the science of learning, it seems evident that a Science of Learning curriculum should be:

  • able to provide a basis for insight into how students think about and acquire learning content
  • able to provide insight into the processes underlying specific practices associated with effective teaching
  • aligned with current state-of-the-art scientific understanding
  • be accessible to educators who are not specialized in science, and who work in a range of contexts (e.g., age groups, topics, cultures)

Communication across the gap between neuroscience and education

Although the potential advantages are many, making scientific knowledge accessible to those who are not specialists in science is always challenging. There are inherent dangers of “boiled down” messages about the science leading to misinterpretations and poor practice in the classroom 4 . On the other hand, of course, messages that are too complex in their content or communication may communicate little or also be easily misunderstood. It is also possible that the scientific messages can become overly biased by the present preoccupations of the scientific field or the professional aims of scientists, leading to statements that are not as educationally relevant or as appropriate as they might be.

The language and terms of science regarding learning can also be quite different from those used by educators. Some words such as “attention” and even “learning” can have quite different definitions in the two domains 16 . In light of this, a simple theoretical framework is needed for classroom learning that is appropriately scientifically and educationally meaningful, so helping to map concepts helpfully across education/public domains and the sciences of mind and brain.

A simple framework for classroom learning

A simple framework for thinking and talking about learning in the classroom might comprise three categories of the underlying process, all of which can actively involve the teacher: (1) engagement for learning , (2) building of new knowledge and understanding , and (3) consolidation of learning . These are broad categories intended to help structure an understanding of the relevance of scientific insight for classroom practice. They attempt to minimize confusion of scientific and educational terms where these are not equivalent. For example, engagement is an educational term that is not often encountered in the scientific literature—it is not constrained by a scientific definition. A range of scientific aspects of learning can be drawn together under this broad educational heading. These include new insights into emotional processing and attention while allowing the discussion to consider these aspects as distinct but potentially related. The heading building of new knowledge and understanding might include Vygotskian/Bruner constructivist notions of an expert scaffolding a novice’s thinking 17 , but could also include more Piagetian approaches that involve, for example, exposure to cognitive conflict 18 . Consolidation of learning has appropriate and helpful scientific associations with memory consolidation processes, but might also include effects of educational practice such as automatization.

Figure 1: Consolidate, Build, Engage

Learning can be assumed to begin with engagement, and consolidation of new content is only likely after it has been initially represented in the student’s mind/brain (i.e., following the building of new knowledge and understanding). Therefore, these elements might be represented as operating in the sequence of engage -> build -> consolidate . However, it is also possible to consider some movement in the opposite direction (e.g., finding ways to engage children in practice that consolidates freshly learnt ideas). Also, different parts of a learning experience might involve processes in more than one category occurring simultaneously. Therefore, these categories of the learning experience are better represented as in Figure 1, with the possibility of moving freely between them.

The science of engagement, building, and consolidation

Scientific research that is relevant to each of these three areas has been briefly reviewed in preceding briefs 19 , 20 , 21 but the executive summaries of this review are reproduced in Table 1 for convenience:

Table 1. Scientific concepts identified with potential relevance to core teaching practices

Insight into the “how” of specific practices associated with effective teaching

To illustrate the potential helpfulness of a scientific understanding of learning, the explanatory power of the above insights will be examined regarding a selection of specific practices associated with effective teaching. The key questions are: (a) whether the particular practices can be explained in terms of this simple neurocognitive model of classroom learning, and (b) whether this deeper understanding of learning can potentially contribute to the implementation of the practice. Examples were drawn from two issues of the IAE-IBE Educational Practices Series, where practices are often referred to as “principles” 23,24 . Note that these were selected based on their generality (i.e., they were general in their potential application, and not tied to specific topics such as literacy or numeracy).

Example 1: Classroom instruction and teacher emotions

The seventh principle provided in IAE-IBE’s “Emotion and learning” in the Educational Practices Series 24 is “Provide high-quality lessons and make use of the positive emotions you experience as a teacher.” This is justified on the basis that the “motivational quality of instruction influences the perceived value of learning, thereby promoting enjoyment and reducing boredom.” Regarding teacher emotions, the report advises that “teachers should take care to show the positive emotions they feel about teaching and the subject matter, and make sure that they share positive emotions and enthusiasm with their students.”

As part of the communication underpinning the support of students’ thinking, a similar issue is considered under Build in Table 1:

  • Our mirror neuron system helps us read each other’s minds. Gestures and faces communicate knowledge and emotions both consciously and unconsciously, supporting the teacher’s transmission of concepts, confidence, and enthusiasm.             

This perspective has a slightly different emphasis that has implications for practice. It highlights the likely transmission of the teacher’s genuine emotion irrespective of their careful effort. This highlights the need for the teacher to maintain an active interest in the topics they teach, ensuring communication of genuine competence and enthusiasm.

Example 2: Guide student practice

The fifth principle of instruction provided in IAE-IBE’s “Principles of instruction” in the Educational Practices Series 23 is “Successful teachers spent more time guiding the students’ practice of new material.”

The review points out that more successful teachers check for student understanding, provide additional explanations and examples, and provide sufficient instruction for students to practice independently without difficulty. This notion of identifying where students’ understanding becomes limited (i.e., the current limit of their prior knowledge) and providing just enough support for them to move on as independently as possible reflects scientific understanding that:

Teachers can help students think meaningfully about new ideas by encouraging them to make connections with their prior knowledge. This is particularly important for children, whose neural circuitry for this connection-making process is still developing. Differences in learning and development will result in diverse individual differences within any class.

This understanding emphasizes the need to consider individual progress and differences in the rate of progress, and that different students will require different levels of scaffolding. Understanding the how/why of guidance may help practices of less successful teachers who, as highlighted in the report, provide fewer explanations, pass out worksheets, and simply tell students to work on the problems.

Example 3: Daily review

The first principle of instruction provided in IAE-IBE’s “Principles of instruction” the Educational Practices Series 23 is “Daily review can strengthen previous learning and can lead to fluent recall.” Review is recommended because practice helps us recall concepts and procedures effortlessly and automatically, and is linked to higher achievement scores. The report points out that the most effective teachers in studies of classroom instruction understand the importance of practice and begin their lessons with a five- to eight-minute review of previously covered material.

In the report, daily review is considered chiefly in terms of working memory. This explanation echoes the discussion regarding consolidation:

  • Practice and rehearsal of freshly learnt knowledge cause it to become automatically accessible. This frees up the brain’s limited capacity to pay conscious attention, and so be ready for further learning.

Scientific research has added to our understanding of why testing may be advantageous for learning:

  • Answering questions, applying knowledge in new situations, discussing it with others, or expressing it in new forms consolidate our learning by helping us to store it in different ways—making it easier to recall and apply it.

There are, however, further justifications for daily review, when considered from a perspective that includes the whole learning process. In terms of supporting students to build their knowledge and understanding:

  • Being aware of students’ prior knowledge is important for a teacher because this is the foundation on which the students’ new knowledge will build.

Daily review may also be important, therefore, to identify students’ prior knowledge (which may be different than the knowledge that has been taught) and so indicate where and how the building of new knowledge might resume (e.g., where additional support is needed).

A scientific understanding of the learning processes underlying daily review can also contribute to its implementation as in the following examples:

  • Daily review might benefit from using novel contexts and examples for testing.
  • Daily review might pay particular attention to knowledge that will soon be built upon.
  • Given the role of sleep in consolidation, morning review of the previous day’s learning may be more meaningful for informing the teacher than end-of-day review of the same day’s learning.
  • Review may benefit from an environment that diminishes anxiety and attracts the full engagement of the student (e.g., praise, game-like rewards).

Broader mapping of the extent to which scientific concepts can underpin core teaching practices

Science concepts were mapped to each of the 10 practices/principles identified in “Effective instruction” and “Emotions and learning” to determine the extent to which the identified scientific concepts could provide insight into core teaching principles. A scientific concept was considered to relate to the principle when it offered insight into how/why the principle works and/or might be implemented (see Table 2).

Coverage of principles/practices was almost, but not entirely, comprehensive. Principle 4 in “Principles of instruction” was “Provide models.” No basis for its underlying processes could be identified amongst the scientific concepts. Discussion of the principle included reference to guiding the student and also encouraging independent practice. These ideas could be supported by the scientific principles identified—and this is evident in the mapping of two other principles in this issue related to these references (Principles 5 and 9). However, Principle 4 made much of “worked examples” and the possibility of mixing worked examples and problems to solve. As with other principles in this text, this is well-supported by educational research as being an effective approach. The author, however, finds it difficult to explain the processes underlying this efficacy based on current scientific understanding of learning. This may highlight how the present type of mapping exercise may be useful in exposing areas where further scientific research might reveal some scientifically interesting and educationally valuable insight.

Also, a mapping was made when a scientific concept provided insight into how/why the principle generally works and/or might be implemented. That does not mean that all aspects of the principle/practice were necessarily explained by the scientific concepts identified.

It is also important to recognize that the extent of evidence underlying the scientific concepts is variable and often constrained to laboratory experiments. In most cases, the relevance of scientific research to classroom learning is itself a reasoned hypothesis that demands further testing. For example, direct evidence of variability in neural representations of material that has been tested (see author’s brief, “Consolidation of learning” ) is restricted to a single fMRI study with adults. This evidence is aligned with current psychological theory based on numerous behavioral studies. However, further imaging research study involving, say, children learning curriculum, would help validate this concept.

These caveats have practical significance for emphasising that these are early days for a scientifically informed approach to teaching and learning and help indicate where future research might be focused in areas that would be very pertinent to education. However, they do not dismiss the central claim made here: In teacher training and development, we now have sufficient knowledge to begin explaining and promoting core classroom learning practices using scientifically informed concepts of learning.

Table 2. Mapping of core scientific concepts to teaching principles as identified in Ref. 23,24

  • EFA. 2015 Global Monitoring Report – Education for All 2000-2015: Achievements and Challenge. (UNESCO, Paris, 2015).
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  • Howard-Jones, P. A. Neuroscience and education: Myths and messages. Nature Reviews Neuroscience 15, 817-824 (2014).
  • Dekker, S., Lee, N. C., Howard-Jones, P. A. & Jolles, J. Neuromyths in education: Prevalence and predictors of misconceptions among teachers. Frontiers in Psychology 3, doi:10.3389/fpsyg.2012.00429 (2012)
  • WellcomeTrust. (ed E. Philippou) (Wellcome Trust, London, 2014).
  • Coe, R., Aloisi, C., Higgins, S. & Major, L. E. What makes great teaching? Review of the underpinning research. (Centre for Evaluation Monitoring (CEM, Durham), Durham University, Sutton Trust (London), 2014).
  • Mevorach, M. & Strauss, S. Teacher educators’ in-action mental models in different teaching situations. Teachers and Teaching 18, 25-41, doi:10.1080/13540602.2011.622551 (2012).
  • Dehaene, S. Reading in the Brain . (Viking Penguin, 2009).
  • UNESCO-UNICEF. Education 2030: Incheon Declaration and Framework for Action for the implementation of Sustainable Development Goal 4. (2015).
  • Zhou, J. X. & Fischer, K. W. Culturally appropriate education: Insights from educational neuroscience. Mind Brain and Education 7, 225-231, doi:10.1111/mbe.12030 (2013).
  • Berry, J. W. in Acculturation: Advances in theory, measurement, and applied research (eds K. Chun, P. Balls-Orsanista, & G. Marin)  Pages: 17-37 (APA Press, 2003).
  • Dubinsky, J. M., Roehrig, G. & Varma, S. Infusing neuroscience into teacher professional development. Educational Researcher 42, 317-329, doi:10.3102/0013189×13499403 (2013).
  • Paunesku, D. et al. Mind-set interventions are a scalable treatment for academic underachievement. Psychological Science 26, 784-793, doi:10.1177/0956797615571017 (2015).
  • Blackwell, L. S., Trzesniewski, K. H. & Dweck, C. S. Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development 78, 246-263 (2007).
  • Howard-Jones, P. A. Introducing Neuroeducational Research: Neuroscience, Education and the Brain from Contexts to Practice .  (Routledge, 2010).
  • Vygotsky, L. S. Mind in Society: The development of higher psychological processes . (Harvard University Press, 1978).
  • Piaget, J. & Cook, M. T. The origins of intelligence in children . (International University Press, 1952).
  • Howard-Jones, P. A. Engagement for Learning: Scientific insights with potential relevance for teachers’ engagement of students in their learning. (International Bureau of Education, Geneva, 2016).
  • Howard-Jones, P. A. Building of new knowledge and understanding: Scientific insights with potential relevance to teacher-guided construction of student thinking. (International Bureau of Education, Geneva, 2016).
  • Howard-Jones, P. A. Consolidation of learning: Scientific insights with potential relevance for supporting students’ consolidation of their learning. (International Bureau of Education, Geneva, 2016).
  • Fales, C. L., Becerril, K. E., Luking, K. R. & Barch, D. M. Emotional-stimulus processing in trait anxiety is modulated by stimulus valence during neuroimaging. Cogn. Emot. 24, 200-222, doi:10.1080/02699930903384691 (2010).
  • Rosenshine, B. Principles of Instruction. (International Academy of Education (IAE) and International Bureau of Education (IBE), Brussels and Geneva, 2010).
  • Pekrun, R. Emotions and Learning. (International Academy of Education (IAE) and International Bureau of Education (IBE), Brussels and Geneva, 2014).

Classroom Science

Strategies for Supporting Students to Engage in Scientific Argumentation

  • Jun 20, 2023

By Jamie N. Mikeska, ETS, Senior Research Scientist, K-12 Teaching, Learning, and Assessment Center

Engaging students in argumentation is one of the scientific practices essential to helping students develop their ability to participate productively in scientific sensemaking. 

Scientific argumentation involves students in generating and responding to scientific claims and evidence-based reasoning. While there are many ways for science teachers to provide opportunities for their students to engage in scientific argumentation as part of classroom learning activities, facilitating high-quality, argumentation-focused discussions is one of the easiest and most accessible ways to do so. When facilitating argumentation-focused discussions, it is important for teachers to attend to and provide opportunities for their students to engage in two related, yet distinct, components of scientific argumentation: (a) argument construction and (b) argument critique. Argument construction involves students in using evidence and reasoning to construct, defend, and/or revise their own and others’ scientific claims. Argument critique focuses on opportunities for students to compare and critique various claims and evidence-based reasoning, as well as try to persuade others as they work to build towards consensus in explaining scientific phenomena. There are a variety of teaching moves that teachers can use to promote their students’ engagement in these two aspects of scientific argumentation during small or whole class discussions (Mikeska & Howell, 2020; Mikeska & Lottero-Perdue, 2022).

When planning to engage students in argument construction and critique, teachers can first consider their goals for the discussion that they plan to facilitate, as well as their students’ previous experiences and their initial ideas related to the scientific phenomenon being investigated.

  • Teachers can think about how their students are currently making sense of and understanding the scientific phenomenon under study and consider the strengths of students’ ideas individually and collectively and potential areas for growth.
  • They can also consider how their students could take on specific roles during the discussion, such as some students being focused on engaging in argument construction while others address the argument critique aspect more directly.
  • Then, teachers can think about various teaching moves they might want to use to facilitate productive student engagement in argument construction and critique during the discussion.
  • Additionally, teachers can determine when and how will they want their students to share their initial claims and evidence-based reasoning with each other?
  • How might they encourage their students to compare and critique ideas or agree and disagree with each other respectfully?

Finally, teachers might also consider various ways that they can develop the conversation. This may include attention to how they plan to encourage students to modify their initial ideas, build towards consensus, or use students’ previous experiences or knowledge as a resource. Teachers’ careful planning and use of a variety of argument construction and critique prompts are both important for the successful facilitation of argumentation-focused discussions in K-12 classrooms.

References   Mikeska, J.N., & Howell, H. (2020). Simulations as practice-based spaces to support elementary science teachers in learning how to facilitate argumentation-focused science discussions. Journal of Research in Science Teaching, 57(9), 1356-1399. https://doi.org/10.1002/tea.21659 Mikeska, J. N. & Lottero‐Perdue, P. S. (2022). How preservice and in‐service elementary teachers engage student avatars in scientific argumentation within a simulated classroom environment. Science Education, 106(4), 980-1009. https://doi.org/10.1002/sce.21726 

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The science of effective learning with spacing and retrieval practice

  • Shana K. Carpenter   ORCID: orcid.org/0000-0003-0784-9026 1 ,
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Research on the psychology of learning has highlighted straightforward ways of enhancing learning. However, effective learning strategies are underused by learners. In this Review, we discuss key research findings on two specific learning strategies: spacing and retrieval practice. We focus on how these strategies enhance learning in various domains across the lifespan, with an emphasis on research in applied educational settings. We also discuss key findings from research on metacognition — learners’ awareness and regulation of their own learning. The underuse of effective learning strategies by learners could stem from false beliefs about learning, lack of awareness of effective learning strategies or the counter-intuitive nature of these strategies. Findings in learner metacognition highlight the need to improve learners’ subjective mental models of how to learn effectively. Overall, the research discussed in this Review has important implications for the increasingly common situations in which learners must effectively monitor and regulate their own learning.

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Scientific Thinking and Critical Thinking in Science Education 

Two Distinct but Symbiotically Related Intellectual Processes

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Scientific thinking and critical thinking are two intellectual processes that are considered keys in the basic and comprehensive education of citizens. For this reason, their development is also contemplated as among the main objectives of science education. However, in the literature about the two types of thinking in the context of science education, there are quite frequent allusions to one or the other indistinctly to refer to the same cognitive and metacognitive skills, usually leaving unclear what are their differences and what are their common aspects. The present work therefore was aimed at elucidating what the differences and relationships between these two types of thinking are. The conclusion reached was that, while they differ in regard to the purposes of their application and some skills or processes, they also share others and are related symbiotically in a metaphorical sense; i.e., each one makes sense or develops appropriately when it is nourished or enriched by the other. Finally, an orientative proposal is presented for an integrated development of the two types of thinking in science classes.

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how does research help in scientific learning for class 4

Philosophical Inquiry and Critical Thinking in Primary and Secondary Science Education

Fostering scientific literacy and critical thinking in elementary science education.

Rui Marques Vieira & Celina Tenreiro-Vieira

how does research help in scientific learning for class 4

Enhancing Scientific Thinking Through the Development of Critical Thinking in Higher Education

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Education is not the learning of facts, but the training of the mind to think. Albert Einstein

1 Introduction

In consulting technical reports, theoretical frameworks, research, and curricular reforms related to science education, one commonly finds appeals to scientific thinking and critical thinking as essential educational processes or objectives. This is confirmed in some studies that include exhaustive reviews of the literature in this regard such as those of Bailin ( 2002 ), Costa et al. ( 2020 ), and Santos ( 2017 ) on critical thinking, and of Klarh et al. ( 2019 ) and Lehrer and Schauble ( 2006 ) on scientific thinking. However, conceptualizing and differentiating between both types of thinking based on the above-mentioned documents of science education are generally difficult. In many cases, they are referred to without defining them, or they are used interchangeably to represent virtually the same thing. Thus, for example, the document A Framework for K-12 Science Education points out that “Critical thinking is required, whether in developing and refining an idea (an explanation or design) or in conducting an investigation” (National Research Council (NRC), 2012 , p. 46). The same document also refers to scientific thinking when it suggests that basic scientific education should “provide students with opportunities for a range of scientific activities and scientific thinking , including, but not limited to inquiry and investigation, collection and analysis of evidence, logical reasoning, and communication and application of information” (NRC, 2012 , p. 251).

A few years earlier, the report Science Teaching in Schools in Europe: Policies and Research (European Commission/Eurydice, 2006 ) included the dimension “scientific thinking” as part of standardized national science tests in European countries. This dimension consisted of three basic abilities: (i) to solve problems formulated in theoretical terms , (ii) to frame a problem in scientific terms , and (iii) to formulate scientific hypotheses . In contrast, critical thinking was not even mentioned in such a report. However, in subsequent similar reports by the European Commission/Eurydice ( 2011 , 2022 ), there are some references to the fact that the development of critical thinking should be a basic objective of science teaching, although these reports do not define it at any point.

The ENCIENDE report on early-year science education in Spain also includes an explicit allusion to critical thinking among its recommendations: “Providing students with learning tools means helping them to develop critical thinking , to form their own opinions, to distinguish between knowledge founded on the evidence available at a certain moment (evidence which can change) and unfounded beliefs” (Confederation of Scientific Societies in Spain (COSCE), 2011 , p. 62). However, the report makes no explicit mention to scientific thinking. More recently, the document “ Enseñando ciencia con ciencia ” (Teaching science with science) (Couso et al., 2020 ), sponsored by Spain’s Ministry of Education, also addresses critical thinking:

(…) with the teaching approach through guided inquiry students learn scientific content, learn to do science (procedures), learn what science is and how it is built, and this (...) helps to develop critical thinking , that is, to question any statement that is not supported by evidence. (Couso et al., 2020 , p. 54)

On the other hand, in referring to what is practically the same thing, the European report Science Education for Responsible Citizenship speaks of scientific thinking when it establishes that one of the challenges of scientific education should be: “To promote a culture of scientific thinking and inspire citizens to use evidence-based reasoning for decision making” (European Commission, 2015 , p. 14). However, the Pisa 2024 Strategic Vision and Direction for Science report does not mention scientific thinking but does mention critical thinking in noting that “More generally, (students) should be able to recognize the limitations of scientific inquiry and apply critical thinking when engaging with its results” (Organization for Economic Co-operation and Development (OECD), 2020 , p. 9).

The new Spanish science curriculum for basic education (Royal Decree 217/ 2022 ) does make explicit reference to scientific thinking. For example, one of the STEM (Science, Technology, Engineering, and Mathematics) competency descriptors for compulsory secondary education reads:

Use scientific thinking to understand and explain the phenomena that occur around them, trusting in knowledge as a motor for development, asking questions and checking hypotheses through experimentation and inquiry (...) showing a critical attitude about the scope and limitations of science. (p. 41,599)

Furthermore, when developing the curriculum for the subjects of physics and chemistry, the same provision clarifies that “The essence of scientific thinking is to understand what are the reasons for the phenomena that occur in the natural environment to then try to explain them through the appropriate laws of physics and chemistry” (Royal Decree 217/ 2022 , p. 41,659). However, within the science subjects (i.e., Biology and Geology, and Physics and Chemistry), critical thinking is not mentioned as such. Footnote 1 It is only more or less directly alluded to with such expressions as “critical analysis”, “critical assessment”, “critical reflection”, “critical attitude”, and “critical spirit”, with no attempt to conceptualize it as is done with regard to scientific thinking.

The above is just a small sample of the concepts of scientific thinking and critical thinking only being differentiated in some cases, while in others they are presented as interchangeable, using one or the other indistinctly to talk about the same cognitive/metacognitive processes or practices. In fairness, however, it has to be acknowledged—as said at the beginning—that it is far from easy to conceptualize these two types of thinking (Bailin, 2002 ; Dwyer et al., 2014 ; Ennis, 2018 ; Lehrer & Schauble, 2006 ; Kuhn, 1993 , 1999 ) since they feed back on each other, partially overlap, and share certain features (Cáceres et al., 2020 ; Vázquez-Alonso & Manassero-Mas, 2018 ). Neither is there unanimity in the literature on how to characterize each of them, and rarely have they been analyzed comparatively (e.g., Hyytinen et al., 2019 ). For these reasons, I believed it necessary to address this issue with the present work in order to offer some guidelines for science teachers interested in deepening into these two intellectual processes to promote them in their classes.

2 An Attempt to Delimit Scientific Thinking in Science Education

For many years, cognitive science has been interested in studying what scientific thinking is and how it can be taught in order to improve students’ science learning (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ). To this end, Kuhn et al. propose taking a characterization of science as argument (Kuhn, 1993 ; Kuhn et al., 2008 ). They argue that this is a suitable way of linking the activity of how scientists think with that of the students and of the public in general, since science is a social activity which is subject to ongoing debate, in which the construction of arguments plays a key role. Lehrer and Schauble ( 2006 ) link scientific thinking with scientific literacy, paying especial attention to the different images of science. According to those authors, these images would guide the development of the said literacy in class. The images of science that Leherer and Schauble highlight as characterizing scientific thinking are: (i) science-as-logical reasoning (role of domain-general forms of scientific reasoning, including formal logic, heuristic, and strategies applied in different fields of science), (ii) science-as-theory change (science is subject to permanent revision and change), and (iii) science-as-practice (scientific knowledge and reasoning are components of a larger set of activities that include rules of participation, procedural skills, epistemological knowledge, etc.).

Based on a literature review, Jirout ( 2020 ) defines scientific thinking as an intellectual process whose purpose is the intentional search for information about a phenomenon or facts by formulating questions, checking hypotheses, carrying out observations, recognizing patterns, and making inferences (a detailed description of all these scientific practices or competencies can be found, for example, in NRC, 2012 ; OECD, 2019 ). Therefore, for Jirout, the development of scientific thinking would involve bringing into play the basic science skills/practices common to the inquiry-based approach to learning science (García-Carmona, 2020 ; Harlen, 2014 ). For other authors, scientific thinking would include a whole spectrum of scientific reasoning competencies (Krell et al., 2022 ; Moore, 2019 ; Tytler & Peterson, 2004 ). However, these competences usually cover the same science skills/practices mentioned above. Indeed, a conceptual overlap between scientific thinking, scientific reasoning, and scientific inquiry is often found in science education goals (Krell et al., 2022 ). Although, according to Leherer and Schauble ( 2006 ), scientific thinking is a broader construct that encompasses the other two.

It could be said that scientific thinking is a particular way of searching for information using science practices Footnote 2 (Klarh et al., 2019 ; Zimmerman & Klarh, 2018 ; Vázquez-Alonso & Manassero-Mas, 2018 ). This intellectual process provides the individual with the ability to evaluate the robustness of evidence for or against a certain idea, in order to explain a phenomenon (Clouse, 2017 ). But the development of scientific thinking also requires metacognition processes. According to what Kuhn ( 2022 ) argues, metacognition is fundamental to the permanent control or revision of what an individual thinks and knows, as well as that of the other individuals with whom it interacts, when engaging in scientific practices. In short, scientific thinking demands a good connection between reasoning and metacognition (Kuhn, 2022 ). Footnote 3

From that perspective, Zimmerman and Klarh ( 2018 ) have synthesized a taxonomy categorizing scientific thinking, relating cognitive processes with the corresponding science practices (Table 1 ). It has to be noted that this taxonomy was prepared in line with the categorization of scientific practices proposed in the document A Framework for K-12 Science Education (NRC, 2012 ). This is why one needs to understand that, for example, the cognitive process of elaboration and refinement of hypotheses is not explicitly associated with the scientific practice of hypothesizing but only with the formulation of questions. Indeed, the K-12 Framework document does not establish hypothesis formulation as a basic scientific practice. Lederman et al. ( 2014 ) justify it by arguing that not all scientific research necessarily allows or requires the verification of hypotheses, for example, in cases of exploratory or descriptive research. However, the aforementioned document (NRC, 2012 , p. 50) does refer to hypotheses when describing the practice of developing and using models , appealing to the fact that they facilitate the testing of hypothetical explanations .

In the literature, there are also other interesting taxonomies characterizing scientific thinking for educational purposes. One of them is that of Vázquez-Alonso and Manassero-Mas ( 2018 ) who, instead of science practices, refer to skills associated with scientific thinking . Their characterization basically consists of breaking down into greater detail the content of those science practices that would be related to the different cognitive and metacognitive processes of scientific thinking. Also, unlike Zimmerman and Klarh’s ( 2018 ) proposal, Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal explicitly mentions metacognition as one of the aspects of scientific thinking, which they call meta-process . In my opinion, the proposal of the latter authors, which shells out scientific thinking into a broader range of skills/practices, can be more conducive in order to favor its approach in science classes, as teachers would have more options to choose from to address components of this intellectual process depending on their teaching interests, the educational needs of their students and/or the learning objectives pursued. Table 2 presents an adapted characterization of the Vázquez-Alonso and Manassero-Mas’s ( 2018 ) proposal to address scientific thinking in science education.

3 Contextualization of Critical Thinking in Science Education

Theorization and research about critical thinking also has a long tradition in the field of the psychology of learning (Ennis, 2018 ; Kuhn, 1999 ), and its application extends far beyond science education (Dwyer et al., 2014 ). Indeed, the development of critical thinking is commonly accepted as being an essential goal of people’s overall education (Ennis, 2018 ; Hitchcock, 2017 ; Kuhn, 1999 ; Willingham, 2008 ). However, its conceptualization is not simple and there is no unanimous position taken on it in the literature (Costa et al., 2020 ; Dwyer et al., 2014 ); especially when trying to relate it to scientific thinking. Thus, while Tena-Sánchez and León-Medina ( 2022 ) Footnote 4 and McBain et al. ( 2020 ) consider critical thinking to be the basis of or forms part of scientific thinking, Dowd et al. ( 2018 ) understand scientific thinking to be just a subset of critical thinking. However, Vázquez-Alonso and Manassero-Mas ( 2018 ) do not seek to determine whether critical thinking encompasses scientific thinking or vice versa. They consider that both types of knowledge share numerous skills/practices and the progressive development of one fosters the development of the other as a virtuous circle of improvement. Other authors, such as Schafersman ( 1991 ), even go so far as to say that critical thinking and scientific thinking are the same thing. In addition, some views on the relationship between critical thinking and scientific thinking seem to be context-dependent. For example, Hyytine et al. ( 2019 ) point out that in the perspective of scientific thinking as a component of critical thinking, the former is often used to designate evidence-based thinking in the sciences, although this view tends to dominate in Europe but not in the USA context. Perhaps because of this lack of consensus, the two types of thinking are often confused, overlapping, or conceived as interchangeable in education.

Even with such a lack of unanimous or consensus vision, there are some interesting theoretical frameworks and definitions for the development of critical thinking in education. One of the most popular definitions of critical thinking is that proposed by The National Council for Excellence in Critical Thinking (1987, cited in Inter-American Teacher Education Network, 2015 , p. 6). This conceives of it as “the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action”. In other words, critical thinking can be regarded as a reflective and reasonable class of thinking that provides people with the ability to evaluate multiple statements or positions that are defensible to then decide which is the most defensible (Clouse, 2017 ; Ennis, 2018 ). It thus requires, in addition to a basic scientific competency, notions about epistemology (Kuhn, 1999 ) to understand how knowledge is constructed. Similarly, it requires skills for metacognition (Hyytine et al., 2019 ; Kuhn, 1999 ; Magno, 2010 ) since critical thinking “entails awareness of one’s own thinking and reflection on the thinking of self and others as objects of cognition” (Dean & Kuhn, 2003 , p. 3).

In science education, one of the most suitable scenarios or resources, but not the only one, Footnote 5 to address all these aspects of critical thinking is through the analysis of socioscientific issues (SSI) (Taylor et al., 2006 ; Zeidler & Nichols, 2009 ). Without wishing to expand on this here, I will only say that interesting works can be found in the literature that have analyzed how the discussion of SSIs can favor the development of critical thinking skills (see, e.g., López-Fernández et al., 2022 ; Solbes et al., 2018 ). For example, López-Fernández et al. ( 2022 ) focused their teaching-learning sequence on the following critical thinking skills: information analysis, argumentation, decision making, and communication of decisions. Even some authors add the nature of science (NOS) to this framework (i.e., SSI-NOS-critical thinking), as, for example, Yacoubian and Khishfe ( 2018 ) in order to develop critical thinking and how this can also favor the understanding of NOS (Yacoubian, 2020 ). In effect, as I argued in another work on the COVID-19 pandemic as an SSI, in which special emphasis was placed on critical thinking, an informed understanding of how science works would have helped the public understand why scientists were changing their criteria to face the pandemic in the light of new data and its reinterpretations, or that it was not possible to go faster to get an effective and secure medical treatment for the disease (García-Carmona, 2021b ).

In the recent literature, there have also been some proposals intended to characterize critical thinking in the context of science education. Table 3 presents two of these by way of example. As can be seen, both proposals share various components for the development of critical thinking (respect for evidence, critically analyzing/assessing the validity/reliability of information, adoption of independent opinions/decisions, participation, etc.), but that of Blanco et al. ( 2017 ) is more clearly contextualized in science education. Likewise, that of these authors includes some more aspects (or at least does so more explicitly), such as developing epistemological Footnote 6 knowledge of science (vision of science…) and on its interactions with technology, society, and environment (STSA relationships), and communication skills. Therefore, it offers a wider range of options for choosing critical thinking skills/processes to promote it in science classes. However, neither proposal refers to metacognitive skills, which are also essential for developing critical thinking (Kuhn, 1999 ).

3.1 Critical thinking vs. scientific thinking in science education: differences and similarities

In accordance with the above, it could be said that scientific thinking is nourished by critical thinking, especially when deciding between several possible interpretations and explanations of the same phenomenon since this generally takes place in a context of debate in the scientific community (Acevedo-Díaz & García-Carmona, 2017 ). Thus, the scientific attitude that is perhaps most clearly linked to critical thinking is the skepticism with which scientists tend to welcome new ideas (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ), especially if they are contrary to well-established scientific knowledge (Bell, 2009 ). A good example of this was the OPERA experiment (García-Carmona & Acevedo-Díaz, 2016a ), which initially seemed to find that neutrinos could move faster than the speed of light. This finding was supposed to invalidate Albert Einstein’s theory of relativity (the finding was later proved wrong). In response, Nobel laureate in physics Sheldon L. Glashow went so far as to state that:

the result obtained by the OPERA collaboration cannot be correct. If it were, we would have to give up so many things, it would be such a huge sacrifice... But if it is, I am officially announcing it: I will shout to Mother Nature: I’m giving up! And I will give up Physics. (BBVA Foundation, 2011 )

Indeed, scientific thinking is ultimately focused on getting evidence that may support an idea or explanation about a phenomenon, and consequently allow others that are less convincing or precise to be discarded. Therefore when, with the evidence available, science has more than one equally defensible position with respect to a problem, the investigation is considered inconclusive (Clouse, 2017 ). In certain cases, this gives rise to scientific controversies (Acevedo-Díaz & García-Carmona, 2017 ) which are not always resolved based exclusively on epistemic or rational factors (Elliott & McKaughan, 2014 ; Vallverdú, 2005 ). Hence, it is also necessary to integrate non-epistemic practices into the framework of scientific thinking (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ), practices that transcend the purely rational or cognitive processes, including, for example, those related to emotional or affective issues (Sinatra & Hofer, 2021 ). From an educational point of view, this suggests that for students to become more authentically immersed in the way of working or thinking scientifically, they should also learn to feel as scientists do when they carry out their work (Davidson et al., 2020 ). Davidson et al. ( 2020 ) call it epistemic affect , and they suggest that it could be approach in science classes by teaching students to manage their frustrations when they fail to achieve the expected results; Footnote 7 or, for example, to moderate their enthusiasm with favorable results in a scientific inquiry by activating a certain skepticism that encourages them to do more testing. And, as mentioned above, for some authors, having a skeptical attitude is one of the actions that best visualize the application of critical thinking in the framework of scientific thinking (Normand, 2008 ; Sagan, 1987 ; Tena-Sánchez and León-Medina, 2022 ).

On the other hand, critical thinking also draws on many of the skills or practices of scientific thinking, as discussed above. However, in contrast to scientific thinking, the coexistence of two or more defensible ideas is not, in principle, a problem for critical thinking since its purpose is not so much to invalidate some ideas or explanations with respect to others, but rather to provide the individual with the foundations on which to position themself with the idea/argument they find most defensible among several that are possible (Ennis, 2018 ). For example, science with its methods has managed to explain the greenhouse effect, the phenomenon of the tides, or the transmission mechanism of the coronavirus. For this, it had to discard other possible explanations as they were less valid in the investigations carried out. These are therefore issues resolved by the scientific community which create hardly any discussion at the present time. However, taking a position for or against the production of energy in nuclear power plants transcends the scope of scientific thinking since both positions are, in principle, equally defensible. Indeed, within the scientific community itself there are supporters and detractors of the two positions, based on the same scientific knowledge. Consequently, it is critical thinking, which requires the management of knowledge and scientific skills, a basic understanding of epistemic (rational or cognitive) and non-epistemic (social, ethical/moral, economic, psychological, cultural, ...) aspects of the nature of science, as well as metacognitive skills, which helps the individual forge a personal foundation on which to position themself in one place or another, or maintain an uncertain, undecided opinion.

In view of the above, one can summarize that scientific thinking and critical thinking are two different intellectual processes in terms of purpose, but are related symbiotically (i.e., one would make no sense without the other or both feed on each other) and that, in their performance, they share a fair number of features, actions, or mental skills. According to Cáceres et al. ( 2020 ) and Hyytine et al. ( 2019 ), the intellectual skills that are most clearly common to both types of thinking would be searching for relationships between evidence and explanations , as well as investigating and logical thinking to make inferences . To this common space, I would also add skills for metacognition in accordance with what has been discussed about both types of knowledge (Khun, 1999 , 2022 ).

In order to compile in a compact way all that has been argued so far, in Table 4 , I present my overview of the relationship between scientific thinking and critical thinking. I would like to point out that I do not intend to be extremely extensive in the compilation, in the sense that possibly more elements could be added in the different sections, but rather to represent above all the aspects that distinguish and share them, as well as the mutual enrichment (or symbiosis) between them.

4 A Proposal for the Integrated Development of Critical Thinking and Scientific Thinking in Science Classes

Once the differences, common aspects, and relationships between critical thinking and scientific thinking have been discussed, it would be relevant to establish some type of specific proposal to foster them in science classes. Table 5 includes a possible script to address various skills or processes of both types of thinking in an integrated manner. However, before giving guidance on how such skills/processes could be approached, I would like to clarify that while all of them could be dealt within the context of a single school activity, I will not do so in this way. First, because I think that it can give the impression that the proposal is only valid if it is applied all at once in a specific learning situation, which can also discourage science teachers from implementing it in class due to lack of time or training to do so. Second, I think it can be more interesting to conceive the proposal as a set of thinking skills or actions that can be dealt with throughout the different science contents, selecting only (if so decided) some of them, according to educational needs or characteristics of the learning situation posed in each case. Therefore, in the orientations for each point of the script or grouping of these, I will use different examples and/or contexts. Likewise, these orientations in the form of comments, although founded in the literature, should be considered only as possibilities to do so, among many others possible.

Motivation and predisposition to reflect and discuss (point i ) demands, on the one hand, that issues are chosen which are attractive for the students. This can be achieved, for example, by asking the students directly what current issues, related to science and its impact or repercussions, they would like to learn about, and then decide on which issue to focus on (García-Carmona, 2008 ). Or the teacher puts forward the issue directly in class, trying for it be current, to be present in the media, social networks, etc., or what they think may be of interest to their students based on their teaching experience. In this way, each student is encouraged to feel questioned or concerned as a citizen because of the issue that is going to be addressed (García-Carmona, 2008 ). Also of possible interest is the analysis of contemporary, as yet unresolved socioscientific affairs (Solbes et al., 2018 ), such as climate change, science and social justice, transgenic foods, homeopathy, and alcohol and drug use in society. But also, everyday questions can be investigated which demand a decision to be made, such as “What car to buy?” (Moreno-Fontiveros et al., 2022 ), or “How can we prevent the arrival of another pandemic?” (Ushola & Puig, 2023 ).

On the other hand, it is essential that the discussion about the chosen issue is planned through an instructional process that generates an environment conducive to reflection and debate, with a view to engaging the students’ participation in it. This can be achieved, for example, by setting up a role-play game (Blanco-López et al., 2017 ), especially if the issue is socioscientific, or by critical and reflective reading of advertisements with scientific content (Campanario et al., 2001 ) or of science-related news in the daily media (García-Carmona, 2014 , 2021a ; Guerrero-Márquez & García-Carmona, 2020 ; Oliveras et al., 2013 ), etc., for subsequent discussion—all this, in a collaborative learning setting and with a clear democratic spirit.

Respect for scientific evidence (point ii ) should be the indispensable condition in any analysis and discussion from the prisms of scientific and of critical thinking (Erduran, 2021 ). Although scientific knowledge may be impregnated with subjectivity during its construction and is revisable in the light of new evidence ( tentativeness of scientific knowledge), when it is accepted by the scientific community it is as objective as possible (García-Carmona & Acevedo-Díaz, 2016b ). Therefore, promoting trust and respect for scientific evidence should be one of the primary educational challenges to combating pseudoscientists and science deniers (Díaz & Cabrera, 2022 ), whose arguments are based on false beliefs and assumptions, anecdotes, and conspiracy theories (Normand, 2008 ). Nevertheless, it is no simple task to achieve the promotion or respect for scientific evidence (Fackler, 2021 ) since science deniers, for example, consider that science is unreliable because it is imperfect (McIntyre, 2021 ). Hence the need to promote a basic understanding of NOS (point iii ) as a fundamental pillar for the development of both scientific thinking and critical thinking. A good way to do this would be through explicit and reflective discussion about controversies from the history of science (Acevedo-Díaz & García-Carmona, 2017 ) or contemporary controversies (García-Carmona, 2021b ; García-Carmona & Acevedo-Díaz, 2016a ).

Also, with respect to point iii of the proposal, it is necessary to manage basic scientific knowledge in the development of scientific and critical thinking skills (Willingham, 2008 ). Without this, it will be impossible to develop a minimally serious and convincing argument on the issue being analyzed. For example, if one does not know the transmission mechanism of a certain disease, it is likely to be very difficult to understand or justify certain patterns of social behavior when faced with it. In general, possessing appropriate scientific knowledge on the issue in question helps to make the best interpretation of the data and evidence available on this issue (OECD, 2019 ).

The search for information from reliable sources, together with its analysis and interpretation (points iv to vi ), are essential practices both in purely scientific contexts (e.g., learning about the behavior of a given physical phenomenon from literature or through enquiry) and in the application of critical thinking (e.g., when one wishes to take a personal, but informed, position on a particular socio-scientific issue). With regard to determining the credibility of information with scientific content on the Internet, Osborne et al. ( 2022 ) propose, among other strategies, to check whether the source is free of conflicts of interest, i.e., whether or not it is biased by ideological, political or economic motives. Also, it should be checked whether the source and the author(s) of the information are sufficiently reputable.

Regarding the interpretation of data and evidence, several studies have shown the difficulties that students often have with this practice in the context of enquiry activities (e.g., Gobert et al., 2018 ; Kanari & Millar, 2004 ; Pols et al., 2021 ), or when analyzing science news in the press (Norris et al., 2003 ). It is also found that they have significant difficulties in choosing the most appropriate data to support their arguments in causal analyses (Kuhn & Modrek, 2022 ). However, it must be recognized that making interpretations or inferences from data is not a simple task; among other reasons, because their construction is influenced by multiple factors, both epistemic (prior knowledge, experimental designs, etc.) and non-epistemic (personal expectations, ideology, sociopolitical context, etc.), which means that such interpretations are not always the same for all scientists (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ). For this reason, the performance of this scientific practice constitutes one of the phases or processes that generate the most debate or discussion in a scientific community, as long as no consensus is reached. In order to improve the practice of making inferences among students, Kuhn and Lerman ( 2021 ) propose activities that help them develop their own epistemological norms to connect causally their statements with the available evidence.

Point vii refers, on the one hand, to an essential scientific practice: the elaboration of evidence-based scientific explanations which generally, in a reasoned way, account for the causality, properties, and/or behavior of the phenomena (Brigandt, 2016 ). In addition, point vii concerns the practice of argumentation . Unlike scientific explanations, argumentation tries to justify an idea, explanation, or position with the clear purpose of persuading those who defend other different ones (Osborne & Patterson, 2011 ). As noted above, the complexity of most socioscientific issues implies that they have no unique valid solution or response. Therefore, the content of the arguments used to defend one position or another are not always based solely on purely rational factors such as data and scientific evidence. Some authors defend the need to also deal with non-epistemic aspects of the nature of science when teaching it (García-Carmona, 2021a ; García-Carmona & Acevedo-Díaz, 2018 ) since many scientific and socioscientific controversies are resolved by different factors or go beyond just the epistemic (Vallverdú, 2005 ).

To defend an idea or position taken on an issue, it is not enough to have scientific evidence that supports it. It is also essential to have skills for the communication and discussion of ideas (point viii ). The history of science shows how the difficulties some scientists had in communicating their ideas scientifically led to those ideas not being accepted at the time. A good example for students to become aware of this is the historical case of Semmelweis and puerperal fever (Aragón-Méndez et al., 2019 ). Its reflective reading makes it possible to conclude that the proposal of this doctor that gynecologists disinfect their hands, when passing from one parturient to another to avoid contagions that provoked the fever, was rejected by the medical community not only for epistemic reasons, but also for the difficulties that he had to communicate his idea. The history of science also reveals that some scientific interpretations were imposed on others at certain historical moments due to the rhetorical skills of their proponents although none of the explanations would convincingly explain the phenomenon studied. An example is the case of the controversy between Pasteur and Liebig about the phenomenon of fermentation (García-Carmona & Acevedo-Díaz, 2017 ), whose reading and discussion in science class would also be recommended in this context of this critical and scientific thinking skill. With the COVID-19 pandemic, for example, the arguments of some charlatans in the media and on social networks managed to gain a certain influence in the population, even though scientifically they were muddled nonsense (García-Carmona, 2021b ). Therefore, the reflective reading of news on current SSIs such as this also constitutes a good resource for the same educational purpose. In general, according to Spektor-Levy et al. ( 2009 ), scientific communication skills should be addressed explicitly in class, in a progressive and continuous manner, including tasks of information seeking, reading, scientific writing, representation of information, and representation of the knowledge acquired.

Finally (point ix ), a good scientific/critical thinker must be aware of what they know, of what they have doubts about or do not know, to this end continuously practicing metacognitive exercises (Dean & Kuhn, 2003 ; Hyytine et al., 2019 ; Magno, 2010 ; Willingham, 2008 ). At the same time, they must recognize the weaknesses and strengths of the arguments of their peers in the debate in order to be self-critical if necessary, as well as to revising their own ideas and arguments to improve and reorient them, etc. ( self-regulation ). I see one of the keys of both scientific and critical thinking being the capacity or willingness to change one’s mind, without it being frowned upon. Indeed, quite the opposite since one assumes it to occur thanks to the arguments being enriched and more solidly founded. In other words, scientific and critical thinking and arrogance or haughtiness towards the rectification of ideas or opinions do not stick well together.

5 Final Remarks

For decades, scientific thinking and critical thinking have received particular attention from different disciplines such as psychology, philosophy, pedagogy, and specific areas of this last such as science education. The two types of knowledge represent intellectual processes whose development in students, and in society in general, is considered indispensable for the exercise of responsible citizenship in accord with the demands of today’s society (European Commission, 2006 , 2015 ; NRC, 2012 ; OECD, 2020 ). As has been shown however, the task of their conceptualization is complex, and teaching students to think scientifically and critically is a difficult educational challenge (Willingham, 2008 ).

Aware of this, and after many years dedicated to science education, I felt the need to organize my ideas regarding the aforementioned two types of thinking. In consulting the literature about these, I found that, in many publications, scientific thinking and critical thinking are presented or perceived as being interchangeable or indistinguishable; a conclusion also shared by Hyytine et al. ( 2019 ). Rarely have their differences, relationships, or common features been explicitly studied. So, I considered that it was a matter needing to be addressed because, in science education, the development of scientific thinking is an inherent objective, but, when critical thinking is added to the learning objectives, there arise more than reasonable doubts about when one or the other would be used, or both at the same time. The present work came about motivated by this, with the intention of making a particular contribution, but based on the relevant literature, to advance in the question raised. This converges in conceiving scientific thinking and critical thinking as two intellectual processes that overlap and feed into each other in many aspects but are different with respect to certain cognitive skills and in terms of their purpose. Thus, in the case of scientific thinking, the aim is to choose the best possible explanation of a phenomenon based on the available evidence, and it therefore involves the rejection of alternative explanatory proposals that are shown to be less coherent or convincing. Whereas, from the perspective of critical thinking, the purpose is to choose the most defensible idea/option among others that are also defensible, using both scientific and extra-scientific (i.e., moral, ethical, political, etc.) arguments. With this in mind, I have described a proposal to guide their development in the classroom, integrating them under a conception that I have called, metaphorically, a symbiotic relationship between two modes of thinking.

Critical thinking is mentioned literally in other of the curricular provisions’ subjects such as in Education in Civics and Ethical Values or in Geography and History (Royal Decree 217/2022).

García-Carmona ( 2021a ) conceives of them as activities that require the comprehensive application of procedural skills, cognitive and metacognitive processes, and both scientific knowledge and knowledge of the nature of scientific practice .

Kuhn ( 2021 ) argues that the relationship between scientific reasoning and metacognition is especially fostered by what she calls inhibitory control , which basically consists of breaking down the whole of a thought into parts in such a way that attention is inhibited on some of those parts to allow a focused examination of the intended mental content.

Specifically, Tena-Sánchez and León-Medina (2020) assume that critical thinking is at the basis of rational or scientific skepticism that leads to questioning any claim that does not have empirical support.

As discussed in the introduction, the inquiry-based approach is also considered conducive to addressing critical thinking in science education (Couso et al., 2020 ; NRC, 2012 ).

Epistemic skills should not be confused with epistemological knowledge (García-Carmona, 2021a ). The former refers to skills to construct, evaluate, and use knowledge, and the latter to understanding about the origin, nature, scope, and limits of scientific knowledge.

For this purpose, it can be very useful to address in class, with the help of the history and philosophy of science, that scientists get more wrong than right in their research, and that error is always an opportunity to learn (García-Carmona & Acevedo-Díaz, 2018 ).

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Constructivism as a Referent for Science Teaching

Introduction.

Why is it, in educational settings, we rarely talk about how students learn? Why aren't teachers using how students learn as a guide to their teaching practices? These questions seem almost too absurd to ask; but think, when was the last time you spoke to colleagues about how students learn? Do you observe learning in your classroom? What does it look like? These are a few of the questions that we have begun to ask ourselves and our teaching colleagues. One way to make sense of how students learn is through constructivism. Constructivism is a word used frequently by science educators lately. It is used increasingly as a theoretical rationale for research and teaching. Many current reform efforts also are associated with the notion of constructivism. But what exactly is constructivism and how can it be useful to the practicing teacher? Constructivism is an epistemology, a theory of knowledge used to explain how we know what we know. We believe that a constructivist epistemology is useful to teachers if used as a referent; that is, as a way to make sense of what they see, think, and do. Our research indicates that teacher's beliefs about how people learn (their personal epistemology), whether verbalized or not, often help them make sense of, and guide, their practice. The epistemology that is dominant in most educational settings today is similar to objectivism. That is to say, most researchers view knowledge as existing outside the bodies of cognizing beings, as beings separate from knowing and knowers. Knowledge is "out there," residing in books, independent of a thinking being. Science is then conceptualized as a search for truths, a means of discovering theories, laws, and principles associated with reality. Objectivity is a major component of the search for truths which underlie reality; learners are encouraged to view objects, events, and phenomenon with an objective mind, which is assumed to be separate from cognitive processes such as imagination, intuition, feelings, values, and beliefs (Johnson, 1987). As a result, teachers implement a curriculum to ensure that students cover relevant science content and have opportunities to learn truths which usually are documented in bulging textbooks. The constructivist epistemology asserts that the only tools available to a knower are the senses. It is only through seeing, hearing, touching, smelling, and tasting that an individual interacts with the environment. With these messages from the senses the individual builds a picture of the world. Therefore, constructivism asserts that knowledge resides in individuals; that knowledge cannot be transferred intact from the head of a teacher to the heads of students. The student tries to make sense of what is taught by trying to fit it with his/her experience. Consequently, words are not containers whose meanings are in the words itself, they are based on the constructions of individuals. We can communicate because individual's meanings of words only have to be compatible with the meanings given by others. If a situation occurred in which your meaning of a word no longer sufficed, you could change the meaning of the word. Using constructivism as a referent, teachers often use problem-solving as a learning strategy; where learning is defined as adaptations made to fit the world they experience. That is, to learn, a person's existing conceptions of the world must be unreliable, inviable. When one's conceptions of the world are inviable one tries to make sense out of the situation based on what is already known (i.e. Prior knowledge is used to make sense of data perceived by the senses). Other persons are part of our experiential world, thus, others are important for meaning making. "Others" are so important for constructivists that cooperative learning is a primary teaching strategy. A cooperative learning strategy allows individuals to test the fit of their experiential world with a community of others. Others help to constrain our thinking. The interactions with others cause perturbations, and by resolving the perturbations individuals make adaptations to fit their new experiential world. Experience involves an interaction of an individual with events, objects, or phenomenon in the universe; an interaction of the senses with things, a personal construction which fits some of the external reality but does not provide a match. The senses are not conduits to the external world through which truths are conducted into the body. Objectivity is not possible for thinking beings. Accordingly, knowledge is a construction of how the world works, one that is viable in the sense that it allows an individual to pursue particular goals. Thus, from a constructivist perspective, science is not the search for truth. It is a process that assists us to make sense of our world. Using a constructivist perspective, teaching science becomes more like the science that scientists do it is an active, social process of making sense of experiences, as opposed to what we now call "school science." Indeed, actively engaging students in science (we have all heard the call for "hands-on, minds-on science") is the goal of most science education reform. It is an admirable goal, and using constructivism as a referent can possibly assist in reaching that goal. Driver (1989) has used a constructivist epistemology as a referent in her research on children's conceptions of science. Children's prior knowledge of phenomena from a scientific point of view differs from the interpretation children construct; children construct meanings that fit their experience and expectations. This can lead children to oftentimes construct meanings different from what was intended by a teacher. Teachers that make sense of teaching from an objectivist perspective fail to recognize that students solve this cognitive conflict by separating school science from their own life experiences. In other words, students distinguish between scientific explanations and their "real world" explanations (the often cited example-that forces are needed to keep a ball in motion versus Newton's explanation is one such example). Children's conceptions are their constructions of reality, ones that are viable in the sense that they allow a child to make sense of his/her environment. By using a constructivist epistemology as a referent teachers can become more sensitive to children's prior knowledge and the processes by which they make sense of phenomena. The teaching practices of two teachers at City Middle School may best illustrate how practice can be influenced by making sense of teaching and learning from constructivist-and objectivist-oriented perspective. To Bob, science was a body of knowledge to be learned. His job was to "give out" what he (and the textbook) knew about science to his students. Thus the learning environment Bob tried to maintain in his classroom facilitated this transfer of knowledge; the desks were neatly in rows facing Bob and the blackboard. Lectures and assignments from the text were given to students. Bob tried to keep students quiet and working all during the class period to ensure that all students could "absorb" the science knowledge efficiently. Another consequence of Bob's notion of teaching and learning was his belief that he had so much cover that he had no time for laboratory activities. Let's look at an example that typifies Bob's teaching style. Bob's sixth grade students were to complete a worksheet that "covered" the concept of friction. After the students completed the worksheet, Bob went over the answers so the students could have the correct answers for the test later in the week. From a constructivist perspective, what opportunities did Bob's students have to relate the concept of friction to their own experiences? Were these opportunities in Bob's lesson plan to negotiate meanings and build a consensus of understanding? Bob spent one class period covering the concept of friction; is that sufficient time for students to learn a concept with understanding? On the other hand, John made sense of teaching and learning from a constructivist perspective. John's classes were student-centered and activity-based. Typically in his high school classes, John introduced students to different science topics with short lectures, textbook readings, and confirmatory laboratories. After the introduction John would ask students what interested them about the topic and encouraged them to pursue and test these ideas. Students usually divided themselves into groups and then, conducted a library research, formulated questions/problems, and procedures to test the questions/problems. In other words, the students were acting as scientists in the classroom. Like Bob, John taught a sixth grade class previously, and also taught students about friction. Included in John's lessons were activities to "get the students involved." Students rubbed their hands together with and without a lubricant so that they could see the purpose of motor oil in engines. The students conducted experiments with bricks to learn about different types of friction, and even watched The Flintstones in class to point out friction and what would really happen (i.e. Fed would burn his feet stopping the car, etc.) John spent two weeks teaching his unit on friction. Were John's students given opportunities to make sense of the concept of friction? Were they able to use personal experiences? Whose students do you think had a deeper understanding of friction? Our research also indicates that as teachers made transitions from objectivist to constructivist oriented thoughts and behaviors their classroom practices changed radically (Lorsbach, Tobin, Briscoe, & LaMaster, In Press, Tobin, 1990). It seemed as if many traditional practices no longer made sense to teachers. Specifically, teachers recognized that learning and making sense of what happens rests ultimately with the individual learners. Learners need time to experience, reflect on their experiences in relation to what they already know, and resolve any problems that arise. Accordingly, learners need time to clarify, elaborate, describe, compare, negotiate, and reach consensus on what specific experiences mean to them. This learning process must occur within the bodies of individuals, however, the inner voices of persons can be supplemented by discussion with others. Therefore, an important part of a constructivist oriented curriculum should be the negotiation of meaning. Students need to be given opportunities to make sense of what is learned by negotiating meaning; comparing what is known to new experiences, and resolving discrepancies between what is known to new experiences, and resolving discrepancies between what is known and what seems to be implied by new experience. The resolution of discrepancies enables an individual to reach an equilibrium in the sense that there should be no remaining curiosity regarding an experience in relation to what is known. Negotiation also can occur between individuals in a classroom. The process involves discussion and attentive listening, making sense of the points of views of theories of peers. When a person understands how a peer is making sense of a point of view, it is then possible to discuss similarities and differences between the theories of peers within a group. Justifying one position over another and selecting those theories that are viable can lead to consensuses that are understood by those within a peer group. The process of learning should not stop at what has been learned in the negotiation of a class consensus. This process can involve accessing other learning resources such as books, videotapes, and practicing scientists. The consensus negotiated within a class can be adapted by students as they make sense of the theories negotiated in other communities. By engaging in such a process students can realize that what is regarded as a viable theory depends on what is known at the time and the context in which the theory is to be applied. Also they can begin to understand how to select the best theoretical formulation for use in a particular set of circumstances. For many years the conventional wisdom of teachers has been similar to Bob's teaching style: to control student behavior so that the class is quiet. Indeed research programs have been premised on this assumption. Accordingly, the research literature provides lists of teacher behavior and strategies that have been demonstrated to control students. If this assumption is abandoned there is little research to guide teachers in the selection of practices that are conducive to students constructing knowledge. Instead of managing to keep students quiet and attentive to the teacher, a classroom might be managed to enable students to talk with one another and utilize collaborative learning strategies. Instead of keeping students seated in rows throughout a lesson, a management system might be developed which permits students to move about the classroom and visit the library, or a field work station. Management is still a priority, but it is subsumed below learning and the implementation of a curriculum that meets the needs of students. Establishing and maintaining a learning environment that is conducive to learning is a priority for science teachers. However, this is not easy to do. To begin with, traditional teaching practices are sometimes difficult to discard. Teachers might commence a lesson with good intentions only to find that they forget to follow their game plan. We have learned from our research that sustained change can take a long time to establish. John, a third year teacher, is committed to get all of his students to accept his style of teaching. Many of his students have an image of teaching of which John's style does not fit. Therefore, students might also have difficulty adapting to an environment in which they are given the responsibility for making sense of science. They too have experienced traditional practices in which they are force fed a diet of factual information to be rote learned. Many students expect to be controlled and filled up with knowledge. They believe teachers to be the experts whose role is to transfer the knowledge to students, much like one fills a bottle with liquid. If teachers do not fulfill their traditional roles students might be confused and have difficulty engaging as intended by the teacher. Just as teachers have to learn how to teach from a constructivist point of view, so too must students learn how to learn. Educating students to be effective learners is an important priority in establishing environments conducive to effective learning of science. Reflect on your science teaching. Have you provided students with new knowledge to be memorized and repeated on a test without providing an opportunity for them to make sense of it? Or, have you provided students with an opportunity to use their prior knowledge and senses in making connections to the new concepts you introduced? If, like so many traditional science classrooms, the practices in your classroom are based on objectivism, you might like to commence the challenge of implementing change that accord with constructivism. If you would like to change your teaching practices (to whatever degree), then perhaps by reflecting on your practice from a constructivist point of view you can begin to construct a new vision of your classroom.

by Anthony Lorsbach, Department of Teacher Education, Bradley University, Peoria, IL 61625 and Kenneth Tobin, Science Education Program, The Florida State University, Tallahassee, FL 32306

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National Academies Press: OpenBook

Learning Through Citizen Science: Enhancing Opportunities by Design (2018)

Chapter: 4 processes of learning and learning in science, 4 processes of learning and learning in science, introduction.

Understanding both the depth and breadth of scholarship on learning is central to addressing the committee’s charge of investigating how citizen science can be poised to support science learning. In this chapter, we review the complex landscape of scholarship on learning in a way that highlights concepts relevant to the design of citizen science for learning. The concepts lay the groundwork for Chapter 5 , which delves into how citizen science can advance specific science learning outcomes. We begin with an explanation of the committee’s perspective on learning in the context of the history and evolution of learning theories. This discussion will set the stage for a description of some of the central cognitive processes involved in learning generally. We conclude the chapter with descriptions of some of the specific kinds of learning that happen in science content domains.

Although we describe the different theoretical perspectives on how learning occurs, contemporary scholars of learning generally recognize that learning is a complicated, interactive phenomenon. Individuals are nested within communities that are nested within societies, and these contexts matter for how knowledge is acquired and engaged. Different theories of learning are not mutually exclusive and can be used in complementary ways to attend to the multifaceted nature of learning, even in a single environment such as a citizen science project. Moreover, participants in citizen science project are also learning in a wide variety of other contexts and may even participate in multiple citizen science projects. It is helpful in both design of citizen science projects and in research about learning to

remember that all learning is happening with a larger ecosystem of citizen science opportunities and other science education experiences, both formal and informal.

This chapter is not intended as a comprehensive review of scholarship on learning; rather, we attempt to lay out central principles of learning, particularly with respect to science, for readers new to the field of science learning.

PERSPECTIVES ON LEARNING

The committee has elected to take an expansive view of learning in general and science learning more specifically: Both what the learning is and the many contextual factors that influence it. Historically, most learning research focuses on individuals, and as we discussed in our section on community science literacy in Chapter 3 , many research literatures and theoretical perspectives (including developmental, social, organizational, and cultural psychology; cognitive science, neuroscience, and the learning sciences; and education) have endeavored to construct frameworks for understanding and facilitating learning in individuals. As we discuss the processes of learning (both in general and in science) later in this chapter, the committee recognizes that these processes are aimed at characterizing what the individual learner knows and is able to do.

Over the past few decades, the study of human learning and development has moved beyond the examination of individual characteristics to understand learning as dependent on sociocultural contexts, even when examining a single individual’s learning. In order to explain why and how people think and act in the world the way they do, scholars employing sociocultural perspectives often study and characterize how people in places interact with each other toward goals and use materials to mediate and support their interactions and goals.

From a sociocultural perspective, culture, learning, and development are seen as dynamic, contested, and variably distributed and transformed within and across groups, and involve a reciprocal and evolving relationship between individuals’ goals, perspectives, values, and their environment ( Cole, 2000 ; Gutiérrez and Rogoff, 2003 ; Hirschfeld, 2002 ; Lave, 1988 ; Lave and Wenger, 1991 ; Nasir and Hand, 2006 ; Rogoff, 2003 ). Culture, in this sense, is both historically constituted and dynamically changing through participation in social practices and making sense of life. More simply put, all people explore, narrate, and build knowledge about their worlds, but they do so in varied ways that are dynamically linked to particular contexts and depend on interaction with others (e.g., Bang et al., 2012 ; National Research Council, 2009 ; Rogoff, 2003 ).

While there remain important distinctions between individual and sociocultural perspectives, it is increasingly accepted that what and how

people think are interdependent, and that both are sculpted by the daily activities, discursive practices, participation structures, and interactional processes over the course of a person’s life. Sociocultural perspectives have expanded our foundational knowledge of human learning as well as led to important practice-based innovations in learning environments. While we acknowledge that much of the research on specific processes of learning mentioned in this chapter are concerned with individual learners, the committee believes that given the explicitly social nature of many citizen science projects, it is critically important to consider learning in citizen science through a sociocultural lens.

Given this perspective, the committee wishes to highlight three major principles of learning that undergird our discussion of how learning happens—both in science and in general. First, as we discussed in Chapter 3 : Learners come to their learning experiences with prior knowledge experiences that shape what they know, their skills, their interests, and their motivation. Constructivist frameworks explain how this prior knowledge and experience matter for learning, positing that learning involves an interplay of the learner’s prior knowledge and current ways of thinking with new ideas introduced by instruction or through interactions in the world (e.g., Piaget, Carey, Vosniadou, Chi, Posner, et al.) Second, learners actively construct their own understanding of the world; they are not passive recipients of knowledge, and transmitting knowledge is not equivalent to learning. Later in this chapter, we will discuss this principle in relationship to conceptual development, and how educators must actively engage learners in the process of developing conceptual understandings of science. Finally, some learning objectives in science are more challenging to achieve than others, so more intentional supports for learning are necessary. We will discuss this in the context of citizen science in Chapter 5 , as we review how the existing literature describes different learning outcomes in citizen science.

In summary, the committee recognizes that learning is inherently social. It is situated in, and dependent upon, social interactions among people as well as their social and cultural tools and practices. In the following discussions of learning processes and kinds of learning in science, the committee emphasizes this sociocultural perspective on learning while also considering the insights gained from many decades of research from other theoretical perspectives.

We begin our discussion of learning by considering the processes of learning in individuals; specifically, the processes of memory, activity, and developing expertise. Then, the chapter narrows in on the specifics of science learning, including learning disciplinary content; using scientific tools; understanding and working with data; developing motivation, interest, and identity; and developing scientific reasoning, epistemological thinking, and the nature of science.

PROCESSES OF LEARNING

This section considers the dominant cognitive processes that contribute to learning—that is, those processes that can be understood at the level of the individual and relate to content knowledge and reasoning. Because the charge of this study is specific to science learning, wherever possible the committee elects to discuss how these learning processes happen in the context of the domain of science. It is critical to note that these processes are not unique to science learning. Indeed, much of the general scholarship on learning has emerged in relationship to other academic disciplines, each with their own scholarly research traditions.

The Role of Memory in Learning

Learning depends fundamentally on memory. Well over a century of research has delved into the properties of human memory in action, detailing the remarkable role memory plays in both developing and sustaining learning over time. From this research, there are several themes that are helpful to keep in mind.

Durable, long-term learning is best accomplished by repeated experience with the material one seeks to remember. Many researchers of memory and learning would caution against relying on a training program that involves a one-time introduction and immediate assessment of proficiency, which tends to result in short-term performance that predictably deteriorates over time, rather than long-term learning ( Soderstrom and Bjork, 2015 ). Further, learning episodes are most efficient when they are spread out over multiple sessions rather than crammed together—a phenomenon known as the spacing effect ( Cepeda et al., 2006 ; Rawson and Dunlosky, 2011 ). That is, the same amount of time invested in studying material one wants to remember will generally result in longer-lasting learning if it is distributed over time rather than performed all at once.

Learning can be enhanced by strategies that promote cognitive engagement with and elaboration of the material one is attempting to learn. Knowledge and skills that are densely interconnected to other information have better storage strength in long-term memory and also have links to more potential retrieval cues. Examples of beneficial strategies include such activities as concept mapping, note-taking, self-explanation, and representing material in multiple formats (e.g., text and graphics). Learning researchers Michelene Chi and Ruth Wylie (2014) have proposed a framework that differentiates cognitive engagement during learning into four modes: interactive, constructive, active, and passive (presented in decreasing order of the intensity of engagement), with interactive and constructive modes having the greatest impact on learning and conceptual development.

Constructive engagement is defined as activities where learners generate some kind of additional externalized product beyond the information they were originally provided with, such as generating inferences and explanations or constructing a new representational format (e.g., a diagram). Interactive engagement goes one step further and occurs when two or more partners (peers, teacher and learner, or intelligent computer agent and learner) together contribute to a mutual dialogue in a constructive mode.

Learning is improved when people are asked to actively apply or construct material from long-term memory, as opposed to passively restudying or being re-told the content, a phenomenon known as the “testing effect” ( Karpicke and Blunt, 2011 ; Karpicke and Roediger, 2008 ; Rowland, 2014 ). Providing regular opportunities to generate active responses, such as through informal assessments or practice in the field, helps learners reinforce their learning while at the same time providing information about current states of proficiency. As these examples suggest, corrective feedback is another tool that can help to promote accurate learning and reinforce retention over time ( Lyster and Ranta, 1997 ).

Learning opportunities that are deliberately designed with these principles of learning and memory in mind often show significant learning gains over traditional instructional practices such as lecture and rote memorization or self-organized learning ( Bjork and Bjork, 2011 ; Bjork, Dunlosky, and Kornell, 2013 ). Although it was developed primarily to improve studying and instructional practices in school learning, the IES Practice Guide on Organizing Instruction and Study to Improve Student Learning (Pashler et al., 2007) provides a concise summary of these and several other principles of learning that are supported by substantial bodies of research and are relevant across learning contexts (see Box 4-1 ).

In Chapter 6 , we will discuss the choices that project designers need to make in order to support science learning in citizen science. As with the all the processes of learning described below, designers of citizen science projects can leverage the role of memory in learning to support specific science learning outcomes.

The Importance of Activity

As noted above, human thinking, learning, and behavior is fundamentally shaped by the need to engage in purposeful activity within social systems involving other people. As active agents, humans engage with the objective world in ways that infuse it with meaning. Activity theory (e.g., Engestrom, Miettinen, and Punamaki, 1999 ) takes a systems approach, treating as the unit of analysis a community of interacting individuals, such as a team or an organization, who have a common object of their activity.

For example, members of a team of health care providers in a hospital are the individual subjects in a community and their patients are the objects.

Activity systems are characterized by rules and conventions, which evolve historically and culturally, as well as divisions of labor and participation structures, which may include social strata or a hierarchical structure to the activity, with different actors taking on distinctive roles. A key insight of activity theory is that “tools,” which may be culturally created artifacts

or concepts (e.g., machines, software interfaces, information systems, protocols, etc.) that evolve over time, mediate behavior in the system, including learning and transmitting knowledge ( Jonassen and Rohrer-Murphy, 1999 ).

Individuals may participate in multiple activity systems, and more recent work on activity theory has brought out the importance of considering interactions among multiple activity systems, which raises issues of individual and cultural identity, power, motivation, and difference ( Bakhurst, 2009 ; Gutiérrez and Rogoff, 2003 ) and also points back to the need to consider citizen science learning in the context of a larger ecosystem of learning experiences. Activity systems are often used as a way of modeling practice in various contexts, including educational practice, in such a way that systems-level relations and dynamics are highlighted. In the context of citizen science, activity theory offers ways to think about the complex set of roles, objectives, values, and activities that can emerge when volunteer participants are simultaneously members of other communities, such as master naturalists and conservationists, community activists, hobbyists, students or teachers in formal or informal education, or workers engaged in related economic activity (e.g., fishing or harvesting). Actors may come from distinctly different groups, each with its own set of objectives, tools, customs, discourse patterns, role structures, and ways of doing things. Activity theory suggests that participants and organizers may advance collaborative goals by paying deliberate attention to recognizing or designing appropriate role structures, shared tools, and systems of communication to take advantage of the resources that different activity systems can potentially contribute while promoting common action and understanding.

Another example that lends itself to an activity systems analysis comes from Ottinger (2016) , who presents the case of a multisite study and report completed by a coalition of environmental and community groups working in parallel with credentialed scientists ( Coming Clean and Global Community Monitor, 2014 ). The study entails the development and deployment of modified instruments and protocols for sampling air quality in ways that were scientifically credible but more affordable and responsive to the concerns and questions of community groups. They allowed project participants to collect data at time intervals and in locations associated with community health concerns, and they provided data that pushed beyond prior standards that focused primarily on long-term averages. Ottinger’s account also illustrates the tensions and interplay among the roles taken by community activists, scientists, and regulatory authorities around issues such as authorship and dissemination of reports, setting standards, and critiquing standard scientific practices vs. aligning with them for the sake of credibility. In summary, activity theory provides a way of identifying, analyzing, and modifying the elements—such as communities, actors and roles, objects of activity, tools, and practices—that both mediate and represent learning.

Developing Expertise

Competence in any domain, and specifically in science, requires the ability to recognize relevance and potential applications of knowledge in varying contexts. While individuals new to the field (known as novices) tend to focus on superficial aspects of a situation and may have correspondingly shallow problem solving methods, experts quickly and accurately perceive higher-order relations, deep structure, and meaningful patterns ( Chi, Feltovich and Glaser, 1981 ; Kellman and Massey, 2013 ). Experts tend to be fast and accurate, in large part because they process available information selectively—ignoring information that is irrelevant and registering information that is not noticed by novices. They are also better able to make fine discriminations and to apply their knowledge to novel cases. Experts are particularly good at recognizing conditions of application of knowledge—that is, knowing which principles and concepts are relevant in a particular situation ( Chi, Feltovich and Glaser, 1981 ; Kellman and Garrigan, 2009 ).

In this subsection, we discuss the role of conceptual change and perceptual learning in the development of expertise. It is important to note that in science, development of expertise hinges on the ability to utilize scientific tools and practices. We discuss this particular aspect of developing expertise—using scientific tools and participating in science practices—later in this chapter, where we discuss specific kinds of learning in science.

Conceptual Change and Development

One way of understanding how people develop expertise in content areas—specifically in the domain of science—explores the evolution of foundational ideas from the perspective of conceptual development over time. Theorists of conceptual development have noted repeatedly that mature concepts are often qualitatively different from concepts held by children or by uninstructed adults ( Duit and Treagust, 2003 ; National Research Council, 2007 ). Acquiring sophisticated understanding of concepts is not merely a matter of accumulating more factual knowledge.

A common idea in theories of conceptual development is that concept learning varies in the degree to which knowledge must be restructured to move from naïve to more expert understanding. Some early understandings can be readily nurtured in thoughtful learning settings ( Gelman et al., 2010 ). On the other hand, strong restructuring is required when novice and expert conceptual structures are fundamentally incompatible or incommensurate ( Carey, 1988 ). In this case, rather than refining individual concepts or adding new concepts to existing ones, the nature of the concepts themselves and the explanatory structures in which they are embedded undergo change. Chi and her colleagues ( Chi, Slotta, and de Leeuw, 1994 ) argue

that some science learning is particularly difficult because learners’ initial conceptions belong to a different ontological category than corresponding scientific conceptions. For example, many novices think of heat, gravity, and force as types of material substances, or properties of matter, rather than interactive processes. This can lead learners to misconstrue instruction, as happens when a learner who thinks of electrical current as similar to flowing water draws on matter-based conceptions, like volume or mass, to try to understand electrical phenomena.

The degree to which scientific concepts displace naïve knowledge during the process of strong restructuring is a subject of much debate. Strike and Posner (1982) show how conceptual change can occur when a learner begins to be sufficiently dissatisfied with a prior conception (e.g., by being confronted with anomalous information) and comes to see a new alternative conception as intelligible, plausible, and fruitful in its ability to explain and understand other problems. However, a number of studies indicate that intuitive ideas are also persistent and learners may ignore, reject or distort anomalous information. Even experts do this, as is illustrated by the history of science ( Chinn and Brewer, 1993 ). Further, intuitive beliefs and alternative frameworks can continue to be activated in particular contexts even after an individual shows evidence of understanding and using a scientific concept.

Importantly, people can hold multiple conceptions about phenomena as they engage in rapid reorganization of knowledge and respond to the demands of a particular context. Even experts will shift their reasoning and understanding about a phenomenon depending upon the context (e.g., Hogan and Maglienti, 2001 ). When confronted with novel activities or practices, learners may need to create their own alternative pathways to reconcile conflicting cultural, ethnic, and academic identities ( Nasir and Saxe, 2003 ).

Learning environments that only see learners’ alternative conceptions as wrong can produce conflicts between learners’ cultural, ethnic, and academic identities ( Nasir and Saxe, 2003 ), and this approach can also leave narrow the possibilities of generative engagements between community ways of knowing and scientific ways of knowing (e.g., Bang and Medin, 2010 ). Instead, research shows that many phenomena of interest in scientific study are intimately related to people’s everyday experiences and knowledge systems of cultural communities historically underrepresented in science can, and should, be regarded as assets for learning ( Cajate, 1999 ; National Research Council, 2007 ). Educators can do this in a variety of ways. The use of culturally relevant examples, analogies, artifacts, and community resources that are familiar to learners can make science more relevant and understandable ( Barba, 1993 ), and integrated approaches that rely on the input of community member participation (e.g., input

from elders, use of traditional language, respect of cultural values) help learners navigate between Western modern scientific thinking and other ways of knowing ( Bang and Medin, 2010 ). Sconiers and Rosiek (2000) point out that science inquiry demands patience, skepticism, and a willingness to embrace uncertainty and ambiguity—which demands trust between teachers and students. Accordingly, the development of trust and caring relationships between teachers and students may be necessary in order to develop deep understandings of science content and practices. In short, research demonstrates that conceptual learning is advanced in contexts and with instructors that recognize learners are simultaneously developing expertise in multiple knowledge systems ( Bang and Medin, 2010 ; Levine Rose and Calabrese Barton, 2012 ).

Perceptual Learning

Another process by which people develop domain expertise is perceptual learning, defined as an increase in the ability to extract relevant information from the environment as a result of experience ( Adolph and Kretch, 2015 ; Gibson, 1969 ). Perceptual learning happens at all ages from infancy through mature adulthood, and has been studied in many professional and academic domains, including medical learning, aviation, mathematics, and chemistry, as well as in everyday learning ( Kellman and Massey, 2013 ). Perceptual learning is often implicit and can be seen as a fundamental complement to more familiar ways of knowing, such as factual and procedural knowledge. Common instructional techniques emphasizing explicit didactic instruction or procedural practice typically do not advance perceptual learning very effectively ( Kellman and Massey, 2013 ). Instead, perceptual learning often results from extended experiences with many examples as individuals participate in a meaningful activity. Recent research demonstrates that perceptual learning can be accelerated by providing systematic opportunities for learners to practice making relevant discriminations and classifications with feedback ( Kellman, Massey, and Son, 2010 ). Learning software is an efficient and cost-effective way to do this. However, it is important for learners to experience a full range of variation in the examples they work with, so that the critical features, patterns, and structures involved in the activity are observed repeatedly across many different situations. Deliberate training tutorials can also ensure that participants have sufficient exposure to unusual or rare cases or difficult discriminations that they might not otherwise encounter often enough to gain proficiency. This kind of repeated classification activity across a range of examples is a central feature of many citizen science projects, like Zooniverse or COASST, suggesting that citizen science projects may be a particularly rich venue for perceptual learning.

Although the term “perceptual” may give the impression that it applies only to simple sensory tasks and discriminations, recent work drawing on modern theories of perception emphasizes that perceptual learning is abstract and adaptive, working synergistically with other cognitive processes ( Kellman and Massey, 2013 ). Rather than conceiving of learning as the acquisition of discrete mental contents, the focus is on how human minds attune themselves to meaningful patterns, relations, and structures in the environment, typically in the context of a purposeful task or activity ( Bereiter and Scardamalia, 1996 ; Goldstone, Landy, and Son, 2010 ). In addition to enabling the selective pick up of information in natural settings, as when a geologist effortlessly sees complex structure and patterns in natural rock formations, it also applies to processing of image representations, such as medical images read by a radiologist, and to symbolic representations, such as equations perceived by a mathematician or chemical formula notations read by a chemist. (Indeed, fluent reading in everyday life relies heavily on automatic information pick up obtained through perceptual learning).

Other approaches to the development of expertise have also emphasized how gaining experience in a domain or sphere of activity changes how one “sees.” Working from an anthropological perspective and drawing on activity theory, Goodwin (1994) introduced the term “professional vision” to describe how members of a professional community engage in discursive practices that shape how they perceive relevant entities and phenomena. Goodwin’s concept of professional vision focuses on practices within professions that create and operate on highly mediated representations of experience. For example, professional practices may highlight specific phenomena in a complex scene to make them salient, and they may apply verbal codes to classify phenomena and relate them to each other in an articulated framework. Professionals also produce shared material representations, such as graphs, charts, images, and annotated records. For example, teams of archeologists excavating a site use shared procedures to create profile maps of dirt that capture spatial relations among distinctive layers. Novices typically gain experience with these practices and tools as apprentices and, over time, develop the professional vision characteristic of their profession.

Similarly, Stevens and Hall (1998) , has introduced the term “disciplined perception” to describe forms of visual interaction that develop among people as they engage in practice or in teaching and learning in a discipline such as mathematics. People create, coordinate, and behaviorally interact with aspects of visual displays to make objects or conditions of interest visible to themselves and to each other. For example, a student working with a tutor on graphs of linear functions develops a set of visual practices specific to the graphing of points and lines on grids representing the Cartesian plane. In Stevens’ analysis, embodied action (e.g., gesture), visual perception, and

talk work together in specific and coordinated ways throughout the teaching and learning process, both enabling and constraining the understanding that the student develops.

KINDS OF LEARNING IN SCIENCE

This section focuses on the kinds of learning in science: learning disciplinary content; using scientific tools; understanding and working with data; developing motivation, interest, and identity; and developing scientific reasoning, epistemological thinking, and an understanding of the nature of science. Throughout this section, we refer back to the strands of informal science learning outlined in Chapter 3 to provide a framework for understanding the outcomes that result from these different kinds of learning in science. As emphasized in that chapter, we note that focusing on strands in insolation is an analytic convenience to help understand science learning; in practice strands are inextricably interwoven and projects that effectively advance science learning outcomes often advance and connect multiple strands. In the next chapter, we see examples of these kinds of learning in the context of citizen science.

Learning Specific Scientific Disciplinary Content

Learning science content and developing expertise in a scientific discipline involve several types of knowledge, which are acquired through multiple learning processes. Following standard practice, we refer to this kind of learning as “developing expertise in a scientific content area” or “science content learning.” Science content learning may be a stand-alone goal of the project and/or it may be part of achieving other scientific or community goals. With respect to the Learning Science in Informal Environments: People, Places, and Pursuits ( LSIE ; National Research Council, 2009 ) strands, science content learning is most closely related to understanding scientific content and knowledge (Strand 2) and using the tools and language of science (Strand 5).

The learning processes that help develop specific disciplinary knowledge and associated competencies, which can be quite sophisticated, go well beyond simple rote memorization of facts. Although the acquisition of specific knowledge is sometimes contrasted with conceptual understanding and the two are treated as if they are competing learning priorities, evidence shows that they play complementary and mutually supportive roles in learning. Specific knowledge and skills that are not incorporated into coherent conceptual organizations tend to exist as isolated “factoids”—difficult to remember, recognize in context, or apply in a productive way. At the same time, a rich foundation of specific knowledge animates abstract

concepts and provides accessible, meaningful instantiations of important relations and patterns.

Expertise in specific disciplinary content requires declarative knowledge—concepts that can be verbalized. This kind of learning is sometimes described as “knowing that.” Declarative knowledge can be thought of as facts that can be reliably and accurately retrieved and applied. A budding geologist, for instance, must learn the names and composition of different types of rocks and minerals and the processes by which they are formed. A volunteer monitoring invasive or endangered species must learn their typical habitats and the properties by which each type is identified. However, as described above in the section on conceptual change, a rich body of factual knowledge is not simply an accumulation of independent facts.

To be functional, science content knowledge must be organized and integrated through conceptual frameworks that provide coherence and explanatory power. Facility in this arena supports the evolution of learners’ relationships to foundational ideas that have broad importance for conceptual development over time. As discussed above, theorists of conceptual development in science learning have noted repeatedly that mature science concepts are often qualitatively different from concepts held by children or by uninstructed adults.

One strong example of how this conceptual change can play out in science domains can be observed through the implementation of A Framework for K–12 Science Education’s core disciplinary ideas, which aim to focus science learning around fewer science topics but to develop them in more depth across multiple years while simultaneously integrating them with science practices, described in the following sections ( National Research Council, 2012 ). The NGSS Framework lays out a small, focused set of core disciplinary ideas in the physical sciences, life sciences, earth and space sciences, engineering, technology, and applications of science. Box 4-2 presents an example of how core disciplinary ideas in life sciences can set the stage for learners’ conceptual change over time.

Not only are specific knowledge and conceptual understanding mutually supportive but also they are both situated in existing knowledge and understanding that learners bring into their experience in citizen science. It can be tempting to think of developing conceptual understanding and specific knowledge as an almost remedial process, where learners enter projects with a deficit and project activities fill that deficit. It is important to note that this approach can undermine other sources of knowledge and other ways of knowing, alienate learners, and impede learning. Learners enter projects with a variety of relevant prior knowledge and experience, some of it cultural, and the research shows that providing opportunities to connect new knowledge and emerging understandings with previous knowledge and experience advances learning.

Using Scientific Tools and Participating in Science Practices

Another way that science learning occurs is by using scientific tools and methods to engage in scientific reasoning (Strand 3) and to engage in scientific practices and discourse (Strand 5). Gaining competence with the scientific tools and practices related to a given content domain is known as procedural knowledge , sometimes described as “knowing how.” In science, “knowing how” enables one to perform procedures and tasks in the service of scientific protocols. This competency might involve developing laboratory skills, measurement techniques, field methods, or analytic skills, such as how to organize, analyze, and present data. While procedural knowledge is sometimes condensed into a fixed set of rote behaviors—and there is certainly scientific value in maintaining consistent methods and protocols—functional competence and active problem solving in science typically require adaptability and flexibility in application, which in turn requires a deeper understanding of why procedures and practices take the form that they do and what the implications of contextual variations might be. It is important to note that the use of tools and scientific practices is strongly influenced by cultural and social norms (e.g., what is a valid practice, how tools are judged) and the interaction of groups. Indeed, learning is mediated through the tools, artifacts, and discourse structures that are used

to frame, create, and convey knowledge. The cultural construction of tools 1 profoundly influences how people learn and how knowledge is organized and communicated, but more local and individualized tools play similar roles in particular contexts. For example, data collection protocols, maps, databases, online interfaces, and computer simulations may all shape how knowledge is produced and how learning occurs in a given setting. Social norms and conventions—whether at a scientific conference, in a classroom, or among a self-organized community group—may also serve as tools that mediate learning and knowledge sharing.

Along those same lines, it can take time for learners who are new to science to understand that measures and the evidence that they provide are developed according to community norms, rather than being direct, self-evident representations of the world ( Manz, 2016 ). It can take even longer for learners to feel like they can contribute to those norms, especially if those norms are presented as the exclusive providence of professional scientists or are grounded in cultural norms from dominant communities. For example, the vigorous questioning that is a norm in discourse among practicing scientists can be discouraging when it is extended, often without thinking about it, to people new to science ( Pandya et al., 2007 ). It is particularly dissonant compared to values of welcoming people to a field and affirming their identity as valued contributors.

Understanding and Working with Data

Many of the tools and practices of science are linked to bodies of data and the associated practices for collecting, organizing, representing, modeling, and interpreting data. The power of data to enhance our understanding of the natural world and to address meaningful problems in our local and global communities is one of the factors that inspires people to participate in science. Though understanding and working with data is technically a subset of participating in scientific practices, the committee chooses to highlight these particular practices because of their centrality to citizen science.

Opportunities to learn to understand science and do science through active engagement with data are rich, plentiful, and multifaceted. In everyday thinking, most people are accustomed to interacting with whole objects embedded in naturalistic contexts. In contrast, framing scientific questions and designing methods to investigate them typically requires a more precise focus on the specific attributes of the objects or phenomena that

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1 The committee wishes to clarify that, in this case, “tools” is defined broadly. Written language, for example, is a tool constructed to transmit ideas. In science, tools are the apparatuses that facilitate the work and process of science: a tool might be a methodological protocol or a mechanism for measuring data.

are relevant to the question and the intentional development of a method for measuring or classifying those attributes. Most people have practical experience with measures of spatial dimensions, such as length, volume, area, and weight, but many measured attributes in science may take less familiar forms, such as rates and ratios (e.g., parts per million, radioactive decay rates) or involve magnitudes—either very large or very small—that fall outside everyday experience (e.g., geologic time, light years, microns, nanometers). Science may also involve developing ways of measuring or classifying behavioral phenomena (e.g., aggressive behavior), which must be operationally defined in the context of a scientific investigation—that is, the investigators and participants have to share a definition of what counts as an occurrence of the behavior of interest in the context of the study and specify how to reliably rate its intensity or frequency.

Data collection also provides a gateway for learning about issues related to measurement and variability, especially when learners have opportunities to reflect on and reason about what they are doing. Repeated measurement often creates conditions for noticing variability and for beginning to think about the sources of that variability. Representing and visualizing variability in a variety of ways can help people see data in the aggregate and to recognize distributions that have central tendencies (e.g., mean, mode, median) and variability or spread, as well as shapes of various sorts ( Lehrer and Schauble, 2004 ). Repeated experience representing variability in data and thinking about different possible explanations for observed variability can help people better explore what drives good practice in designing and implementing data collection. They may become more responsive to or even spontaneously suggest procedures such as improving conditions of observation, using reliable instruments, training multiple data collectors to be consistent, and using multiple samples to reduce error variation in data being collected.

Lehrer and English (2018) wrote a comprehensive overview of methods for introducing young learners to central ideas related to measurement, sampling, variability, and distributions through data modeling activities. In this review, they propose a framework for organizing key concepts and the practices through which they are expressed and understood. Although this framework is aimed at younger learners in classrooms, such an approach could be applied to learners of all ages in various settings. The learning-focused road map starts with forming questions, and then moves into making decisions about relevant attributes and how they will be measured, organizing data and representing variability in distributions of data, and ultimately making inferences, which will in turn stimulate new questions (see Figure 4-1 ). Similar to other inquiry-driven approaches to science education that emphasize doing science as engaging in interrelated practices (e.g., Manz, 2016 ; National Research Council, 2007 , 2012 ; Schwartz et

Image

al., 2009 ), data collection and data modeling can be connected in iterative cycles. This cycle begins with forming questions, and then moves into making decisions about relevant attributes and how they will be measured, organizing data and representing variability in distributions of data, and ultimately making inferences, which will in turn stimulate new questions.

Several projects have looked more closely at how students learn to engage in practices related to scientific modeling; these projects offer field-tested strategies and curricular resources for supporting this learning with topics such as genetics, Darwinian evolution, plant growth, light and shadows, and evaporation and condensation ( Lehrer and Schauble, 2004 ; Schwarz et al., 2009 ; Stewart, Cartier, and Passmore, 2005 ). Some common features have appeared across these various projects. One feature is that learners generally need some prior knowledge in a topic or domain to ground their thinking. As has been demonstrated in many studies of cognition and learning, it is difficult for people to engage in sophisticated, productive thinking and problem solving without a sufficient knowledge base to think with. For example, in scientific modeling, students working in the domain of genetics should already have some background in topics such as

meiosis. Modeling activities would be aimed at deepening this knowledge further, integrating it with new concepts, and using it to develop specific models. This background knowledge may come from a variety of sources—provided by instructors and curricular materials, gathered through online or library research, and so forth. At the same time, it is important to set up the learning situation to encourage learners to be able to probe their own understanding of established knowledge, to raise questions about it, and to evaluate the credibility of their sources rather than passively accepting everything on authority.

A second common feature across a variety of projects is providing sufficient time for repeated cycles of data collection, modeling, and revision. Many of the projects reported in the literature played out over multiple months or even entire academic years. A third common feature is that teachers or teams of teachers and researchers provided systematic facilitation to help guide students toward more and more sophisticated ways of thinking about and engaging in modeling. They did this through the types of assignments they made and how they sequenced them, how they modeled and managed classroom discourse, and the physical and representational resources they provided for conducting investigations and for organizing and representing data and models.

The Importance of Motivation, Interest, and Identity

Motivation, interest, and identity can be thought of as inputs to, mediators for, and outcomes of participation in science. For example, interest in a science topic can motivate people to seek out information; people whose whole identities are welcomed and appreciated are more likely to participate in science learning activities ( Rahm et al., 2003 ); and building identity as someone with something to contribute to science ( Ballard, Harris, and Dixon, 2017 ) can deepen an individual’s interest in science ( Bonney et al., 2009 ). 2

Learning research suggests that motivation, interest, and identity are important touchstones for learning. An individual’s identity plays an important role in learning—both through shaping what is of interest, as well as what people find motivating. A spark of curiosity can develop into an interest, but to support long-term learning and eventual identification with the scientific enterprise, learners must demonstrate sustained and persistent motivation ( Hidi and Renninger, 2006 ). Underdevelopment of these compe-

2 As a note, the committee wishes to acknowledge issues around motivation, interest, and identity are not specific to science, and are important to learning in any disciplinary context. For the purposes of this report, however, the committee is interested in how to support these outcomes in science and is discussing research with that specific focus.

tencies present substantial obstacles to learning, while support for the development of these competencies can lead to achievement of science learning outcomes. In the “free-choice” contexts of citizen science, these constructs are particularly important as they are integral to the drive to participate, as well as the choice to stay engaged in the work. The committee finds it particularly important to call out this interplay of identity, motivation, and interest, as it is critical to support learning in citizen science.

Learning experiences can be purposefully designed in ways that support or constrain development in these arenas. In this chapter, we discuss these competencies as mediators for learning and their subsequent role(s) in learning processes. In the following chapter, we consider how citizen science can support their development as outcomes in science learning.

Two primary theories support contemporary understandings of motivation. Expectancy value theory posits that people are goal oriented and that behavior is driven by the relationship between an individual’s expectations or perceptions and the value they place on the goal they are working toward. Such an approach predicts that when more than one behavior is possible, the behavior chosen will be the one with the largest combination of expected success and value ( Palmgreen, 1984 ). An alternative theory, achievement goal theory, was developed in order to understand the unfolding or development of engagement in a task. Achievement goals generally refer to the purposes or reasons an individual is pursuing a task as well as the standards or criteria used to judge successful performance ( Pintrich, 2000 ; Pintrich and Schunk, 1996 ). This theory identifies two types of co-mingled achievement goals: mastery, sometimes called competence, and performance. Mastery goals have been labeled task-goals ( Nicholls, 1984 ) and learning goals ( Dweck and Leggett, 1988 ; Elliott and Dweck, 1988 ), whereas performance goals have been labeled ego-goals ( Nicholls, 1984 ) and ability goals ( Ames and Ames, 1984 ). However, mastery and performance goals may also comingle.

An individual who adopts a performance goal toward learning is generally more concerned with the outcome and demonstrating his or her competence to others. A person who adopts mastery goals toward learning is often more focused on the process of learning rather than the outcome and often experiences learning to be a rewarding in and of itself. In the domain of education, mastery goals have been articulated to focus on what learners should know, understand, and be able to do. Thus, mastery requires that individuals understand concepts, have background knowledge (content), and can address tasks that require critical thinking, inference, induction, deduction, and application of knowledge—to solve problems and address

issues in novel situations. In schools, students with mastery orientations show consistent, positive learning outcomes, engage in deeper cognitive strategies, and are intrinsically motivated to learn ( Anderman and Young, 1994 ; Lee and Brophy, 1996 ; Meece, Blumenfeld, and Hoyle, 1988 ).

An important development in the field of motivation has been focused on the ways in which goals and forms of motivation are variable and context dependent—that is, how the social context impacts motivation, goals, and participation ( Nolen and Ward, 2008 ). Part of this social context is the ways in which tasks and forms of participation are intertwined. For individuals that have mastery-oriented goals, a task that does not afford continual mastery goals can lead to disengagement—if something is too easy, a mastery-oriented person may lose interest and seek other opportunities.

Another important finding in the field of science education has been the interlocking of motivation and learning with opportunities to participate in the full range of scientific practices and sense-making (e.g., Chin and Brown, 2000 ). That is, motivation and learning increase when individuals have opportunities to develop explanations, carry out investigations, and evaluate knowledge claims ( Blumenfeld et al., 1991 ). Importantly, the different forms of practice and activity tend to mutually reinforce each other—learning in one area tends to promote learning and engagement in another ( Eveleigh et al., 2014 ). Furthermore, scholarship has demonstrated the need to carefully attend to the variation in factors that motivate or fail to motivate students from particular demographic groups when designing instruction.

Motivation is a central component of the ability to develop self-efficacy (i.e., feelings of “I can do this”). There is considerable evidence that people will work harder, perform better, and persist in the face of challenges—all central components in learning—if they have some sense of control and believe that they are capable of success ( Atkinson, 1964 ; Eccles et al., 1983 ; Hidi and Ainley, 2008 ; Sansone, 2009 ; Wigfield et al., 2006 ). People generally develop feelings of self-efficacy from past experiences, observations of others, performance feedback, emotional or physiological states, and social influences. As such, feelings of self-efficacy can evolve with new experiences.

When people are interested in a topic or task, they are more likely to be attracted to challenges, use effective learning strategies, and make appropriate use of feedback (Csikszentmihalyi, Rathunde, and Whalen, 1993 ; Lipstein and Renninger, 2006 ; Renninger and Hidi, 2002 ). With increased interest, participants will begin to develop and seek out answers to questions as they work on a project ( Renninger, 2000 ), and they are also

more likely to use systematic approaches to answer these questions ( Engle and Conant, 2002 ; Kuhn and Franklin, 2006 ; Renninger, 2000 ). Having an interest in a subject helps individuals to pay attention, learn, and retain more information for longer periods of time ( Beier and Ackerman, 2003 ; Hidi and Renninger, 2006 ; National Research Council, 2000; Renninger and Hidi, 2011 ). Learning contexts that engage participants’ personal interests have demonstrated increased participation, particularly by people from underrepresented groups ( Barton and Tan, 2018 ).

A person’s interest in a topic may be an enduring connection to a domain (e.g., they have a concern about water quality and public health) or connection to specific features of a task (e.g., they enjoy hiking and being outdoors with their family). Interest is not fixed but rather develops over time. Interest begins with sparks of curiosity, extends to voluntary re-engagement, and if supported, can develop into a part of a person’s identity ( Hidi and Renninger, 2006 ; Renninger and Hidi, 2011 ). Vocational interests in children often change with age and seem to be particularly aligned with one’s social class at ages 9–13 ( Cook et al., 1996 ), whereas beyond age 13, children develop differentiated and individualized career interests based on their internal, unique selves ( Schoon, 2001 ). Learners of all ages can be supported to develop specific interests ( Renninger, 2010 ). Beyond changes associated with getting older, interests are also influenced by other mutable factors, such as gender, race, ethnicity, and social class, all of which are discussed in the identity section of this chapter, below.

Part of learning involves the construction of identities, including viewing one’s self as a member or part of an enterprise. We discuss two primary ways of understanding issues of identity and science learning including: (1) disciplinary identities—who develops, and how, an identity as someone who does science and contributes to science learning, and (2) social and cultural identities—how socially and culturally constructed identities such as racial and gendered identities intersect with learning, as well as how power dynamics and processes such as racialization impact learning and engagement.

Disciplinary identity. In science, one particularly important aspect of learning is developing a disciplinary identity as someone who actually does science and can contribute to science more broadly. Developing an identity as someone who does and can contribute to science is shaped by an individual’s long-standing perceptions and experiences with science ( Atwater et al., 2013 ), some of which may not be very positive. For example, more than 60 years of research has demonstrated that young people, as well

adults, tend to think about science as a body of facts or as a rigid, largely laboratory-based process that white males engage in ( Finson, 2002 ; Mead and Metraux, 1957 ). However, this perception is changing; a recent meta-analysis of more than 50 years of “draw-a-scientist” surveys collected from more than 20,000 children in the United States shows drawings depicted more female scientists in later decades, especially among younger children ( Miller et al., 2018 ).

Social and cultural identities. This research also highlights the ways in which individuals develop, even if implicitly, gendered and racialized perspectives about who does science; thus, social identities and disciplinary identities are intertwined, which we explore in the following section. It is important to note the ways in which these issues exclude people and influence the progress and relevance of science. For example, the increased participation of women and scientists from nondominant backgrounds has led to important new foundational knowledge in several fields of science. The environmental justice community draws a link between the historical exclusion of certain communities from science and the prevalence of toxic areas within communities of color.

The ways in which researchers have investigated the construction, reinforcement, and interaction of social and cultural identities with learning has shifted over time. An individual’s social and cultural identity shapes how he or she will engage with science and what each will learn from these experiences. Similarly, these identities will influence the extent to which they come to identify with science or as someone who can contribute to science. The next chapter will explore the ways in which these identities intersect with, influence, and are influenced by science learning outcomes in citizen science.

Scientific Reasoning, Epistemological Thinking, and the Nature of Science

The concepts covered in this subsection—scientific reasoning and epistemological thinking 3 —correspond to Strand 2 (using arguments and fact related to science) and Strand 4 (reflecting on science as a way of knowing). Critical thinking and reasoning in science involve a number of factors that must be coordinated in complex ways. Learners need to develop an understanding of how to differentiate among facts, hypotheses, theories, and evidence, and how data can gain meaning as they are used to evaluate potential explanations ( King and Kitchener, 1994 ; Kuhn, 1999 ; Smith et al., 2000 ). Further learning objectives involve knowledge of how research

3 Epistemological thinking understands the nature of building knowledge in science and the use of the methods of science to develop knowledge through scientific inquiry and argumentation.

designs, sampling, and measurement methodologies provide frameworks by which research questions and hypotheses are related to data, and how these methodologies can enable or limit the strength of the inferences that can be drawn from data. A central example of this is distinguishing when patterns of evidence do and do not warrant conclusions about causality ( Kuhn et al., 1995 ; Schauble, 1996 ). Closely related to these abilities is the process of scientific argumentation, whereby people construct knowledge claims, justify them with evidence, consider and critique alternative claims, and revise claims ( Berland and McNeill, 2010 ). There is general consensus among learning scholars that acquiring competence in scientific reasoning, argumentation, and discourse requires rich and extended opportunities to engage actively in these as practices ( National Research Council, 2007 , 2012 ).

Scientific reasoning entails learning to coordinate knowledge claims with evidence, but this, in turn, depends on understanding that there is a difference between claims and evidence or between facts and beliefs. Researchers who study epistemological development in children and adults in Western cultures typically propose that there is a general progression in the development of epistemological understanding ( Hofer and Pintrich, 1997 ; King and Kitchener, 1994 ; Kuhn, 1999 ; Perry, 1970 ). An early view takes a dualistic stance toward knowledge, believing that all knowledge is unproblematically true or false and can be known with certainty by authorities. Facts are seen as a direct representation of reality, and experts should not disagree unless one knows less, has made a mistake, or is intentionally lying. Further in the progression, some uncertainty may be admitted, but it is seen as temporary. Eventually, an individual may recognize that knowledge is uncertain and that different people can have different subjective views, but he or she may still not fully distinguish between theory and evidence and may not feel that how well a belief is justified by evidence can or should be adjudicated, because it is a matter of personal opinion. Evidence may be seen more as an illustration of a belief than a justification for it. At more advanced levels, knowledge is viewed as something that is actively constructed and must be supported and justified by evidence. Differing interpretations of evidence vary in how well-grounded they are, and even experts’ judgments can be productively questioned. One’s own beliefs and conclusions are also open to revision based on new evidence or new interpretations of evidence. Individuals with this stance see knowledge as constructed and view themselves as active meaning-makers.

Both longitudinal and cross-sectional studies indicate that the most advanced levels are uncommon even among graduating college seniors ( King and Kitchener, 1994 ), and are most often seen among advanced graduate students. However, older adults, noncollege-educated adults, and non-Western populations have not been well-represented in research sam-

ples (for an exception, see Belenky et al., 1986 ). It is possible that older individuals may bring more sophisticated critical thinking skills and more advanced beliefs about what they think knowledge is and how it is generated as a result of work and life experience.

While there are clear developmental progressions in epistemological thinking, current theories generally do not conceive of them in terms of fixed all-or-nothing stages, and the same individual may show somewhat more or less sophisticated reasoning or may draw on alternative views of knowledge and knowing as a function of the situation and the types of supports available in the environment. There is also evidence of the importance of structured learning opportunities: younger learners are capable of advancing in their epistemological reasoning and their use of evidence to support arguments in appropriate science contexts ( Berland and McNeill, 2010 ; Smith et al., 2000 ); at the same time, adults may not commonly achieve higher levels of sophistication spontaneously without such learning opportunities ( King and Kitchener, 1994 ). Kuhn (1999) argues that, in addition to epistemological knowledge, critical thinking also involves metacognitive knowledge—that is, an individual becomes more aware of his or her own thinking and is able to intentionally reflect on it and also control it by monitoring it and selecting strategies to manage critical thinking. Constructing a rebuttal in science, for example, requires this kind of complex, controlled thinking to evaluate the strengths and weaknesses of counterclaims and to generate and evaluate support for one’s own claims.

Sociocultural perspectives are an important additional lens for understanding how epistemologies and scientific reasoning develop. They also call attention to variations in how people from different cultural backgrounds think about knowledge and the sources and processes that create and validate knowledge (e.g., Bang and Medin, 2010 ). Globally, many different cultures have developed sophisticated epistemologies based in systematic observations of nature. The traditional ecological knowledge of Indigenous communities is one example. The interplay of indigenous epistemologies and more mainstream scientific disciplines has been productive for a range of topics including, but not limited to, ecosystem management, fisheries, agroforestry, animal behavior, medicine, and pharmacology. Traditional knowledge not only brings diverse ideas to these areas of study, but also is associated with a cultural framework of respect, reciprocity, and responsibility ( Kimmerer, 1998 ; Pierotti and Wildcat, 2000 ). Although traditional ecological knowledge has recently been formally recognized as having an equal status with Western scientific knowledge ( United Nations Environment Programme, 1998 ), it has historically been marginalized or ignored in the scientific community ( Salmon, 1996 ).

While European and Western scientific epistemologies have been productive in many contexts, history is rife with examples in which it has

been used to oppress certain peoples. For example, colonists have utilized biased, ethnocentric tests to support racist ideals and assert their cultural superiority over colonized people, resulting in a legacy of persistent distrust and alienation of some cultures or communities from scientific research. Sociocultural analyses emphasize that the ways of knowing associated with Western science are not culturally neutral, and they have been privileged in part because they have been associated with power and dominant culture ( Agrawal, 1995 ).

Some recent projects have attempted to develop new approaches to community participation in and support for science and science education by taking an explicitly integrative approach toward epistemological differences. In formal education contexts, for learners who recognize differences in the orientations of their home culture and that of western science, effective instructors can help students negotiate “border crossings” between the different ways of thinking ( Aikenhead and Jegede, 1999 ; Costa, 1995 ). For example, Bang and Medin (2010) describe how a large project collaborating with urban and rural Native American communities blends the practice of science with elements of culturally based epistemological orientations, such as the stance that humans are an interconnected part of the natural world rather than independent and external from it. An integrated approach that relies on the participation of community members (e.g., elder input, use of traditional language, community participation in the research agenda, respect of cultural value, informed consent) may be useful to remove the implicit privileging of Western scientific thinking and recognize the importance of different cultural values and orientations. Place-based educational programs that are co-created and implemented with members of indigenous communities have demonstrated success in helping Native American learners to navigate multiple epistemologies and deepen their understanding of science related to plants, animals, and ecology while also appreciating the historic legacy and contemporary relevance of their own communities’ knowledge and experience of the natural world.

Fluency in science also includes an understanding of the nature of science, which includes an in-depth understanding of the histories, philosophies, and sociologies of the institution of science. This metacognition also requires an awareness of the values implicit in scientific endeavors that shape the products of science, and an awareness of the ways in which science is not neutral and subject to constant review. It also includes an understanding of how science knowledge is built and the notion that there is a community of scientists working together to build knowledge through the use of scientific practices. Mastery of these concepts is embodied in Strand 4, reflecting on science as a way of knowing.

There is general agreement about the important concepts that are part of the nature of science ( McComas and Olson, 1998 ; National Research

Council, 2007 ; Osborne, Simons, and Collins, 2003 ). First and foremost, understanding the nature of science recognizes that science is an empirical way of knowing about the world that utilizes transparent methods to make evidence-based claims. Science is an ongoing enterprise: Knowledge acquired scientifically is subject to continued review and revision. It is also important to understand that scientific knowledge is partially based on human inference, human imagination and creativity, and the social and cultural contexts in which it is formed. Data are collected and interpreted in context: current scientific perspectives, cultural influences, and the experiences and values of individual scientists all matter in the building of scientific knowledge. Third, there is no unitary scientific method. Instead, science is built on a number of methods, which like scientific knowledge in general, are subject to constant innovation, creativity, and revision. Finally, science can be understood as an epistemological framework, and even that framework is subject to revision as new ideas. In fact, thinking about the way in which learners approach science can yield insight into how the nature of science itself evolves over time.

It has been argued that engaging students in authentic science experiences contributes to their understanding of the nature of science ( Schwartz et al., 2004 ), but evidence suggests that it is important to explicitly teach students about the nature of science ( Abd-El-Khalick and Lederman, 2000 ). Because citizen science engages directly in scientific activity, it has the potential—though largely unrealized and not without significant supports—to provide opportunities to learn about the nature of science.

This chapter outlines some of the most current understandings of how people learn, and how people learn science. As we explain throughout this chapter, individuals learn, they learn through interaction with others, and their learning occurs in a broad landscape that is influenced by culture, practice, and history. Historically, inequities in society have affected people’s opportunity to learn by discounting or neglecting cultural knowledge and prior experience. Attending to those prior experiences and providing learning opportunities that welcome the individual, social, and sociocultural aspects of learning are especially effective for addressing these inequities and provide enriched opportunities for all learners.

As we will see in the next chapter, awareness of the multiple factors that influence learning provide opportunities to build rich learning experiences that leverage and build out from citizen science. At the same time, research on learning reveals that any learning, including learning is citizen science, occurs in a larger ecosystem of learning opportunities and experiences. That means design and practice of citizen science for learning should be

considered within a broader landscape of learning experiences, which can inform, enrich, and extend learning opportunities in citizen science. The next chapter will discuss these learning processes in the specific contexts of citizen science projects.

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In the last twenty years, citizen science has blossomed as a way to engage a broad range of individuals in doing science. Citizen science projects focus on, but are not limited to, nonscientists participating in the processes of scientific research, with the intended goal of advancing and using scientific knowledge. A rich range of projects extend this focus in myriad directions, and the boundaries of citizen science as a field are not clearly delineated. Citizen science involves a growing community of professional practitioners, participants, and stakeholders, and a thriving collection of projects. While citizen science is often recognized for its potential to engage the public in science, it is also uniquely positioned to support and extend participants' learning in science.

Contemporary understandings of science learning continue to advance. Indeed, modern theories of learning recognize that science learning is complex and multifaceted. Learning is affected by factors that are individual, social, cultural, and institutional, and learning occurs in virtually any context and at every age. Current understandings of science learning also suggest that science learning extends well beyond content knowledge in a domain to include understanding of the nature and methods of science.

Learning Through Citizen Science: Enhancing Opportunities by Design discusses the potential of citizen science to support science learning and identifies promising practices and programs that exemplify the promising practices. This report also lays out a research agenda that can fill gaps in the current understanding of how citizen science can support science learning and enhance science education.

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February 21, 2024

Why Writing by Hand Is Better for Memory and Learning

Engaging the fine motor system to produce letters by hand has positive effects on learning and memory

By Charlotte Hu

Student handwriting notes in class

FG Trade/Getty Images

Handwriting notes in class might seem like an anachronism as smartphones and other digital technology subsume every aspect of learning across schools and universities. But a steady stream of research continues to suggest that taking notes the traditional way—with pen and paper or even stylus and tablet—is still the best way to learn, especially for young children. And now scientists are finally zeroing in on why.

A recent study in Frontiers in Psychology monitored brain activity in students taking notes and found that those writing by hand had higher levels of electrical activity across a wide range of interconnected brain regions responsible for movement, vision, sensory processing and memory. The findings add to a growing body of evidence that has many experts speaking up about the importance of teaching children to handwrite words and draw pictures.

Differences in Brain Activity

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The new research, by Audrey van der Meer and Ruud van der Weel at the Norwegian University of Science and Technology (NTNU), builds on a foundational 2014 study . That work suggested that people taking notes by computer were typing without thinking, says van der Meer , a professor of neuropsychology at NTNU. “It’s very tempting to type down everything that the lecturer is saying,” she says. “It kind of goes in through your ears and comes out through your fingertips, but you don’t process the incoming information.” But when taking notes by hand, it’s often impossible to write everything down; students have to actively pay attention to the incoming information and process it—prioritize it, consolidate it and try to relate it to things they’ve learned before. This conscious action of building onto existing knowledge can make it easier to stay engaged and grasp new concepts .

To understand specific brain activity differences during the two note-taking approaches, the NTNU researchers tweaked the 2014 study’s basic setup. They sewed electrodes into a hairnet with 256 sensors that recorded the brain activity of 36 students as they wrote or typed 15 words from the game Pictionary that were displayed on a screen.

When students wrote the words by hand, the sensors picked up widespread connectivity across many brain regions. Typing, however, led to minimal activity, if any, in the same areas. Handwriting activated connection patterns spanning visual regions, regions that receive and process sensory information and the motor cortex. The latter handles body movement and sensorimotor integration, which helps the brain use environmental inputs to inform a person’s next action.

“When you are typing, the same simple movement of your fingers is involved in producing every letter, whereas when you’re writing by hand, you immediately feel that the bodily feeling of producing A is entirely different from producing a B,” van der Meer says. She notes that children who have learned to read and write by tapping on a digital tablet “often have difficulty distinguishing letters that look a lot like each other or that are mirror images of each other, like the b and the d.”

Reinforcing Memory and Learning Pathways

Sophia Vinci-Booher , an assistant professor of educational neuroscience at Vanderbilt University who was not involved in the new study, says its findings are exciting and consistent with past research. “You can see that in tasks that really lock the motor and sensory systems together, such as in handwriting, there’s this really clear tie between this motor action being accomplished and the visual and conceptual recognition being created,” she says. “As you’re drawing a letter or writing a word, you’re taking this perceptual understanding of something and using your motor system to create it.” That creation is then fed back into the visual system, where it’s processed again—strengthening the connection between an action and the images or words associated with it. It’s similar to imagining something and then creating it: when you materialize something from your imagination (by writing it, drawing it or building it), this reinforces the imagined concept and helps it stick in your memory.

The phenomenon of boosting memory by producing something tangible has been well studied. Previous research has found that when people are asked to write, draw or act out a word that they’re reading, they have to focus more on what they’re doing with the received information. Transferring verbal information to a different form, such as a written format, also involves activating motor programs in the brain to create a specific sequence of hand motions, explains Yadurshana Sivashankar , a cognitive neuroscience graduate student at the University of Waterloo in Ontario who studies movement and memory. But handwriting requires more of the brain’s motor programs than typing. “When you’re writing the word ‘the,’ the actual movements of the hand relate to the structures of the word to some extent,” says Sivashankar, who was not involved in the new study.

For example, participants in a 2021 study by Sivashankar memorized a list of action verbs more accurately if they performed the corresponding action than if they performed an unrelated action or none at all. “Drawing information and enacting information is helpful because you have to think about information and you have to produce something that’s meaningful,” she says. And by transforming the information, you pave and deepen these interconnections across the brain’s vast neural networks, making it “much easier to access that information.”

The Importance of Handwriting Lessons for Kids

Across many contexts, studies have shown that kids appear to learn better when they’re asked to produce letters or other visual items using their fingers and hands in a coordinated way—one that can’t be replicated by clicking a mouse or tapping buttons on a screen or keyboard. Vinci-Booher’s research has also found that the action of handwriting appears to engage different brain regions at different levels than other standard learning experiences, such as reading or observing. Her work has also shown that handwriting improves letter recognition in preschool children, and the effects of learning through writing “last longer than other learning experiences that might engage attention at a similar level,” Vinci-Booher says. Additionally, she thinks it’s possible that engaging the motor system is how children learn how to break “ mirror invariance ” (registering mirror images as identical) and begin to decipher things such as the difference between the lowercase b and p.

Vinci-Booher says the new study opens up bigger questions about the way we learn, such as how brain region connections change over time and when these connections are most important in learning. She and other experts say, however, that the new findings don’t mean technology is a disadvantage in the classroom. Laptops, smartphones and other such devices can be more efficient for writing essays or conducting research and can offer more equitable access to educational resources. Problems occur when people rely on technology too much , Sivashankar says. People are increasingly delegating thought processes to digital devices, an act called “ cognitive offloading ”—using smartphones to remember tasks, taking a photo instead of memorizing information or depending on a GPS to navigate. “It’s helpful, but we think the constant offloading means it’s less work for the brain,” Sivashankar says. “If we’re not actively using these areas, then they are going to deteriorate over time, whether it’s memory or motor skills.”

Van der Meer says some officials in Norway are inching toward implementing completely digital schools . She claims first grade teachers there have told her their incoming students barely know how to hold a pencil now—which suggests they weren’t coloring pictures or assembling puzzles in nursery school. Van der Meer says they’re missing out on opportunities that can help stimulate their growing brains.

“I think there’s a very strong case for engaging children in drawing and handwriting activities, especially in preschool and kindergarten when they’re first learning about letters,” Vinci-Booher says. “There’s something about engaging the fine motor system and production activities that really impacts learning.”

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    Rather, it highlights the critical elements of teaching and learning described in the cognitive research. In addition, some research-based instructional strategies cut across these elements including formative assessment and other techniques for monitoring student learning. These strategies can help ensure that students are engaged ...

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