What Is Research, and Why Do People Do It?

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article and research

  • James Hiebert 6 ,
  • Jinfa Cai 7 ,
  • Stephen Hwang 7 ,
  • Anne K Morris 6 &
  • Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

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Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

An image of the book's description with the words like research, science, and inquiry and what the word research meant in the scientific world.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

An image represents the observation required in the scientific inquiry including planning and explaining.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

An image represents the data explanation as it is not limited and takes numerous non-quantitative forms including an interview, journal entries, etc.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

An image represents the scientific inquiry definition given by the editors of Britannica and also defines the hypothesis on the basis of the experiments.

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

An image represents the rationale and the prediction for the scientific inquiry and different types of information provided by the terms.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

An image represents the cycle of events that take place before making predictions, developing the rationale, and studying the prediction and rationale multiple times.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

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Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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The top 10 journal articles of 2020

In 2020, APA’s 89 journals published more than 5,000 articles—the most ever and 25% more than in 2019. Here’s a quick look at the 10 most downloaded to date.

Vol. 52 No. 1 Print version: page 24

man watching television

1. Me, My Selfie, and I: The Relations Between Selfie Behaviors, Body Image, Self-Objectification, and Self-Esteem in Young Women

Veldhuis, j., et al..

Young women who appreciate their bodies and consider them physical objects are more likely to select, edit, and post selfies to social media, suggests this study in Psychology of Popular Media (Vol. 9, No. 1). Researchers surveyed 179 women, ages 18 to 25, on how often they took selfies, how they selected selfies to post, how often they used filters and editing techniques, and how carefully they planned their selfie postings. They also assessed participants’ levels of body appreciation and dissatisfaction, self-objectification, and self-esteem. Higher levels of self-objectification were linked to more time spent on all selfie behaviors, while body appreciation was related to more time spent selecting selfies to post, but not frequency of taking or editing selfies. Body dissatisfaction and self-esteem were not associated with selfie behaviors. DOI: 10.1037/ppm0000206

2. A Closer Look at Appearance and Social Media: Measuring Activity, Self-Presentation, and Social Comparison and Their Associations With Emotional Adjustment

Zimmer-gembeck, m. j., et al..

This Psychology of Popular Media (online first publication) article presents a tool to assess young people’s preoccupation with their physical appearance on social media. Researchers administered a 21-item survey about social media to 281 Australian high school students. They identified 18 items with strong inter-item correlation centered on three categories of social media behavior: online self-presentation, appearance-related online activity, and appearance comparison. In a second study with 327 Australian university students, scores on the 18-item survey were found to be associated with measures of social anxiety and depressive symptoms, appearance-related support from others, general interpersonal stress, coping flexibility, sexual harassment, disordered eating, and other factors. The researchers also found that young women engaged in more appearance-related social media activity and appearance comparison than did young men. DOI: 10.1037/ppm0000277

3. The Novel Coronavirus (COVID-2019) Outbreak: Amplification of Public Health Consequences by Media Exposure

Garfin, d. r., et al..

Repeated media exposure to the COVID-19 pandemic may be associated with psychological distress and other public health consequences, according to this commentary in Health Psychology (Vol. 39, No. 5). The authors reviewed research about trends in health behavior and psychological distress as a response to media coverage of crises, including terrorist attacks, school shootings, and disease outbreaks. They found that repeated media exposure to collective crises was associated with increased anxiety and heightened acute and post-traumatic stress, with downstream effects on health outcomes such as new incidence of cardiovascular disease. Moreover, misinformation can further amplify stress responses and lead to misplaced or misguided health-protective and help-seeking behaviors. The authors recommended public health agencies use social media strategically, such as with hashtags, to keep residents updated during the pandemic. They also urged the public to avoid sensationalism and repeated coverage of the same information. DOI: 10.1037/hea0000875

4. Barriers to Mental Health Treatment Among Individuals With Social Anxiety Disorder and Generalized Anxiety Disorder

Goetter, e. m., et al..

This study in Psychological Services (Vol. 17, No. 1) indicates that 3 in 4 people who suffer from anxiety do not receive proper care. Researchers recruited 226 participants in the United States who were previously diagnosed with social anxiety disorder or generalized anxiety disorder and assessed their symptom severity and asked them to self-report any barriers to treatment. Shame and stigma were the highest cited barriers, followed by logistical and financial barriers and not knowing where to seek treatment. Participants with more severe symptoms reported more barriers to treatment than those with milder symptoms. Racial and ethnic minorities reported more barriers than racial and ethnic majorities even after controlling for symptom severity. The researchers called for increased patient education and more culturally sensitive outreach to reduce treatment barriers. DOI: 10.1037/ser0000254

5. The Construction of “Critical Thinking”: Between How We Think and What We Believe

This History of Psychology (Vol. 23, No. 3) article examines the emergence of “critical thinking” as a psychological concept. The author describes how, between World War I and World War II in the United States, the concept emerged out of growing concerns about how easily people’s beliefs could be changed and was constructed in a way that was independent of what people believed. The author delves into how original measurements of critical thinking avoided assumptions about the accuracy of specific real-world beliefs and details how subsequent critical thinking tests increasingly focused on logical abilities, often favoring outcome (what we believe) over process (how we think). DOI: 10.1037/hop0000145

6. Treatment of Alcohol Use Disorder: Integration of Alcoholics Anonymous and Cognitive Behavioral Therapy

Breuninger, m. m., et al..

This article in Training and Education in Professional Psychology (Vol. 14, No. 1) details how to work with alcohol use disorder patients who are participating in both cognitive behavioral therapy (CBT) and Alcoholics Anonymous (AA). The authors point to distinctions between AA and CBT: The goal of AA is total abstinence and the primary therapeutic relationship is with a peer in recovery, while CBT takes a less absolute approach and the primary relationship is with a psychotherapist. The authors also point to commonalities: both approaches emphasize identifying and replacing dysfunctional beliefs and place value in social support. The authors recommend clinicians and trainees become more educated about AA and recommend a translation of the 12-step language into CBT terminology to bridge the gap. DOI: 10.1037/tep0000265

7. Positivity Pays Off: Clients’ Perspectives on Positive Compared With Traditional Cognitive Behavioral Therapy for Depression

Geschwind, n., et al..

Positive cognitive behavioral therapy, a version of CBT focused on exploring exceptions to the problem rather than the problem itself, personal strengths, and embracing positivity, works well to counter depressive symptoms and build well-being, according to this study in Psychotherapy (Vol. 57, No. 3). Participants received a block of eight sessions of traditional CBT and a block of eight sessions of positive CBT. Researchers held in-depth interviews with 12 of these participants. Despite initial skepticism, most participants reported preferring positive CBT but indicated experiencing a steeper learning curve than with traditional CBT. Researchers attributed positive CBT’s favorability to four factors: feeling empowered, benefiting from effects of positive emotions, learning to appreciate baby steps, and rediscovering optimism as a personal strength. DOI: 10.1037/pst0000288

8. Targeted Prescription of Cognitive-Behavioral Therapy Versus Person-Centered Counseling for Depression Using a Machine Learning Approach

Delgadillo, j., & gonzalez salas duhne, p..

Amachine learning algorithm can identify which patients would derive more benefit from cognitive behavioral therapy (CBT) versus counseling for depression, suggests research in this Journal of Consulting and Clinical Psychology (Vol. 88, No. 1) article. Researchers retrospectively explored data from 1,085 patients in the United Kingdom treated with either CBT or counseling for depression and discovered six patient characteristics—age, employment status, disability, and three diagnostic measures of major depression and social adjustment—relevant to developing an algorithm for prescribing the best approach. The researchers then used the algorithm to determine which therapy would work best for an additional 350 patients with depression. They found that patients receiving their optimal treatment type were twice as likely to improve significantly. DOI: 10.1037/ccp0000476

9. Traumatic Stress in the Age of COVID-19: A Call to Close Critical Gaps and Adapt to New Realities

Horesh, d., & brown, a. d..

This article in Psychological Trauma: Theory, Research, Practice, and Policy (Vol. 12, No. 4) argues that COVID-19 should be examined from a post-traumatic stress perspective. The authors call for mental health researchers and clinicians to develop better diagnoses and prevention strategies for COVID-related traumatic stress; create guidelines and talking points for the media and government officials to use when speaking to an anxious, and potentially traumatized, public; and provide mental health training to professionals in health care, education, childcare, and occupational support in order to reach more people. DOI: 10.1037/tra0000592

10. Emotional Intelligence Predicts Academic Performance: A Meta-Analysis

Maccann, c., et al..

Students with high emotional intelligence get better grades and score higher on standardized tests, according to the research presented in this article in Psychological Bulletin (Vol. 146, No. 2). Researchers analyzed data from 158 studies representing more than 42,529 students—ranging in age from elementary school to college—from 27 countries. The researchers found that students with higher emotional intelligence earned better grades and scored higher on achievement tests than those with lower emotional intelligence. This finding was true even when controlling for intelligence and personality factors, and the association held regardless of age. The researchers suggest that students with higher emotional intelligence succeed because they cope well with negative emotions that can harm academic performance; they form stronger relationships with teachers, peers, and family; and their knowledge of human motivations and socialinteractions helps them understand humanities subject matter. DOI: 10.1037/bul0000219

5 interviews to listen to now

Psychology’s most innovative thinkers are featured on APA’s Speaking of Psychology podcast , which highlights important research and helps listeners apply psychology to their lives. The most popular episodes of 2020, as measured by the number of downloads in the first 30 days, were: 

  • How to have meaningful dialogues despite political differences , with  Tania Israel, PhD
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  • The challenges faced by women in leadership , with  Alice Eagly, PhD
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Evaluating Resources: Research Articles

Research articles.

A research article is a journal article in which the authors report on the research they did. Research articles are always primary sources. Whether or not a research article is peer reviewed depends on the journal that publishes it.

Published research articles follow a predictable pattern and will contain most, if not all, of the sections listed below. However, the names for these sections may vary.

  • Title & Author(s)
  • Introduction
  • Methodology

To learn about the different parts of a research article, please view this tutorial:

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Articles, Books and . . . ? Understanding the Many Types of Information Found in Libraries

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Academic Journals

Magazines and trade journals, conference papers, technical reports, anthologies.

  • Documents and Reports
  • Non-Text Content
  • Archival Materials

Short works, anywhere from a paragraph up to about 30 pages, published as part of some larger work.

Because of their short length, articles often exclude background info and explanations, so they're usually the last stop in your research process, after you've narrowed down your topic and need to find very specific information.

The main thing to remember about articles is that they're almost always published in some larger work , like a journal, a newspaper, or an anthology. It's those "article containers" that define the types of articles, how you use them, and how you find them.

Articles are also the main reason we have so many databases . The Library Catalog lists everything we own, but only at the level of whole books and journals. It will tell you we have the New York Times, and for what dates, but it doesn't know what articles are in it. Search in UC Library Search using the "Articles, books, and more" scope will search all the databases we subscribe to and some we don't. If you find something we do not own, you can request it on Interlibrary Loan.

Physical Media

While newer journals and magazines are usually online, many older issues are still only available in paper. In addition, many of our online subscriptions explicitly don't include the latest material, specifically to encourage sales of print subscriptions. Older newspapers are usually transferred to microfilm.

Scholarly Sources

The terms academic or scholarly journal are usually synonymous with peer-reviewed , but check the journal's publishing policies to be sure. Trade journals, magazines, and newspapers are rarely peer-reviewed.

Primary or Secondary Sources

In the social sciences and humanities, articles are usually secondary sources; the exceptions are articles reporting original research findings from field studies. Primary source articles are more common in the physical and life sciences, where many articles are reporting primary research results from experiments, case studies, and clinical trials.

Clues that you're reading an academic article

article and research

  • Footnotes or endnotes
  • Bilbliography or list of references

Articles in academic (peer-reviewed) journals are the primary forum for scholarly communication, where scholars introduce and debate new ideas and research. They're usually not written for laymen, and assume familiarity with other recent work in the field. Journal articles also tend to be narrowly focused, concentrating on analysis of one or two creative works or studies, though they may also contain review articles or literature reviews which summarize recent published work in a field.

In addition to regular articles, academic journals often include book reviews (of scholarly books ) and letters from readers commenting on recent articles.

Clues that you're reading a non -academic article

article and research

  • Decorative photos
  • Advertisements

Unlike scholarly journals, magazines are written for a mainstream audience and are not peer-reviewed. A handful of academic journals (like Science and Nature ) blur the line between these two categories; they publish peer-reviewed articles, but combine them with news, opinions, and full-color photos in a magazine-style presentation.

Trade journals are targeted toward a specific profession or industry. Despite the name, they are usually not peer-reviewed. However, they sometimes represent a gray area between popular magazines and scholarly journals. When in doubt, ask your professor or TA whether a specific source is acceptable.

Newspapers as Primary Sources

Though usually written by journalists who were not direct witnesses to events, newspapers and news broadcasts may include quotes or interviews from people who were. In the absence of first-person accounts, contemporary news reports may be the closest thing to a primary source available.

Of all the content types listed here, newspapers are the fastest to publish. Use newspaper articles to find information about recent events and contemporary reports of/reactions to historic events.

article and research

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Reviews are a type of article that can appear in any of the categories above. The type of publication will usually determine the type of review. Newspapers and magazines review movies, plays, general interest books, and consumer products. Academic journals review scholarly books.

Note that a review is not the same as scholarly analysis and criticism! Book reviews, even in scholarly journals, are usually not peer-reviewed.

Review Scholarly Criticism

article and research

Conference papers aren't always published and can be tricky to find . Recent conference papers are often online, along with the PowerPoint files or other materials used in the actual presentation. However, access may be limited to conference participants and/or members of the academic organization which sponsored the conference.

In paper formats, all of the papers from a certain conference may be re-printed in the conference proceedings . Search for Proceedings of the [name of conference] to find what's available, or ask for help from a librarian. But be aware that published proceedings may only include abstracts or even just the name of the presenter and the title of the presentation. This is especially true of poster presentations , which really are large graphic posters (which don't translate well to either printed books or computer monitors).

As the name implies, most technical reports are about research in the physical sciences or engineering. However, there are also technical reports produced in the life and social sciences,

article and research

Like conference papers , some technical reports are eventually transformed into academic journal articles , but they may also be released after a journal article to provide supplementary data that didn't fit within the article. Also like conference papers, technical reports can be hard to find , especially older reports which may only be available in microfiche . Ask for help from a librarian!

Anthologies are a cross-over example. They're books that contain articles (chapters). Anthologies may be collections of articles by a single author, or collections of articles on a theme from different authors chosen by an editor. Many anthologies reprint articles already published elsewhere, but some contain original works.

Anthologies are rarely peer-reviewed, but they still may be considered scholarly works, depending on the reputation of the authors and editors. Use the same criteria listed for scholarly books .

Of course, reprints of articles originally published in peer-reviewed journals retain their "scholarly" status. (Note that most style manuals have special rules for citing reprinted works.)

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Difference between Paper and Article for scientific writings

As I know, in most of situations (in scientific context) these two terms are used to point to same thing and even they are used interchangeably.

For example,

Theory of value with public goods: A survey article
A survey paper on cloud computing

Are there any major differences between them? and can we use them interchangeably in any context?

  • differences

Mari-Lou A's user avatar

  • 1 See also: article vs paper –  Martin Thoma Commented Nov 24, 2019 at 11:46

3 Answers 3

The following extract helps understand the difference between a research article and a research paper :

Research paper and research articles are pieces of writing that require critical analysis, inquiry, insight, and demonstration of some special skills from students and scientists. It is really overwhelming for students when their teachers ask them to write a research paper as a form of assignment. Students remain confused between a research paper and a research article because of their similarities. This article attempts to find out if the two terms are synonymous or there is any difference between the two.

Research Article

What do you do when you are a scientist or a scholar and have arrived at a solution to a problem or have made a discovery that you want to share with the world? Well, one of the best ways to let the world know about your piece of wisdom or knowledge is through a research article. This is a piece of writing that contains an original research idea with the relevant data and findings Research article is published in renowned scientific journals that are involved with works in the area to which the paper pertains. A research article is a paper or writing that informs people of a path breaking research or a finding with clinical data to support the finding.

Research Paper

Research is an activity that is given much importance in academics, and this is why assignments requiring research and technical writing start early in the school. Students are asked to submit a research paper as early as in High School, and they become used to the concept when they are pursuing higher studies in colleges. However, a research paper is not just these assignment papers written by students as those written by scholars and scientists and published in journals are also referred to as research papers.
  • What is the difference between Research Article and Research Paper?
• There is no difference as such between a research article and a research paper and both involve original research with findings. • There is a trend to refer to term papers and academic papers written by students in colleges as research papers whereas articles submitted by scholars and scientists with their groundbreaking research are termed as research articles. • Research articles are published in renowned scientific journals whereas papers written by students do not go to journals.

(www.differencebetween.com)

There is no definitive distinction between papers and articles that can be applied to all scientific disciplines. Usage varies between disciplines. and within disciplines it can vary depending on context.

Both the examples quoted refer to ‘writings’ that are surveys (in other areas often termed reviews) — one in the field of a social science (economics) and the other in a numerical science (computing). However the term science is also (and perhaps more) associated with the experimental sciences (physics, chemistry and biology), where the types of ‘writings’ are different and where different words are used to distinguish them.

Articles and papers in the Experimental Sciences

Let me illustrate this for the Biomolecular Sciences (biochemistry, molecular biology, molecular genetics and the like). As a practitioner in this area, when I hear these terms, e.g. talking to colleagues, I understand:

Paper : A report of a piece of experimental research work in which the original data presented by the authors was central to interpretation and conclusions regarding advancement of knowledge and understanding of the field. Article : A review or commentary in which the author was discussing the previously published work of others (perhaps including his own) in attempting to provide a perspective of the field or to present a new theory/model/interpretation by integrating such work.

However, despite this professional conversational use of the terms, if I go to any specific journal — here the US heavyweight, Journal of Biological Chemistry (JBC) — I would find a somewhat different usage:

JBC publishes several types of articles but only two of those can be submitted as an unsolicited manuscript: regular papers and accelerated communications.

Thus, JBC regards all the ‘writings’ it publishes as ‘articles’, in common with other journals such as The Journal of Biophysics , and this is consistent with general non-scientific usage — “I read an article in the Financial Times yesterday…”

The way JBC uses ‘regular paper’, is consistent with my specialist conversational definition (above), and although it doesn’t actually say what types of ‘article’ are unsolicited, but if you look at a table of contents of the journal , you would conclude that for this journal it is ‘minireviews’ and historical appraisals of the work of individual scientists.

The Journal of Biophysics only uses the term ‘paper’ in describing only one of its categories of ‘article’:

Comments to the Editor | Short commentaries on a paper published earlier in BJ.

Again using ‘paper’ rather in the sense I defined above.

To conclude, in the extended sense used by peer-reviewed journals in the experimental sciences, all published ‘papers’ can be referred to as articles, but not all articles would be referred to as ‘papers’. (One wouldn’t use ‘paper’ for an editorial, a news item and generally not for a review.) This is exactly the opposite conclusion reached by @1006a from his reading of the OED.

Conflict with the OED and non-experimental sciences

How can one resolve the conflict with the OED, mentioned above? I think the OED describes more traditional usage in the non-experimental sciences and the arts. It is pertinent, in this respect, to consider the phrase “reading a paper” .

As far as my area of science goes, this is just a rather outdated term for presenting one’s results orally at a conference. The talk in itself is transitory, the abstract unreviewed, and the information conveyed will most probably be published elsewhere.

However for colleagues in computing science the talk is likely to be based on a ‘paper’ that has been submitted to the conference organisers, selected after peer-review, and will be published as conference proceedings or in a journal associated with the conference. This is more in line with traditional non-scientific academic presentations, although in this case the ‘paper’ might never have been published.

The difference would seem to derive in part from whether the field of science is one in which original work is in the form of ideas or in the form of measurements and their interpretation.

The distinction I would make is that an article is formally published, generally in some kind of periodical. The relevant definition, from Oxford Dictionaries:

A piece of writing included with others in a newspaper, magazine, or other publication.

Scholarly/scientific/research articles are thus "pieces of writing included with others in" an appropriate publication, most often an academic journal (see Wikipedia).

A paper , on the other hand, may or may not be published anywhere; and if it is published, may be in some alternate venue like conference proceedings (though it can be published in a scholarly journal). Again from Oxford:

An essay or dissertation, especially one read at an academic lecture or seminar or published in an academic journal.

So you can generally call any scientific (research) article a paper, but not all papers are articles.

Edited to clarify the last sentence, to which I also added the parenthetical (research):

Of course, not all articles are scientific (or research ) articles; that distinction generally means that the article presents original research, and as I am using it, that it has met certain standards of whichever field it represents (usually some form of peer review) so that it can be published in a scientific/scholarly journal. A scientific (research) paper meets the first of these criteria, but not necessarily the second (it presents original research, but may or may not be published). There are other kinds of articles/papers, which would ordinarily get a different modifier, like review or meta-review (or newspaper/magazine etc. for articles), or might commonly go by other terms altogether, like essay .

By this definition, not all articles are papers, and not all papers are articles, but all scientific (research) articles are also scientific (research) papers.

1006a's user avatar

  • Just to mention that in my consideration of experimental sciences I present the opposite conclusion from that you draw from the OED. Please don't think I am saying you are wrong, but as I explain, that your assertions only hold for certain areas of science. –  David Commented Jul 15, 2017 at 22:27
  • @David The key distinction I make is that articles are published . That would, indeed, include things like (literature) review articles, commentary, and possibly book reviews. It does not exclude original research in any field of which I am aware (which includes "experimental science"). It is certainly possible that certain disciplines or specific journals have non-standard usages, but I don't believe it breaks down along "experimental" and "non-experimental" lines. –  1006a Commented Jul 16, 2017 at 16:38
  • I agree about there being a difference in relation to publication. The whole background of "reading a paper" implies it can exist without being published, and even in the experimental sciences one might say "I wrote a paper about 'whatever' and sent it to such-and-such a Journal, but they rejected it because the referees were too stupid to understand it". You might feasibly say that about an article (I once had a solicited mini-review rejected because it was thought to be in bad taste) but it would be unusual. But a very popular program for storing PDFs of publications is called... "Papers". –  David Commented Jul 16, 2017 at 16:53

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Research Method

Home » Review Article vs Research Article

Review Article vs Research Article

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Review Article vs Research Article

Review articles and Research Articles are two different types of scholarly publications that serve distinct purposes in the academic literature.

Research Articles

A Research Article is a primary source that presents original research findings based on a specific research question or hypothesis. These articles typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections. Research articles often include detailed descriptions of the research design, data collection and analysis procedures, and the results of statistical tests. These articles are typically peer-reviewed to ensure that they meet rigorous scientific standards before publication.

Review Articles

A Review Article is a secondary source that summarizes and analyzes existing research on a particular topic or research question. These articles provide an overview of the current state of knowledge on a particular topic, including a critical analysis of the strengths and limitations of previous research. Review articles often include a meta-analysis of the existing literature, which involves combining and analyzing data from multiple studies to draw more general conclusions about the research question or topic. Review articles are also typically peer-reviewed to ensure that they are comprehensive, accurate, and up-to-date.

Difference Between Review Article and Research Article

Here are some key differences between review articles and research articles:

AspectResearch ArticleReview Article
Present original research findings based on a research question or hypothesisSummarize and analyze existing research on a particular topic or research question
Standard sections including an introduction, literature review, methodology, results, discussion, and conclusionDepends on the journal and topic, but typically includes an introduction, methods, results, discussion, and conclusion
Describe the research design, data collection and analysis procedures, and results of statistical testsDescribe the methodology used to identify and analyze the literature
Statistical analysis of dataMeta-analysis or systematic review of existing literature
Presents original data collected through researchDoes not present original data, but rather synthesizes and analyzes existing data
Based on the results of the research conductedBased on the analysis of existing literature
Peer-reviewed to ensure that they meet rigorous scientific standards before publicationPeer-reviewed to ensure that they are comprehensive, accurate, and up-to-date

In summary, research articles and review articles serve different purposes in the academic literature. Research articles present original research findings based on a specific research question or hypothesis, while review articles summarize and analyze existing research on a particular topic or research question. Both types of articles are typically peer-reviewed to ensure that they meet high standards of scientific rigor and accuracy.

Also see Research Methods

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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article and research

Difference between Research Paper and Research Article

Difference between Research Paper and Research Article

Research paper and research articles are bits of composing that require inquiry, critical analysis, demonstration and insight of few special abilities from understudies and researchers. This article endeavors to see whether the two terms are synonymous or there is any contrast between the two.

Research paper

Research can be said as activity which is specified much significance in scholastics. Be that as it may, research papers are not only these task papers composed by understudies as those composed by scholars and researchers and also published in different journals are additionally alluded to as research papers.

Research Article

Research article is a bit of composing that have original research thought with the pertinent data and discoveries. A research article is a composing or paper that advises individuals of a way breaking a finding or research with data to bolster the finding.

Research Paper VS Research Article

 There is a pattern to allude to academic papers and term papers composed by understudies in schools as a research paper

The articles presented by researchers and scholars with their noteworthy examination are known as research articles.

Research papers composed by the students mostly not take in journals.

Research articles composed by researchers or scholars mostly published in prestigious scientific journals.

A research paper depends on the original research. The sort of research may fluctuate, contingent upon your field or topics that include survey, experiments, questionnaire, interview and so on; yet authors require gathering and investigating raw data and make an original and real study. The research paper will be founded on the investigation and understanding of this raw data.

A research article depends on other different published articles. It is usually not depend on original study. Research articles for the most part condense the current writing on a point trying to clarify the present condition of comprehension on topic.

A research paper can be said as the primary source that means, it studies the techniques and consequences of original study performed by the writers.

A research article can be said as secondary source that means it is composed about different articles, and does not studies actual research of its own.

  • Importance:

In research paper, every part of this has its own importance. A concise is important in light of the fact that it shows that the writers know about existing literature, and want to add to this presented research definitively. A methods part is usually detailed and it is important in a way that different analysts have the capacity to check and/or duplicate these strategies. A result segment depicts the results of the analysis.

Research articles can be considered very important because they describe upon different articles that they analyze to propose new research bearings, to give powerful support for presented theories or distinguish designs among presented research studies. For understudy analysts, these research articles give an excellent review of presented literature on that topic. In the event that you discover a literature review that can be fit in study, investigate its references/works referred to list for guide on other articles.

From the above article we can conclude that research paper is the primary source whereas research articles are secondary.

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17 Comments Already

good article but which of them is more useful when we conduct a research

both. but research paper is more useful.

Nice explanation

There is a little difference but both are different.

Nice but i have a confusion that can a guys of Bachelors level can write Research Papers?

YEs they can if they do research project instead of development project and do something new in their project.

Thank you 😊

do you have something in your mind then please share with us. We will appreciate that.

Though it may be fairly easy to learn to speak English well enough to be understood, learning to write English correctly is very difficult, as this article so clearly illustrates. Though I greatly admire all those who are making an effort to learn another language, like English, as a non-native speaker, it is wrong for these same individuals to assume they can write English well enough to publish articles.

This article is so poorly written that I cannot understand most of it. For instance, the following phrases are utter nonsense: “A research paper can be said as the primary source that means,” — “A concise is important in light of the fact that it shows that . . .” — “A methods part is usually detailed” — “A result segment depicts the results . . .” — “they describe upon different articles that they analyze to propose new research bearings . . . or distinguish designs among presented . .. studies” — “to clarify the present condition of comprehension” — “Research papers and . . . articles require inquiry, critical analysis, demonstration and insight of few special abilities from . . .”

This article also states that “[a] research article . . . is usually not depend (sic) on original study,” then contradicts that in the next sentence with “[r]esearch articles . . . condense the current writing on a point . . .” Most studies these days are current. But, even if a study was conducted 50 years ago, it’s a cardinal rule that one should always use the original source of information rather than relying on the articles of other authors who may have misquoted something from the original study.

Articles like this one do a grave disservice to the viewing and researching public. To present this article as informative is disingenuous. To ask people who are seeking useful information to struggle with reading and trying to make sense of this poor English is so unkind and inconsiderate that I feel compelled to bring it to the author’s and publisher’s attention.

I would be honored to help anyone with their efforts to write English, but, please, be honest with yourselves about your lack of knowledge, so you will cease and desist the writing of anything online until your English skills have improved significantly. Thank you.

Thanks for such a detail input. Best wishes.

Yes you are saying right. So if you have the skills to deliver the answer in an efficient manner so kindly type it for me. Because I really want to know the difference between research paper and research article

Yes I agree with Martha. I myself found difficulty in going through the article. Although the topic is very important to be discussed because being the student of graduate, I must know the difference. But the way of delivering has dispirited me that now what other website should I visit to get accurate answer.

we need Published example of a scientific research article and another for a scientific research

how can I cite this?

“Difference between Research Paper and Research Article”, Reserachpedia.info, https://researchpedia.info/difference-between-research-paper-and-research-article/ , [27 December 2021].

I don’t understand anything. I am confused more than i came. Otehrwise, thank you for a trial. Simplify this communication.

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Exploring the Difference between Research Papers and Articles

Research papers and articles are common forms of academic writing that have distinct differences. This article explores the various components that distinguish a research paper from an article, including purpose, audience, structure, and content. Moreover, it examines how these factors vary depending on context and provides tips for successful composition in each genre. Ultimately, by understanding the key distinctions between research papers and articles—as well as when to apply them appropriately—scholars can craft high-quality works that effectively communicate their ideas.

I. Introduction

Ii. definition of research papers and articles, iii. overview of research papers vs articles, iv. reasons for writing research papers and articles, v. elements included in a research paper or article, vi. different types of sources used to write research papers/articles, vii conclusion.

A brief overview The concept of research papers and articles is one that has been around for centuries. For those who have a basic understanding, they may think that the two terms are interchangeable – however this is far from true! A research paper involves an in-depth analysis and evaluation of scholarly literature on a particular topic. It often requires multiple stages of research to be completed before the paper can be written, as well as requiring significant knowledge within the specific subject area it pertains to. On the other hand, an article usually takes much less time to complete than does a research paper; while still involving some degree of examination or investigation into certain topics or ideas related to its field, it tends not to go as deep into detail compared with more involved studies such as those associated with most academic courses.

Analytical components When creating either type of writing project there will always exist numerous analytical components – including referencing material from both primary (firsthand) sources such as interviews/surveys etc., and secondary (background) sources which includes journal articles/books etc. Depending on what kind of work is being done these references must allude towards different kinds critical thinking – whether qualitative (describing qualities), quantitative (measuring amount) or mixed methods (combining above). Furthermore when considering specific topics further exploration should also consider if deductive reasoning applies alongside inductive reasoning in order for appropriate conclusions regarding content matter at hand can be drawn up correctly by synthesising material accessed throughout entire composition process mentioned earlier.

Research papers and articles are integral to academia, but they often have different definitions. A research paper is a form of academic writing that has theoretical and substantial information regarding a certain topic. It requires the thorough investigation of sources and it typically follows an established structure: introduction, body paragraphs, conclusion. Additionally, research papers can include extensive quotes or references from other publications as part of their evidence-based claims.

In contrast to a research paper, an article , generally speaking, presents the author’s own opinion on some subject matter without necessarily having any formalized structure or referencing system in place. Articles are usually found in magazines such as Time Magazine or The Economist; newspapers like The New York Times or USA Today; scholarly journals such as American Journal for Social Science Research; and online blogs. In many cases authors tend to incorporate personal anecdotes which may not be present within more conventional forms of academic writing (e.g., journaling).

Research Papers vs Articles It is important to recognize the differences between research papers and articles when considering which type of content should be written. Research papers generally require extensive amounts of data collection, analysis, and synthesis from a variety of sources; they are written in an academic or scientific format that follows certain conventions. On the other hand, articles tend to focus more on opinion-based pieces with personal experience anecdotes as their main source material. Additionally, while both types often cite similar resources for reference information (such as peer reviewed journals), it is typically not necessary for an article’s citations to meet the same standards as those used in a research paper.

Another key difference between research papers and articles lies within their overall goal – while most research papers aim to answer specific questions about a subject matter by providing credible evidence through rigorous study methods, many articles are simply meant to inform readers on topics without necessarily drawing any conclusions or pushing any particular agenda/viewpoint. This makes them more suitable for use cases such as educational publications where concise explanations may be preferred over longer analyses into complex theories and ideas.

Benefits of Scholarly Writing:

Writing research papers and articles can offer a variety of benefits to scholars. By delving into an area of study, the researcher is able to obtain greater understanding and clarity about their subject matter. Through this type of writing, one has the ability to demonstrate their knowledge and comprehension while presenting compelling arguments that will support or refute existing theories within a specific field. In addition, research-based publications are essential for individuals wishing to gain recognition as experts in their respective fields.

Another benefit offered by scholarly writing is its potential impact on society at large; it allows ideas and evidence from various disciplines – such as medicine, psychology or economics –  to be integrated together thus providing holistic solutions addressing complex problems our world faces today. This also provides opportunities for different groups of people with diverse backgrounds to learn from each other which may lead to mutual understanding between them.

Difference Between Research Papers & Articles: Although both types require rigorous investigation prior to producing any kind content there are key differences between these two types academic works. Generally speaking, a research paper , requires more comprehensive data collection than that expected in articles due its lengthier format allowing for more detailed information regarding the topic being discussed including background history along with interpretation results provided by primary sources used throughout the paper’s body text sections itself.

. On contrary , an article tends lean towards shorter length publication requiring less detail although same investigative process still remains necessary followed further revision carried out prior submitting draft editorial board review consideration where they then decide if ready publishing under relevant journal name associated specified discipline field .

When writing research papers or articles, the elements included in them can vary depending on the nature of the work. However, there are some general components which usually appear regardless of field.

  • Abstract: An abstract is a brief summary which outlines what is discussed and presents main conclusions drawn from it. It’s also used to introduce readers to an article before they read it in full.

The difference between a research paper and an article comes down to purpose. Research papers often delve into complex theories whereas articles are intended more for informing people about topics in simple terms so that they can apply this knowledge practically. Articles generally require less background information than research papers as well as being shorter overall.

  • Introduction: The introduction should be catchy enough to attract interest but not contain too much detail; its job is simply to provide context for your project by summarising what you plan to do in order words.

In addition, while both types include references/citations at the end (which cite sources used), these tend to be formatted differently due – again –to their differing purposes; academic journals have different requirements than books or magazines when citing sources because such publications deal with more complex material involving specialized terminology etc.

When writing a research paper or article, it is essential to understand the various types of sources that can be used. There are three main categories of sources – primary, secondary and tertiary – each with their own characteristics and benefits for authors:

  • Primary Sources : These are original materials on which other research may be based. They usually include documents created by an author who had direct personal knowledge of an event or topic (e.g., diaries, letters). Primary source material also includes photographs, audio recordings, interviews and surveys.
  • Secondary Sources : These refer to materials that have been already analyzed and interpreted by another researcher(s) (e.g., books analyzing the works of one author). Secondary source materials can also include biographies written about someone else’s life as well as journal articles.
  • Tertiary Sources : Tertiary sources summarize or organize information from primary and secondary sources such as dictionaries, textbooks, encyclopedias or directories.

Authors should use multiple types of sources when composing both research papers and articles in order to provide evidence-based conclusions backed up by reliable data points from different perspectives. For example if researching a historical personage then combining a biography book outlining their achievements with records from archives could create well rounded opinion piece regarding this individual’s legacy in history. It is important for authors to note that while most elements remain consistent between both assignments there exists some subtle differences; namely -for instance- whilst citing 2+ primary resources within academic pieces tends encourage credibility towards any stated hypothesis so too does linking several relevant external publications make articles more persuasive/informative overall due its journalistic nature .

Drawing to a Close As we have seen in this paper, there are several differences between research papers and articles. Research papers delve deeply into the subject matter; often containing original research or data compiled from sources that support the author’s perspective. Articles tend to be more concise summaries of topics, usually lacking primary research or data analysis yet providing an overview of existing information on a topic.

On the other hand, both mediums can provide valuable insight for readers. The takeaway is simple: when seeking further details about particular subject matter it would be wise to look at both options before drawing any conclusions. Indeed, reading multiple points-of-view on a single topic may give us greater clarity regarding its complexity – something we could all benefit from in these times of polarization and uncertainty!

In conclusion, the primary distinction between research papers and articles lies in their purpose. Research papers are typically longer in length and more comprehensive than articles as they seek to answer a specific hypothesis or question through an extensive analysis of the available evidence. Articles tend to focus on one particular issue or topic from an opinion-based perspective, rather than conducting original research into a subject. Regardless of whether you’re writing for academic publication or professional purposes, understanding these two forms can help ensure that your content meets its intended audience’s expectations effectively.

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A technique for more effective multipurpose robots

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Four photos show, on top level, a simulation of a robot hand using a spatula, knife, hammer and wrench. The second row shows a real robot hand performing the tasks, and the bottom row shows a human hand performing the tasks.

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Four photos show, on top level, a simulation of a robot hand using a spatula, knife, hammer and wrench. The second row shows a real robot hand performing the tasks, and the bottom row shows a human hand performing the tasks.

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Let’s say you want to train a robot so it understands how to use tools and can then quickly learn to make repairs around your house with a hammer, wrench, and screwdriver. To do that, you would need an enormous amount of data demonstrating tool use.

Existing robotic datasets vary widely in modality — some include color images while others are composed of tactile imprints, for instance. Data could also be collected in different domains, like simulation or human demos. And each dataset may capture a unique task and environment.

It is difficult to efficiently incorporate data from so many sources in one machine-learning model, so many methods use just one type of data to train a robot. But robots trained this way, with a relatively small amount of task-specific data, are often unable to perform new tasks in unfamiliar environments.

In an effort to train better multipurpose robots, MIT researchers developed a technique to combine multiple sources of data across domains, modalities, and tasks using a type of generative AI known as diffusion models.

They train a separate diffusion model to learn a strategy, or policy, for completing one task using one specific dataset. Then they combine the policies learned by the diffusion models into a general policy that enables a robot to perform multiple tasks in various settings.

In simulations and real-world experiments, this training approach enabled a robot to perform multiple tool-use tasks and adapt to new tasks it did not see during training. The method, known as Policy Composition (PoCo), led to a 20 percent improvement in task performance when compared to baseline techniques.

“Addressing heterogeneity in robotic datasets is like a chicken-egg problem. If we want to use a lot of data to train general robot policies, then we first need deployable robots to get all this data. I think that leveraging all the heterogeneous data available, similar to what researchers have done with ChatGPT, is an important step for the robotics field,” says Lirui Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on PoCo .      

Wang’s coauthors include Jialiang Zhao, a mechanical engineering graduate student; Yilun Du, an EECS graduate student; Edward Adelson, the John and Dorothy Wilson Professor of Vision Science in the Department of Brain and Cognitive Sciences and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of CSAIL. The research will be presented at the Robotics: Science and Systems Conference.

Combining disparate datasets

A robotic policy is a machine-learning model that takes inputs and uses them to perform an action. One way to think about a policy is as a strategy. In the case of a robotic arm, that strategy might be a trajectory, or a series of poses that move the arm so it picks up a hammer and uses it to pound a nail.

Datasets used to learn robotic policies are typically small and focused on one particular task and environment, like packing items into boxes in a warehouse.

“Every single robotic warehouse is generating terabytes of data, but it only belongs to that specific robot installation working on those packages. It is not ideal if you want to use all of these data to train a general machine,” Wang says.

The MIT researchers developed a technique that can take a series of smaller datasets, like those gathered from many robotic warehouses, learn separate policies from each one, and combine the policies in a way that enables a robot to generalize to many tasks.

They represent each policy using a type of generative AI model known as a diffusion model. Diffusion models, often used for image generation, learn to create new data samples that resemble samples in a training dataset by iteratively refining their output.

But rather than teaching a diffusion model to generate images, the researchers teach it to generate a trajectory for a robot. They do this by adding noise to the trajectories in a training dataset. The diffusion model gradually removes the noise and refines its output into a trajectory.

This technique, known as Diffusion Policy , was previously introduced by researchers at MIT, Columbia University, and the Toyota Research Institute. PoCo builds off this Diffusion Policy work. 

The team trains each diffusion model with a different type of dataset, such as one with human video demonstrations and another gleaned from teleoperation of a robotic arm.

Then the researchers perform a weighted combination of the individual policies learned by all the diffusion models, iteratively refining the output so the combined policy satisfies the objectives of each individual policy.

Greater than the sum of its parts

“One of the benefits of this approach is that we can combine policies to get the best of both worlds. For instance, a policy trained on real-world data might be able to achieve more dexterity, while a policy trained on simulation might be able to achieve more generalization,” Wang says.

Because the policies are trained separately, one could mix and match diffusion policies to achieve better results for a certain task. A user could also add data in a new modality or domain by training an additional Diffusion Policy with that dataset, rather than starting the entire process from scratch.

The researchers tested PoCo in simulation and on real robotic arms that performed a variety of tools tasks, such as using a hammer to pound a nail and flipping an object with a spatula. PoCo led to a 20 percent improvement in task performance compared to baseline methods.

“The striking thing was that when we finished tuning and visualized it, we can clearly see that the composed trajectory looks much better than either one of them individually,” Wang says.

In the future, the researchers want to apply this technique to long-horizon tasks where a robot would pick up one tool, use it, then switch to another tool. They also want to incorporate larger robotics datasets to improve performance.

“We will need all three kinds of data to succeed for robotics: internet data, simulation data, and real robot data. How to combine them effectively will be the million-dollar question. PoCo is a solid step on the right track,” says Jim Fan, senior research scientist at NVIDIA and leader of the AI Agents Initiative, who was not involved with this work.

This research is funded, in part, by Amazon, the Singapore Defense Science and Technology Agency, the U.S. National Science Foundation, and the Toyota Research Institute.

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  • Edward Adelson
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  • Computer Science and Artificial Intelligence Laboratory
  • Department of Electrical Engineering and Computer Science
  • Department of Mechanical Engineering
  • Department of Aeronautics and Astronautics
  • Department of Brain and Cognitive Sciences

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  • Artificial intelligence
  • Computer science and technology
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  • Computer Science and Artificial Intelligence Laboratory (CSAIL)
  • Electrical Engineering & Computer Science (eecs)
  • Aeronautical and astronautical engineering
  • Brain and cognitive sciences
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NVIDIA Research Wins CVPR Autonomous Grand Challenge for End-to-End Driving

Making moves to accelerate self-driving car development, NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, running this week in Seattle.

Building on last year’s win in 3D Occupancy Prediction , NVIDIA Research topped the leaderboard this year in the End-to-End Driving at Scale category with its Hydra-MDP model, outperforming more than 400 entries worldwide.

This milestone shows the importance of generative AI in building applications for physical AI deployments in autonomous vehicle (AV) development. The technology can also be applied to industrial environments, healthcare, robotics and other areas.

The winning submission received CVPR’s Innovation Award as well, recognizing NVIDIA’s approach to improving “any end-to-end driving model using learned open-loop proxy metrics.”

In addition, NVIDIA announced NVIDIA Omniverse Cloud Sensor RTX , a set of microservices that enable physically accurate sensor simulation to accelerate the development of fully autonomous machines of every kind.

How End-to-End Driving Works

The race to develop self-driving cars isn’t a sprint but more a never-ending triathlon, with three distinct yet crucial parts operating simultaneously: AI training, simulation and autonomous driving. Each requires its own accelerated computing platform, and together, the full-stack systems purpose-built for these steps form a powerful triad that enables continuous development cycles, always improving in performance and safety.

To accomplish this, a model is first trained on an AI supercomputer such as NVIDIA DGX . It’s then tested and validated in simulation — using the NVIDIA Omniverse platform and running on an NVIDIA OVX system — before entering the vehicle, where, lastly, the NVIDIA DRIVE AGX platform processes sensor data through the model in real time.

Building an autonomous system to navigate safely in the complex physical world is extremely challenging. The system needs to perceive and understand its surrounding environment holistically, then make correct, safe decisions in a fraction of a second. This requires human-like situational awareness to handle potentially dangerous or rare scenarios.

AV software development has traditionally been based on a modular approach, with separate components for object detection and tracking, trajectory prediction, and path planning and control.

End-to-end autonomous driving systems streamline this process using a unified model to take in sensor input and produce vehicle trajectories, helping avoid overcomplicated pipelines and providing a more holistic, data-driven approach to handle real-world scenarios.

Watch a video about the Hydra-MDP model, winner of the CVPR Autonomous Grand Challenge for End-to-End Driving:

Navigating the Grand Challenge 

This year’s CVPR challenge asked participants to develop an end-to-end AV model, trained using the nuPlan dataset, to generate driving trajectory based on sensor data.

The models were submitted for testing inside the open-source NAVSIM simulator and were tasked with navigating thousands of scenarios they hadn’t experienced yet. Model performance was scored based on metrics for safety, passenger comfort and deviation from the original recorded trajectory.

NVIDIA Research’s winning end-to-end model ingests camera and lidar data, as well as the vehicle’s trajectory history, to generate a safe, optimal vehicle path for five seconds post-sensor input.

The workflow NVIDIA researchers used to win the competition can be replicated in high-fidelity simulated environments with NVIDIA Omniverse. This means AV simulation developers can recreate the workflow in a physically accurate environment before testing their AVs in the real world. NVIDIA Omniverse Cloud Sensor RTX microservices will be available later this year. Sign up for early access.

In addition, NVIDIA ranked second for its submission to the CVPR Autonomous Grand Challenge for Driving with Language . NVIDIA’s approach connects vision language models and autonomous driving systems, integrating the power of large language models to help make decisions and achieve generalizable, explainable driving behavior.

Learn More at CVPR 

More than 50 NVIDIA papers were accepted to this year’s CVPR, on topics spanning automotive, healthcare, robotics and more. Over a dozen papers will cover NVIDIA automotive-related research, including:

  • Winner of CVPR’s End-to-End Driving at Scale challenge
  • Read the NVIDIA technical blog
  • CVPR best paper award finalist
  • See DRIVE Labs: LLM-Based Road Rules Guide Simplifies Driving
  • Is Ego Status All You Need for Open-Loop End-to-End Autonomous Driving?
  • Improving Distant 3D Object Detection Using 2D Box Supervision
  • Dynamic LiDAR Resimulation Using Compositional Neural Fields
  • BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection
  • PARA-Drive: Parallelized Architecture for Real-Time Autonomous Driving

Sanja Fidler, vice president of AI research at NVIDIA, will speak on vision language models at the CVPR Workshop on Autonomous Driving .

Learn more about NVIDIA Research , a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.

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