How Learning Works
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How Learning Works

Seven Research-Based Principles for Smart Teaching

Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, Marie K. Norman

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eBook - ePub

How Learning Works

Seven Research-Based Principles for Smart Teaching

Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, Marie K. Norman

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Praise for How Learning Works

" How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning."
— Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching

"This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching."
— Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education

"Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues."
— Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching

"As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book."
— From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning

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How Does Students’ Prior Knowledge Affect Their Learning?
But They Said They Knew This!
I recently taught Research Methods in Decision Sciences for the first time. On the first day of class, I asked my students what kinds of statistical tests they had learned in the introductory statistics course that is a prerequisite for my course. They generated a fairly standard list that included T-tests, chi-square, and ANOVA. Given what they told me, I was pretty confident that my first assignment was pitched at the appropriate level; it simply required that students take a data set that I provided, select and apply the appropriate statistical test from those they had already learned, analyze the data, and interpret the results. It seemed pretty basic, but I was shocked at what they handed in. Some students chose a completely inappropriate test while others chose the right test but did not have the foggiest idea how to apply it. Still others could not interpret the results. What I can’t figure out is why they told me they knew this stuff when it’s clear from their work that most of them don’t have a clue.
Professor Soo Yon Won
Why Is This So Hard for Them to Understand?
Every year in my introductory psychology class I teach my students about classic learning theory, particularly the concepts of positive and negative reinforcement. I know that these can be tough concepts for students to grasp, so I spell out very clearly that reinforcement always refers to increasing a behavior and punishment always refers to decreasing a behavior. I also emphasize that, contrary to what they might assume, negative reinforcement does not mean punishment; it means removing something aversive to increase a desired behavior. I also provide a number of concrete examples to illustrate what I mean. But it seems that no matter how much I explain the concept, students continue to think of negative reinforcement as punishment. In fact, when I asked about negative reinforcement on a recent exam, almost 60 percent of the class got it wrong. Why is this so hard for students to understand?
Professor Anatole Dione
The instructors in these stories seem to be doing all the right things. Professor Won takes the time to gauge students’ knowledge of statistical tests so that she can pitch her own instruction at the appropriate level. Professor Dione carefully explains a difficult concept, provides concrete examples, and even gives an explicit warning about a common misconception. Yet neither instructor’s strategy is having the desired effect on students’ learning and performance. To understand why, it is helpful to consider the effect of students’ prior knowledge on new learning.
Professor Won assumes that students have learned and retained basic statistical skills in their prerequisite course, an assumption that is confirmed by the students’ self-report. In actuality, although students have some knowledge—they are able to identify and describe a variety of statistical tests—it may not be sufficient for Professor Won’s assignment, which requires them to determine when particular tests are appropriate, apply the right test for the problem, and then interpret the results. Here Professor Won’s predicament stems from a mismatch between the knowledge students have and the knowledge their instructor expects and needs them to have to function effectively in her course.
In Professor Dione’s case it is not what students do not know that hurts them but rather what they do know. His students, like many of us, have come to associate positive with “good” and negative with “bad,” an association that is appropriate in many contexts, but not in this one. When students are introduced to the concept of negative reinforcement in relation to classic learning theory, their prior understanding of “negative” may interfere with their ability to absorb the technical definition. Instead of grasping that the “negative” in negative reinforcement involves removing something to get a positive change (an example would be a mother who promises to quit nagging if her son will clean his room), students interpret the word “negative” to imply a negative response, or punishment. In other words, their prior knowledge triggers an inappropriate association that ultimately intrudes on and distorts the incoming knowledge.
As we teach, we often try to enhance our students’ understanding of the course content by connecting it to their knowledge and experiences from earlier in the same course, from previous courses, or from everyday life. But sometimes—like Professor Won—we overestimate students’ prior knowledge and thus build new knowledge on a shaky foundation. Or we find—like Professor Dione—that our students are bringing prior knowledge to bear that is not appropriate to the context and which is distorting their comprehension. Similarly, we may uncover misconceptions and inaccuracies in students’ prior knowledge that are actively interfering with their ability to learn the new material.
Although, as instructors, we can and should build on students’ prior knowledge, it is also important to recognize that not all prior knowledge provides an equally solid foundation for new learning.
Principle: Students’ prior knowledge can help or hinder learning.
Students do not come into our courses as blank slates, but rather with knowledge gained in other courses and through daily life. This knowledge consists of an amalgam of facts, concepts, models, perceptions, beliefs, values, and attitudes, some of which are accurate, complete, and appropriate for the context, some of which are inaccurate, insufficient for the learning requirements of the course, or simply inappropriate for the context. As students bring this knowledge to bear in our classrooms, it influences how they filter and interpret incoming information.
Ideally, students build on a foundation of robust and accurate prior knowledge, forging links between previously acquired and new knowledge that help them construct increasingly com­plex and robust knowledge structures (see Chapter Two). However, students may not make connections to relevant prior knowledge spontaneously. If they do not draw on relevant prior knowledge—in other words, if that knowledge is inactive—it may not facilitate the integration of new knowledge. Moreover, if students’ prior knowledge is insufficient for a task or learning situation, it may fail to support new knowledge, whereas if it is inappropriate for the context or inaccurate, it may actively distort or impede new learning. This is illustrated in Figure 1.1.
Figure 1.1. Qualities of Prior Knowledge That Help or Hinder Learning
Understanding what students know—or think they know—coming into our courses can help us design our instruction more appropriately. It allows us not only to leverage their accurate knowledge more effectively to promote learning, but also to identify and fill gaps, recognize when students are applying what they know inappropriately, and actively work to correct misconceptions.
Students connect what they learn to what they already know, interpreting incoming information, and even sensory perception, through the lens of their existing knowledge, beliefs, and assumptions (Vygotsky, 1978; National Research Council, 2000). In fact, there is widespread agreement among researchers that students must connect new knowledge to previous knowledge in order to learn (Bransford & Johnson, 1972; Resnick, 1983). However, the extent to which students are able to draw on prior knowledge to effectively construct new knowledge depends on the nature of their prior knowledge, as well as the instructor’s ability to harness it. In the following sections, we discuss research that investigates the effects of various kinds of prior knowledge on student learning and explore its implications for teaching.
Activating Prior Knowledge
When students can connect what they are learning to accurate and relevant prior knowledge, they learn and retain more. In essence, new knowledge “sticks” better when it has prior knowledge to stick to. In one study focused on recall, for example, participants with variable knowledge of soccer were presented with scores from different soccer matches and their recall was tested. People with more prior knowledge of soccer recalled more scores (Morris et al., 1981). Similarly, research conducted by Kole and Healy (2007) showed that college students who were presented with unfamiliar facts about well-known individuals demonstrated twice the capacity to learn and retain those facts as students who were presented with the same number of facts about unfamiliar individuals. Both these studies illustrate how prior knowledge of a topic can help students integrate new information.
However, students may not spontaneously bring their prior knowledge to bear on new learning situations (see the discussion of transfer in Chapter Four). Thus, it is important to help students activate prior knowledge so they can build on it productively. Indeed, research suggests that even small instructional interventions can activate students’ relevant prior knowledge to positive effect. For instance, in one famous study by Gick and Holyoak (1980), college students were presented with two problems that required them to apply the concept of convergence. The researchers found that even when the students knew the solution to the first problem, the vast majority did not think to apply an analogous solution to the second problem. However, when the instructor suggested to students that they think about the second problem in relation to the first, 80 percent of the student participants were able to solve it. In other words, with minor prompts and simple reminders, instructors can activate relevant prior knowledge so that students draw on it more effectively (Bransford & Johnson, 1972; Dooling & Lachman, 1971).
Research also suggests that asking students questions specifically designed to trigger recall can help them use prior knowledge to aid the integration and retention of new information (Woloshyn, Paivio, & Pressley, 1994). For example, Martin and Pressley (1991) asked Canadian adults to read about events that had occurred in various Canadian provinces. Prior to any instructional intervention, the researchers found that study participants often failed to use their relevant prior knowledge to logically situate events in the provinces where they occurred, and thus had difficulty remembering specific facts. However, when the researchers asked a set of “why” questions (for example, “Why would Ontario have been the first place baseball was played?”), participants were forced to draw on their prior knowledge of Canadian history and relate it logically to the new information. The researchers found that this intervention, which they called elaborative interrogation, improved learning and retention significantly.
Researchers have also found that if students are asked to generate relevant knowledge from previous courses or their own lives, it can help to facilitate their integration of new material (Peeck, Van Den Bosch, & Kruepeling, 1982). For example, Garfield and her colleagues (Garfield, Del Mas, & Chance, 2007) designed an instructional study in a college statistics course that focused on the concept of variability—a notoriously difficult concept to grasp. The instructors first collected baseline data on students’ understanding of variability at the end of a traditionally taught course. The following semester, they redesigned the course so that students were asked to generate examples of activities in their own lives that had either high or low variability, to represent them graphically, and draw on them as they reasoned about various aspects of variability. While both groups of students continued to struggle with the concept, post-tests showed that students who had generated relevant prior knowledge outperformed students in the baseline class two to one.
Exercises to generate prior knowledge can be a double-edged sword, however, if the knowledge students generate is inaccurate or inappropriate for the context (Alvermann, Smith, & Readance, 1985). Problems involving inaccurate and inappropriate prior knowledge will be addressed in the next two sections.
Implications of This Research
Students learn more readily when they can connect what they are learning to what they already know. However, instructors should not assume that students will immediately or naturally draw on relevant prior knowledge. Instead, they should deliberately activate students’ prior knowledge to help them forge robust links to new knowledge.
Accurate but Insufficient Prior Knowledge
Even when students’ prior knowledge is accurate and activated, it may not be sufficient to support subsequent learning or a desired level of performance. Indeed, when students possess some relevant knowledge, it can lead both students and instructors to assume that students are better prepared than they truly are for a particular task or level of instruction.
In fact, there are many different types of knowledge, as evidenced by a number of typologies of knowledge (for example, Anderson & Krathwohl, 2001; Anderson, 1983; Alexander, Schallert, & Hare, 1991; DeJong & Ferguson-Hessler, 1996). One kind of knowledge that appears across many of these typologies is declarative knowledge, or the knowledge of facts and concepts that can be stated or declared. Declarative knowledge can be thought of as “knowing what.” The ability to name the parts of the circulatory system, describe the characteristics of hunter-gatherer social structure, or explain Newton’s Third Law are examples of declarative knowledge. A second type of knowledge is often referred to as procedural knowledge, because it involves knowing how and knowing when to apply various procedures, methods, theories, styles, or approaches. The ability to calculate integrals, draw with 3-D perspective, and calibrate lab equipment—as well as the knowledge of when these skills are and are not applicable—fall into the category of procedural knowledge.
Declarative and procedural knowledge are not the same, nor do they enable the same kinds of performance. It is common, for instance, for students to know facts and concepts but not know how or when to apply them. In fact, research on science learning demonstrates that even when students can state scientific facts (for example, “Force equals mass times acceleration”), they are often weak at applying those facts to solve problems, interpret data, and draw conclusions (Clement, 1982). We see this problem clearly in Professor Won’s class. Her students know what various statistical tests are, but this knowledge is insufficient for the task Professor Won has assigned, which requires them to select appropriate tests for a given data set, execute the statistical tests properly, and interpret the results.
Similarly, studies have shown that students can often perform procedural tasks without being able to articulate a clear understanding of what they are doing or why (Berry & Broadbent, 1988; Reber & Kotovsky, 1997; Sun, Merrill, & Peterson, 2001). For example, business students may be able to apply formulas to solve finance problems but not to explain their logic or the principles underlying their solutions. Similarly, design students may know how to execute a particular design without being able to explain or justify the choices they have made. These students may have sufficient procedural knowledge to function effectively in specific contexts, yet lack the declarative knowledge of deep features and principles that would allow them both to adapt to different contexts (see discussion of transfer in Chapter Three) and explain themselves to others.
Implications of This Research
Because knowing what is a very different kind of knowledge than knowing how or knowing when, it is especially important that, as instructors, we are clear in our own minds about the knowledge requirements of different tasks and that we not assume that because our students have one kind of knowledge that they have another. Instead, it is critical to assess both the amount and nature of students’ prior knowledge so that we can design our instruction appropriately.
Inappropriate Prior Knowledge
Under some circumstances, students draw on prior knowledge that is inappropriate for the learning context. Although this knowledge is not necessarily inaccurate, it can skew their comprehension of new material.
One situation in which prior knowledge can distort learning and performance is when students import everyday meanings into technical contexts. Several studies in statistics, for example, show how commonplace definitions of terms such as random and spread intrude in technical contexts, distorting students’ understandings of statistical concepts (Del Mas & Liu, 2007; Kaplan, Fisher, & Rogness, 2009). This seems to be the problem for Professor Dione’s students, whose everyday associations with the terms positive and negative may have skewed their understanding of negative reinforcement.
Another situation in which inappropriate prior knowledge can impede new learning is if students analogize from one situation to another without recognizing the limitations of the analogy. For the most part, analogies serve an important pedagogical function, allowing instructors to build on what students already know to help them understand complex, abstract, or unfamiliar concepts. However, problems can arise when students do not recognize where the analogy breaks down or fail to see the limitations of a simple analogy for describing a complex phenomenon. For example, skeletal muscles and cardiac muscles share some traits; hence, drawing analogies between them makes sense to a point. However, the differences in how these two types of muscles function are substantial and vital to understanding their normal operation, as well as for determining how to effectively intervene in a health crisis. In fact, Spiro and colleagues (Spiro et al., 1989) found that many medical students possess a misconception about...

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Stili delle citazioni per How Learning Works
APA 6 Citation
Ambrose, S., Bridges, M., DiPietro, M., Lovett, M., & Norman, M. (2010). How Learning Works (1st ed.). Wiley. Retrieved from (Original work published 2010)
Chicago Citation
Ambrose, Susan, Michael Bridges, Michele DiPietro, Marsha Lovett, and Marie Norman. (2010) 2010. How Learning Works. 1st ed. Wiley.
Harvard Citation
Ambrose, S. et al. (2010) How Learning Works. 1st edn. Wiley. Available at: (Accessed: 14 October 2022).
MLA 7 Citation
Ambrose, Susan et al. How Learning Works. 1st ed. Wiley, 2010. Web. 14 Oct. 2022.