In this section we offer (1) a set of strategies to help instructors determine the extent and quality of students ’ prior knowledge, relative to the learning requirements of a course. We then provide strategies instructors can employ to (2) activate students ’ relevant prior knowledge, (3) address gaps in students ’ prior knowledge, (4) help students avoid applying prior knowledge in the wrong contexts, and (5) help students revise and rethink inaccurate knowledge.
Methods to Gauge the Extent and Nature of Students ’ Prior Knowledge
Talk to Colleagues As a starting point for fi nding out what prior knowledge students bring to your course, talk to colleagues who teach prerequisite courses or ask to see their syllabi and assignments. This can give you a quick sense of what material was covered, and in what depth. It can also alert you to differences in approach, emphasis, terminology, and notation so that you can address potential gaps or discrepancies. Remember, though, that just because the material was taught does not mean that students necessarily learned it. To get a better sense of students ’ knowledge, as well as their ability to apply it, you might also ask your colleagues about students ’ profi ciencies: for example, what concepts and skills did students seem to master easily? Which ones did they struggle with? Did students seem to hold any systematic and pervasive misconceptions? This kind of information from colleagues can help you design your instructional activities so they effectively connect to, support, extend, and, if needed, correct, students ’ prior knowledge.
Administer a Diagnostic Assessment To fi nd out what relevant knowledge students possess coming into your course, consider assigning a short, low - stakes assessment, such as a quiz or an essay, at the beginning of the semester. Students ’ performance on this assignment can give you a sense of their knowledge of prerequisite facts and concepts, or their competence in various skills. For example, if your course requires knowledge of a technical vocabulary and basic calculus skills, you could create a short quiz asking students to defi ne terms and solve calculus problems. You can mark these assignments individually to get a sense of the skill and knowledge of particular students, or simply
look them over as a set to get a feel for students ’ overall level of preparedness.
Another way to expose students ’ prior knowledge is by administering a concept inventory. Concept inventories are ungraded tests, typically in a multiple - choice format, that are designed to include incorrect answers that help reveal common misconceptions. Developing a concept inventory of your own can be time -intensive, so check the Internet to see whether there are inventories already available in your discipline that would suit your needs. A number of concept inventories have been widely used and have high validity and reliability.
Have Students Assess Their Own Prior Knowledge In some fields and at some levels of expertise, having students assess their own knowledge and skills can be a quick and effective — though not necessarily foolproof — way to diagnose missing or insuffi cient prior knowledge. One way to have students self - assess is to create a list of concepts and skills that you expect them to have coming into your course, as well as some concepts and skills you expect them to acquire during the semester. Ask students to assess their level of competence for each concept or skill, using a scale that ranges from cursory familiarity ( “ I have heard of the term ” ) to factual knowledge ( “ I could defi ne it ” ) to conceptual knowledge ( “ I could explain it to someone else ” ) to application ( “ I can use it to solve problems ” ). Examine the data for the class as a whole in order to identify areas in which your students have either less knowledge than you expect or more. In either case, this information can help you recalibrate your instruction to better meet student needs.
Use Brainstorming to Reveal Prior Knowledge One way to expose students ’ prior knowledge is to conduct a group brainstorming session. Brainstorming can be used to uncover beliefs, associations, and assumptions (for example, with questions such as “ What do you think of when you hear the word evangelical ? ” ). It can also be used to expose factual or conceptual knowledge ( “ What were some of the key historical events in the Gilded Age? ” or “ What comes to mind when you think about environmental ethics? ” ), procedural knowledge ( “ If you were going to do a research project on the Farm Bill, where would you begin? ” ), or contextual knowledge ( “ What are some methodologies you could use to research this question? ” ). Bear in mind that brainstorming does not provide a systematic gauge of students ’ prior knowledge. Also, be prepared to differentiate accurate and appropriately applied knowledge from knowledge that is inaccurate or inappropriately applied.
Assign a Concept Map Activity To gain insights into what your students know about a given subject, ask them to construct a concept map representing everything that they know about the topic. You can ask students to create a concept map, representing what they know about an entire disciplinary domain (for example, social psychology), a particular concept (for instance, Newton ’ s third law), or a question (for example, “ What are the ethical issues with stem cell research? ” ). Some students may be familiar with concept maps, but others may not be, so be sure to explain what they are and how to create them (circles for concepts, lines between concepts to show how they relate). There are a number of ways to construct concept maps, so you should give some thought to what you are trying to ascertain. For instance, if you are interested in gauging students ’ knowledge of concepts as well as their ability to articulate the connections among them, you can ask students to generate both concepts and links. But if you are primarily interested in students ’ ability to articulate the connections, you can provide the list of concepts and ask students to arrange and connect them, labeling the links. If there are particular kinds of information you are looking for (for example, causal relationships, examples, theoretical orientations) be sure to specify what you want. Review the concept maps your students create to try to determine gaps in their knowledge, inappropriate links, and the intrusion of lay terms and ideas that may indicate the presence of na ï ve theories or preconceptions.
Look for Patterns of Error in Student Work Students ’ misconceptions tend to be shared and produce a consistent pattern of errors. You (or your TAs or graders) can often identify these misconceptions simply by looking at students ’ errors on homework assignments, quizzes, or exams and noting commonalities across the class. You can also keep track of the kinds of problems and errors that students reveal when they come to offi ce hours or as they raise or answer questions during class. Paying attention to these patterns of error can alert you to common problems and help you target instruction to correct misconceptions or fi ll gaps in understanding. Some instructors use classroom response systems (also called “ clickers ” ) to quickly collect students ’ answers to concept questions posed in class. Clickers provide an instant histogram of students ’ answers and can alert instructors to areas of misunderstanding that might stem from insuffi cient prior knowledge.
วันพุธที่ 7 กรกฎาคม พ.ศ. 2553
วันจันทร์ที่ 28 มิถุนายน พ.ศ. 2553
Inaccurate Prior Knowledge
We have seen in the sections above that prior knowledge will not support new learning if it is insuffi cient or inappropriate for the task at hand. But what if it is downright wrong? Research indicates that inaccurate prior knowledge (in other words, fl awed ideas, beliefs, models, or theories) can distort new knowledge by predisposing students to ignore, discount, or resist evidence that confl icts with what they believe to be true (Dunbar, Fugelsang, & Stein, 2007 ; Chinn & Malhotra, 2002 ; Brewer & Lambert, 2000 ; Fiske & Taylor, 1991 ; Alvermann, Smith, & Readance, 1985 ). Some psychologists explain this distortion as a result of our striving for internal consistency. For example, Vosniadou and Brewer (1987) found that children reconcile their perception that the earth is fl at with formal instruction stating that the earth is round by conceiving of the earth as a pancake: circular but with a fl at surface. In other words, children — like all learners — try to make sense of what they are learning by fi tting it into what they already know or believe.
Inaccurate prior knowledge can be corrected fairly easily if it consists of relatively isolated ideas or beliefs that are not embedded in larger conceptual models (for example, the belief that Pluto is a planet or that the heart oxygenates blood). Research indicates that these sorts of beliefs respond to refutation; in other words, students will generally revise them when they are explicitly confronted with contradictory explanations and evidence (Broughton, Sinatra, & Reynolds, 2007 ; Guzetti, Snyder, Glass, & Gamas, 1993 ; Chi, 2008 ). Even more integrated — yet nonetheless fl awed — conceptual models may respond to refutation over time if the individual inaccuracies they contain are refuted systematically (Chi & Roscoe, 2002 ).
However, some kinds of inaccurate prior knowledge — called misconceptions — are remarkably resistant to correction. Misconceptions are models or theories that are deeply embedded in students ’ thinking. Many examples have been documented in the literature, including na ï ve theories in physics (such as the notion that objects of different masses fall at different rates), “ folk psychology ” myths (for example, that blind people have more sensitive hearing than sighted people or that a good hypnotist can command total obedience), and stereotypes about groupsof people (Brown, 1983 ; Kaiser, McCloskey, & Proffi tt, 1986 ; McCloskey, 1983 ; Taylor & Kowalski, 2004 ).
Misconceptions are diffi cult to refute for a number of reasons. First, many of them have been reinforced over time and across multiple contexts. Moreover, because they often include accurate — as well as inaccurate — elements, students may not recognize their fl aws. Finally, in many cases, misconceptions may allow for successful explanations and predictions in a number of everyday circumstances. For example, although stereotypes are dangerous oversimplifi cations, they are diffi cult to change in part because they fi t aspects of our perceived reality and serve an adaptive human need to generalize and categorize (Allport, 1954 ; Brewer, 1988 ; Fiske & Taylor, 1991 ).
Research has shown that deeply held misconceptions often persist despite direct instructional interventions (Ram, Nersessian, & Keil, 1997 ; Gardner & Dalsing, 1986 ; Gutman, 1979 ; Confrey, 1990 ). For example, Stein and Dunbar conducted a study (described in Dunbar, Fugelsang, & Stein, 2007 ) in which they asked college students to write about why the seasons changed, and then assessed their relevant knowledge via a multiple choice test. After fi nding that 94 percent of the students in their study had misconceptions (including the belief that the shape of the earth ’ s orbit was responsible for the seasons), the researchers showed students a video that clearly explained that the tilt of the earth ’ s axis, not the shape of the earth ’ s orbit, was responsible for seasonal change. Yet in spite of the video, when students were asked to revise their essays, their explanations for the seasons did not change fundamentally. Similarly, McCloskey, Caramazza, and Green (1980) found that other deeply held misconceptions about the physical world persist even when they are refuted through formal instruction.
Results like these are sobering. Yet the picture is not altogether gloomy. To begin with, it is important to recognize that conceptual change often occurs gradually and may not be immediately visible. Thus, students may be moving in the direction of more accurate knowledge even when it is not yet apparent in their performance (Alibali, 1999 ; Chi & Roscoe, 2002 ). Moreover, even when students retain inaccurate beliefs, they can learn to inhibit and override those beliefs and draw on accurate knowledge instead. Research indicates, for instance, that when people are suffi ciently motivated to do so, they can consciously suppress stereotypical judgments and learn to rely on rational analysis more and stereotypes less (Monteith & Mark, 2005 ; Monteith, Sherman, & Devine, 1998 ). Moreover, since consciously overcoming misconceptions requires more cognitive energy than simply falling back on intuitive, familiar modes of thinking, there is research to suggest that when distractions and time pressures are minimized, students will be more likely to think rationally and avoid applying misconceptions and fl awed assumptions (Finucane et al., 2000 ; Kahnemann & Frederick, 2002 ).
In addition, carefully designed instruction can help wean students from misconceptions through a process called bridging (Brown, 1992 ; Brown & Clement, 1989 ; Clement, 1993 ). For example, Clement observed that students often had trouble believing that a table exerts force on a book placed on its surface. To help students grasp this somewhat counterintuitive concept, he designed an instructional intervention for high school physics students that started from students ’ accurate prior knowledge. Because students did believe that a compressed spring exerted force, the researchers were able to analogize from the spring to foam, then to pliable wood, and fi nally to a solid table. The intermediate objects served to bridge the difference between a spring and the table and enabled the students to extend their accurate prior knowledge to new contexts. Using this approach, Clement obtained signifi cantly greater pre - to posttest gains compared to traditional classroom instruction. In a similar vein, Minstrell ’ s research (1989) shows that students can be guided away from misconceptions through a process of reasoning that helps them build on the accurate facets of their knowledge as they gradually revise the inaccurate facets.
Implications of This Research It is important for instructors to address inaccurate prior knowledge that might otherwise distort or impede learning. In some cases, inaccuracies can be corrected simply by exposing students to accurate information and evidence that confl icts with fl awed beliefs and models. However, it is important for instructors to recognize that a single correction or refutation is unlikely to be enough to help students revise deeply held misconceptions. Instead, guiding students through a process of conceptual change is likely to take time, patience, and creativity.
Inaccurate prior knowledge can be corrected fairly easily if it consists of relatively isolated ideas or beliefs that are not embedded in larger conceptual models (for example, the belief that Pluto is a planet or that the heart oxygenates blood). Research indicates that these sorts of beliefs respond to refutation; in other words, students will generally revise them when they are explicitly confronted with contradictory explanations and evidence (Broughton, Sinatra, & Reynolds, 2007 ; Guzetti, Snyder, Glass, & Gamas, 1993 ; Chi, 2008 ). Even more integrated — yet nonetheless fl awed — conceptual models may respond to refutation over time if the individual inaccuracies they contain are refuted systematically (Chi & Roscoe, 2002 ).
However, some kinds of inaccurate prior knowledge — called misconceptions — are remarkably resistant to correction. Misconceptions are models or theories that are deeply embedded in students ’ thinking. Many examples have been documented in the literature, including na ï ve theories in physics (such as the notion that objects of different masses fall at different rates), “ folk psychology ” myths (for example, that blind people have more sensitive hearing than sighted people or that a good hypnotist can command total obedience), and stereotypes about groupsof people (Brown, 1983 ; Kaiser, McCloskey, & Proffi tt, 1986 ; McCloskey, 1983 ; Taylor & Kowalski, 2004 ).
Misconceptions are diffi cult to refute for a number of reasons. First, many of them have been reinforced over time and across multiple contexts. Moreover, because they often include accurate — as well as inaccurate — elements, students may not recognize their fl aws. Finally, in many cases, misconceptions may allow for successful explanations and predictions in a number of everyday circumstances. For example, although stereotypes are dangerous oversimplifi cations, they are diffi cult to change in part because they fi t aspects of our perceived reality and serve an adaptive human need to generalize and categorize (Allport, 1954 ; Brewer, 1988 ; Fiske & Taylor, 1991 ).
Research has shown that deeply held misconceptions often persist despite direct instructional interventions (Ram, Nersessian, & Keil, 1997 ; Gardner & Dalsing, 1986 ; Gutman, 1979 ; Confrey, 1990 ). For example, Stein and Dunbar conducted a study (described in Dunbar, Fugelsang, & Stein, 2007 ) in which they asked college students to write about why the seasons changed, and then assessed their relevant knowledge via a multiple choice test. After fi nding that 94 percent of the students in their study had misconceptions (including the belief that the shape of the earth ’ s orbit was responsible for the seasons), the researchers showed students a video that clearly explained that the tilt of the earth ’ s axis, not the shape of the earth ’ s orbit, was responsible for seasonal change. Yet in spite of the video, when students were asked to revise their essays, their explanations for the seasons did not change fundamentally. Similarly, McCloskey, Caramazza, and Green (1980) found that other deeply held misconceptions about the physical world persist even when they are refuted through formal instruction.
Results like these are sobering. Yet the picture is not altogether gloomy. To begin with, it is important to recognize that conceptual change often occurs gradually and may not be immediately visible. Thus, students may be moving in the direction of more accurate knowledge even when it is not yet apparent in their performance (Alibali, 1999 ; Chi & Roscoe, 2002 ). Moreover, even when students retain inaccurate beliefs, they can learn to inhibit and override those beliefs and draw on accurate knowledge instead. Research indicates, for instance, that when people are suffi ciently motivated to do so, they can consciously suppress stereotypical judgments and learn to rely on rational analysis more and stereotypes less (Monteith & Mark, 2005 ; Monteith, Sherman, & Devine, 1998 ). Moreover, since consciously overcoming misconceptions requires more cognitive energy than simply falling back on intuitive, familiar modes of thinking, there is research to suggest that when distractions and time pressures are minimized, students will be more likely to think rationally and avoid applying misconceptions and fl awed assumptions (Finucane et al., 2000 ; Kahnemann & Frederick, 2002 ).
In addition, carefully designed instruction can help wean students from misconceptions through a process called bridging (Brown, 1992 ; Brown & Clement, 1989 ; Clement, 1993 ). For example, Clement observed that students often had trouble believing that a table exerts force on a book placed on its surface. To help students grasp this somewhat counterintuitive concept, he designed an instructional intervention for high school physics students that started from students ’ accurate prior knowledge. Because students did believe that a compressed spring exerted force, the researchers were able to analogize from the spring to foam, then to pliable wood, and fi nally to a solid table. The intermediate objects served to bridge the difference between a spring and the table and enabled the students to extend their accurate prior knowledge to new contexts. Using this approach, Clement obtained signifi cantly greater pre - to posttest gains compared to traditional classroom instruction. In a similar vein, Minstrell ’ s research (1989) shows that students can be guided away from misconceptions through a process of reasoning that helps them build on the accurate facets of their knowledge as they gradually revise the inaccurate facets.
Implications of This Research It is important for instructors to address inaccurate prior knowledge that might otherwise distort or impede learning. In some cases, inaccuracies can be corrected simply by exposing students to accurate information and evidence that confl icts with fl awed beliefs and models. However, it is important for instructors to recognize that a single correction or refutation is unlikely to be enough to help students revise deeply held misconceptions. Instead, guiding students through a process of conceptual change is likely to take time, patience, and creativity.
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 defi nitions 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 a potential cause of heart failure that can be traced to their failure to recognize the limitations of the skeletal muscle - cardiac muscle analogy.
Knowledge from one disciplinary context, moreover, may obstruct learning and performance in another disciplinary context if students apply it inappropriately. According to Beaufort (2007) , college composition courses sometimes contribute to this phenomenon by teaching a generic approach to writing that leaves students ill - prepared to write well in particular domains. Because students come to think of writing as a “ one size fi ts all ” skill, they misapply conventions and styles from their general writing classes to disciplinary contexts in which they are not appropriate. For example, they might apply the conventions of a personal narrative or an opinion piece to writing an analytical paper or a lab report. Beaufort argues that without remediation, this intrusion of inappropriate knowledge can affect not only students ’ performance but also their ability to internalize the rhetorical conventions and strategies of the new discipline.
Furthermore, learning can also be impeded when linguistic knowledge is applied to contexts where it is inappropriate (Bartlett, 1932 ). For example, when many of us are learning a foreign language, we apply the grammatical structure we know from our native language to the new language. This can impede learning when the new language operates according to fundamentally different grammatical rules, such as a subject - object - verb con fi guration as opposed to a subject - verb - object structure (Thonis, 1981 ).
Similarly, misapplication of cultural knowledge can — and often does — lead to erroneous assumptions. For example, when Westerners draw on their own cultural knowledge to interpret practices such as veiling in the Muslim world, they may misinterpret the meaning of the veil to the women who wear it. For instance, Westerners may assume that veiling is a practice imposed by men on unwilling women or that Muslim women who veil do so to hide their beauty. In fact, neither of these conclusions is necessarily accurate; for instance, some Muslim women voluntarily choose to cover — sometimes against the wishes of male family members — as a statement of modern religious and political identity (Ahmed, 1993 ; El Guindi, 1999 ). By the same token, some women think of the veil as a way to accentuate, not conceal, beauty (Wikan, 1982 ). Yet if Westerners interpret these practices through the lens of their own prior cultural knowledge and assumptions, they may emerge with a distorted understanding that can impede further learning.
Research suggests that if students are explicitly taught the conditions and contexts in which knowledge is applicable (and inapplicable), it can help them avoid applying prior knowledge inappropriately. Moreover, if students learn abstract principles to guide the application of their knowledge and are presented with multiple examples and contexts in which to practice applying those principles, it not only helps them recognize when their prior knowledge is relevant to a particular context (see Chapter Four on transfer), but also helps them avoid misapplying knowledge in the wrong contexts (Schwartz et al., 1999 ). Researchers also observe that making students explicitly aware of the limitations of a given analogy can help them learn not to approach analogies uncritically or stretch a simple analogy too far (Spiro et al., 1989 ).
Another way to help students avoid making inappropriate associations or applying prior knowledge in the wrong contexts is to deliberately activate their relevant prior knowledge (Minstrell, 1989, 1992 ). If we recall Professor Dione ’ s course from the story at the beginning of the chapter, we can imagine a potential application for this idea. When presented with the counterintuitive concept of negative reinforcement, Professor Dione ’ s students drew on associations (of positive as desirable and negative as undesirable) that were interfering with their comprehension. However, if Professor Dione had tried activating a different set of associations — namely of positive as adding and negative as subtracting — he may have been able to leverage those associations to help his students understand that positive reinforcement involves adding something to a situation to increase a desired behavior whereas negative reinforcement involves subtracting something to increase a desired behavior.
Implications of This Research
When learning new material, students may draw on knowledge (from everyday contexts, from incomplete analogies, from other disciplinary contexts, and from their own cultural or linguistic backgrounds) that is inappropriate for the context, and which can distort their interpretation of new material or impede new learning. To help students learn where their prior knowledge is and is not applicable, it is important for instructors to (a) clearly explain the conditions and contexts of applicability, (b) teach abstract principles but also provide multiple examples and contexts, (c) point out differences, as well as similarities, when employing analogies, and (d) deliberately activate relevant prior knowledge to strengthen appropriate associations.
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 defi nitions 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 a potential cause of heart failure that can be traced to their failure to recognize the limitations of the skeletal muscle - cardiac muscle analogy.
Knowledge from one disciplinary context, moreover, may obstruct learning and performance in another disciplinary context if students apply it inappropriately. According to Beaufort (2007) , college composition courses sometimes contribute to this phenomenon by teaching a generic approach to writing that leaves students ill - prepared to write well in particular domains. Because students come to think of writing as a “ one size fi ts all ” skill, they misapply conventions and styles from their general writing classes to disciplinary contexts in which they are not appropriate. For example, they might apply the conventions of a personal narrative or an opinion piece to writing an analytical paper or a lab report. Beaufort argues that without remediation, this intrusion of inappropriate knowledge can affect not only students ’ performance but also their ability to internalize the rhetorical conventions and strategies of the new discipline.
Furthermore, learning can also be impeded when linguistic knowledge is applied to contexts where it is inappropriate (Bartlett, 1932 ). For example, when many of us are learning a foreign language, we apply the grammatical structure we know from our native language to the new language. This can impede learning when the new language operates according to fundamentally different grammatical rules, such as a subject - object - verb con fi guration as opposed to a subject - verb - object structure (Thonis, 1981 ).
Similarly, misapplication of cultural knowledge can — and often does — lead to erroneous assumptions. For example, when Westerners draw on their own cultural knowledge to interpret practices such as veiling in the Muslim world, they may misinterpret the meaning of the veil to the women who wear it. For instance, Westerners may assume that veiling is a practice imposed by men on unwilling women or that Muslim women who veil do so to hide their beauty. In fact, neither of these conclusions is necessarily accurate; for instance, some Muslim women voluntarily choose to cover — sometimes against the wishes of male family members — as a statement of modern religious and political identity (Ahmed, 1993 ; El Guindi, 1999 ). By the same token, some women think of the veil as a way to accentuate, not conceal, beauty (Wikan, 1982 ). Yet if Westerners interpret these practices through the lens of their own prior cultural knowledge and assumptions, they may emerge with a distorted understanding that can impede further learning.
Research suggests that if students are explicitly taught the conditions and contexts in which knowledge is applicable (and inapplicable), it can help them avoid applying prior knowledge inappropriately. Moreover, if students learn abstract principles to guide the application of their knowledge and are presented with multiple examples and contexts in which to practice applying those principles, it not only helps them recognize when their prior knowledge is relevant to a particular context (see Chapter Four on transfer), but also helps them avoid misapplying knowledge in the wrong contexts (Schwartz et al., 1999 ). Researchers also observe that making students explicitly aware of the limitations of a given analogy can help them learn not to approach analogies uncritically or stretch a simple analogy too far (Spiro et al., 1989 ).
Another way to help students avoid making inappropriate associations or applying prior knowledge in the wrong contexts is to deliberately activate their relevant prior knowledge (Minstrell, 1989, 1992 ). If we recall Professor Dione ’ s course from the story at the beginning of the chapter, we can imagine a potential application for this idea. When presented with the counterintuitive concept of negative reinforcement, Professor Dione ’ s students drew on associations (of positive as desirable and negative as undesirable) that were interfering with their comprehension. However, if Professor Dione had tried activating a different set of associations — namely of positive as adding and negative as subtracting — he may have been able to leverage those associations to help his students understand that positive reinforcement involves adding something to a situation to increase a desired behavior whereas negative reinforcement involves subtracting something to increase a desired behavior.
Implications of This Research
When learning new material, students may draw on knowledge (from everyday contexts, from incomplete analogies, from other disciplinary contexts, and from their own cultural or linguistic backgrounds) that is inappropriate for the context, and which can distort their interpretation of new material or impede new learning. To help students learn where their prior knowledge is and is not applicable, it is important for instructors to (a) clearly explain the conditions and contexts of applicability, (b) teach abstract principles but also provide multiple examples and contexts, (c) point out differences, as well as similarities, when employing analogies, and (d) deliberately activate relevant prior knowledge to strengthen appropriate associations.
วันอังคารที่ 22 มิถุนายน พ.ศ. 2553
Implications of This Research
Even when students ’ prior knowledge is accurate and activated, it may not be suffi cient 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 scientifi c 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 insuffi cient 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 fi nance 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 suffi cient procedural knowledge to function effectively in specifi c 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.
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 scientifi c 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 insuffi cient 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 fi nance 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 suffi cient procedural knowledge to function effectively in specifi c 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.
วันศุกร์ที่ 18 มิถุนายน พ.ศ. 2553
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 defi nitions 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 a potential cause of heart failure that can be traced to their failure to recognize the limitations of the skeletal muscle - cardiac muscle analogy.
Knowledge from one disciplinary context, moreover, may obstruct learning and performance in another disciplinary context if students apply it inappropriately. According to Beaufort (2007) , college composition courses sometimes contribute to this phenomenon by teaching a generic approach to writing that leaves students ill - prepared to write well in particular domains. Because students come to think of writing as a “ one size fi ts all ” skill, they misapply conventions and styles from their general writing classes to disciplinary contexts in which they are not appropriate. For example, they might apply the conventions of a personal narrative or an opinion piece to writing an analytical paper or a lab report. Beaufort argues that without remediation, this intrusion of inappropriate knowledge can affect not only students ’ performance but also their ability to internalize the rhetorical conventions and strategies of the new discipline.
Furthermore, learning can also be impeded when linguistic knowledge is applied to contexts where it is inappropriate (Bartlett, 1932 ). For example, when many of us are learning a foreign language, we apply the grammatical structure we know from our native language to the new language. This can impede learning when the new language operates according to fundamentally different grammatical rules, such as a subject - object - verb con fi guration as opposed to a subject - verb - object structure (Thonis, 1981 ).
Similarly, misapplication of cultural knowledge can — and often does — lead to erroneous assumptions. For example, when Westerners draw on their own cultural knowledge to interpret practices such as veiling in the Muslim world, they may misinterpret the meaning of the veil to the women who wear it. For instance, Westerners may assume that veiling is a practice imposed by men on unwilling women or that Muslim women who veil do so to hide their beauty. In fact, neither of these conclusions is necessarily accurate; for instance, some Muslim women voluntarily choose to cover — sometimes against the wishes of male family members — as a statement of modern religious and political identity (Ahmed, 1993 ; El Guindi, 1999 ). By the same token, some women think of the veil as a way to accentuate, not conceal, beauty (Wikan, 1982 ). Yet if Westerners interpret these practices through the lens of their own prior cultural knowledge and assumptions, they may emerge with a distorted understanding that can impede further learning.
Research suggests that if students are explicitly taught the conditions and contexts in which knowledge is applicable (and inapplicable), it can help them avoid applying prior knowledge inappropriately. Moreover, if students learn abstract principles to guide the application of their knowledge and are presented with multiple examples and contexts in which to practice applying those principles, it not only helps them recognize when their prior knowledge is relevant to a particular context (see Chapter Four on transfer), but also helps them avoid misapplying knowledge in the wrong contexts (Schwartz et al., 1999 ). Researchers also observe that making students explicitly aware of the limitations of a given analogy can help them learn not to approach analogies uncritically or stretch a simple analogy too far (Spiro et al., 1989 ).
Another way to help students avoid making inappropriate associations or applying prior knowledge in the wrong contexts is to deliberately activate their relevant prior knowledge (Minstrell, 1989, 1992 ). If we recall Professor Dione ’ s course from the story at the beginning of the chapter, we can imagine a potential application for this idea. When presented with the counterintuitive concept of negative reinforcement, Professor Dione ’ s students drew on associations (of positive as desirable and negative as undesirable) that were interfering with their comprehension. However, if Professor Dione had tried activating a different set of associations — namely of positive as adding and negative as subtracting — he may have been able to leverage those associations to help his students understand that positive reinforcement involves adding something to a situation to increase a desired behavior whereas negative reinforcement involves subtracting something to increase a desired behavior.
Implications of This Research When learning new material, students may draw on knowledge (from everyday contexts, from incomplete analogies, from other disciplinary contexts, and from their own cultural or linguistic backgrounds) that is inappropriate for the context, and which can distort their interpretation of new material or impede new learning. To help students learn where their prior knowledge is and is not applicable, it is important for instructors to (a) clearly explain the conditions and contexts of applicability, (b) teach abstract principles but also provide multiple examples and contexts, (c) point out differences, as well as similarities, when employing analogies, and (d) deliberately activate relevant prior knowledge to strengthen appropriate associations.
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 defi nitions 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 a potential cause of heart failure that can be traced to their failure to recognize the limitations of the skeletal muscle - cardiac muscle analogy.
Knowledge from one disciplinary context, moreover, may obstruct learning and performance in another disciplinary context if students apply it inappropriately. According to Beaufort (2007) , college composition courses sometimes contribute to this phenomenon by teaching a generic approach to writing that leaves students ill - prepared to write well in particular domains. Because students come to think of writing as a “ one size fi ts all ” skill, they misapply conventions and styles from their general writing classes to disciplinary contexts in which they are not appropriate. For example, they might apply the conventions of a personal narrative or an opinion piece to writing an analytical paper or a lab report. Beaufort argues that without remediation, this intrusion of inappropriate knowledge can affect not only students ’ performance but also their ability to internalize the rhetorical conventions and strategies of the new discipline.
Furthermore, learning can also be impeded when linguistic knowledge is applied to contexts where it is inappropriate (Bartlett, 1932 ). For example, when many of us are learning a foreign language, we apply the grammatical structure we know from our native language to the new language. This can impede learning when the new language operates according to fundamentally different grammatical rules, such as a subject - object - verb con fi guration as opposed to a subject - verb - object structure (Thonis, 1981 ).
Similarly, misapplication of cultural knowledge can — and often does — lead to erroneous assumptions. For example, when Westerners draw on their own cultural knowledge to interpret practices such as veiling in the Muslim world, they may misinterpret the meaning of the veil to the women who wear it. For instance, Westerners may assume that veiling is a practice imposed by men on unwilling women or that Muslim women who veil do so to hide their beauty. In fact, neither of these conclusions is necessarily accurate; for instance, some Muslim women voluntarily choose to cover — sometimes against the wishes of male family members — as a statement of modern religious and political identity (Ahmed, 1993 ; El Guindi, 1999 ). By the same token, some women think of the veil as a way to accentuate, not conceal, beauty (Wikan, 1982 ). Yet if Westerners interpret these practices through the lens of their own prior cultural knowledge and assumptions, they may emerge with a distorted understanding that can impede further learning.
Research suggests that if students are explicitly taught the conditions and contexts in which knowledge is applicable (and inapplicable), it can help them avoid applying prior knowledge inappropriately. Moreover, if students learn abstract principles to guide the application of their knowledge and are presented with multiple examples and contexts in which to practice applying those principles, it not only helps them recognize when their prior knowledge is relevant to a particular context (see Chapter Four on transfer), but also helps them avoid misapplying knowledge in the wrong contexts (Schwartz et al., 1999 ). Researchers also observe that making students explicitly aware of the limitations of a given analogy can help them learn not to approach analogies uncritically or stretch a simple analogy too far (Spiro et al., 1989 ).
Another way to help students avoid making inappropriate associations or applying prior knowledge in the wrong contexts is to deliberately activate their relevant prior knowledge (Minstrell, 1989, 1992 ). If we recall Professor Dione ’ s course from the story at the beginning of the chapter, we can imagine a potential application for this idea. When presented with the counterintuitive concept of negative reinforcement, Professor Dione ’ s students drew on associations (of positive as desirable and negative as undesirable) that were interfering with their comprehension. However, if Professor Dione had tried activating a different set of associations — namely of positive as adding and negative as subtracting — he may have been able to leverage those associations to help his students understand that positive reinforcement involves adding something to a situation to increase a desired behavior whereas negative reinforcement involves subtracting something to increase a desired behavior.
Implications of This Research When learning new material, students may draw on knowledge (from everyday contexts, from incomplete analogies, from other disciplinary contexts, and from their own cultural or linguistic backgrounds) that is inappropriate for the context, and which can distort their interpretation of new material or impede new learning. To help students learn where their prior knowledge is and is not applicable, it is important for instructors to (a) clearly explain the conditions and contexts of applicability, (b) teach abstract principles but also provide multiple examples and contexts, (c) point out differences, as well as similarities, when employing analogies, and (d) deliberately activate relevant prior knowledge to strengthen appropriate associations.
Accurate but Insuffi cient Prior Knowledge
Even when students’ prior knowledge is accurate and activated, it may not be suffi cient 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 scientifi c 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 insuffi cient 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 fi nance 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 suffi cient procedural knowledge to function effectively in specifi c 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.
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 scientifi c 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 insuffi cient 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 fi nance 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 suffi cient procedural knowledge to function effectively in specifi c 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.
วันพฤหัสบดีที่ 17 มิถุนายน พ.ศ. 2553
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 fi rst 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 fi rst, 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 specifi cally 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 diffi culty remembering specifi c facts. However, when the researchers asked a set of “ why ” questions (for example, “ Why would Ontario have been the fi rst 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 signifi cantly.
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, Garfi eld and her colleagues (Garfi eld, Del Mas, & Chance, 2007 ) designed an instructional study in a college statistics course that focused on the concept of variability — a notoriously diffi cult concept to grasp. The instructors fi rst 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.
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 fi rst 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 fi rst, 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 specifi cally 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 diffi culty remembering specifi c facts. However, when the researchers asked a set of “ why ” questions (for example, “ Why would Ontario have been the fi rst 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 signifi cantly.
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, Garfi eld and her colleagues (Garfi eld, Del Mas, & Chance, 2007 ) designed an instructional study in a college statistics course that focused on the concept of variability — a notoriously diffi cult concept to grasp. The instructors fi rst 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.
WHAT DOES THE RESEARCH TELL US ABOUT PRIOR KNOWLEDGE?
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.
WHAT PRINCIPLE OF LEARNING IS AT WORK HERE?
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 fi nd — 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, insuffi cient 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 insuffi cient 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.
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, insuffi cient 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 insuffi cient 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.
How Does Students ’ Prior Knowledge Aff ect Their Learning?
But They Said They Knew This!
I recently taught Research Methods in Decision Sciences for the fi rst 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 confi dent that my fi rst 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 fi gure 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
WHAT IS GOING ON IN THESE STORIES?
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 confi rmed 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 suffi cient 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 defi nition. 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.
I recently taught Research Methods in Decision Sciences for the fi rst 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 confi dent that my fi rst 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 fi gure 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
WHAT IS GOING ON IN THESE STORIES?
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 confi rmed 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 suffi cient 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 defi nition. 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.
OUR PRINCIPLES OF LEARNING
Our seven principles of learning come from a perspective that is developmental and holistic. In other words, we begin with the recognition that (a) learning is a developmental process that intersects with other developmental processes in a student ’ s life, and (b) students enter our classrooms not only with skills, knowledge, and abilities, but also with social and emotional experiences that infl uence what they value, how they perceive themselves and others, and how they will engage in the learning process. Consistent with this holistic perspective, readers should understand that, although we address each principle individually to highlight particular issues pertaining to student learning, they are all at work in real learning situations and are functionally inseparable.
In the paragraphs below, we briefl y summarize each of the principles in the order in which they are discussed in the blog.
Students ’ prior knowledge can help or hinder learning.
Students come into our courses with knowledge, beliefs, and attitudes gained in other courses and through daily life. As students bring this knowledge to bear in our classrooms, it infl uences how they fi lter and interpret what they are learning. If students ’ prior knowledge is robust and accurate and activated at the appropriate time, it provides a strong foundation for building new knowledge. However, when knowledge is inert, insuffi cient for the task, activated inappropriately, or inaccurate, it can interfere with or impede new learning.
How students organize knowledge infl uences how they learn and apply what they know.
Students naturally make connections between pieces of knowledge. When those connections form knowledge structures that are accurately and meaningfully organized, students are better able to retrieve and apply their knowledge effectively and effi ciently. In contrast, when knowledge is connected in inaccurate or random ways, students can fail to retrieve or apply it appropriately.
Students ’ motivation determines, directs, and sustains what they do to learn.
As students enter college and gain greater autonomy over what, when, and how they study and learn, motivation plays a critical role in guiding the direction, intensity, persistence, and quality of the learning behaviors in which they engage. When students fi nd positive value in a learning goal or activity, expect to successfully achieve a desired learning outcome, and perceive support from their environment, they are likely to be strongly motivated to learn
To develop mastery, students must acquire component skills, practice integrating them, and know when to apply what they have learned.
Students must develop not only the component skills and knowledge necessary to perform complex tasks, they must also practice combining and integrating them to develop greater fluency and automaticity. Finally, students must learn when and how to apply the skills and knowledge they learn. As instructors, it is important that we develop conscious awareness of these elements of mastery so as to help our students learn more effectively.
Goal - directed practice coupled with targeted feedback enhances the quality of students ’ learning.
Learning and performance are best fostered when students engage in practice that focuses on a specifi c goal or criterion, targets an appropriate level of challenge, and is of suffi cient quantity and frequency to meet the performance criteria. Practice must be coupled with feedback that explicitly communicates about some aspect(s) of students ’ performance relative to specifi c target criteria, provides information to help students progress in meeting those criteria, and is given at a time and frequency that allows it to be useful.
Students ’ current level of development interacts with the social, emotional, and intellectual climate of the course to impact learning.
Students are not only intellectual but also social and emotional beings, and they are still developing the full range of intellectual, social, and emotional skills. While we cannot control the developmental process, we can shape the intellectual, social, emotional, and physical aspects of the classroom climate in developmentally appropriate ways. In fact, many studies have shown that the climate we create has implications for our students. A negative climate may impede learning and performance, but a positive climate can energize students ’ learning.
To become self - directed learners, students must learn to monitor and adjust their approaches to learning.
Learners may engage in a variety of metacognitive processes to monitor and control their learning — assessing the task at hand, evaluating their own strengths and weaknesses, planning their approach, applying and monitoring various strategies, and refl ecting on the degree to which their current approach is working. Unfortunately, students tend not to engage in these processes naturally. When students develop the skills to engage these processes, they gain intellectual habits that not only improve their performance but also their effectiveness as learners.
In the paragraphs below, we briefl y summarize each of the principles in the order in which they are discussed in the blog.
Students ’ prior knowledge can help or hinder learning.
Students come into our courses with knowledge, beliefs, and attitudes gained in other courses and through daily life. As students bring this knowledge to bear in our classrooms, it infl uences how they fi lter and interpret what they are learning. If students ’ prior knowledge is robust and accurate and activated at the appropriate time, it provides a strong foundation for building new knowledge. However, when knowledge is inert, insuffi cient for the task, activated inappropriately, or inaccurate, it can interfere with or impede new learning.
How students organize knowledge infl uences how they learn and apply what they know.
Students naturally make connections between pieces of knowledge. When those connections form knowledge structures that are accurately and meaningfully organized, students are better able to retrieve and apply their knowledge effectively and effi ciently. In contrast, when knowledge is connected in inaccurate or random ways, students can fail to retrieve or apply it appropriately.
Students ’ motivation determines, directs, and sustains what they do to learn.
As students enter college and gain greater autonomy over what, when, and how they study and learn, motivation plays a critical role in guiding the direction, intensity, persistence, and quality of the learning behaviors in which they engage. When students fi nd positive value in a learning goal or activity, expect to successfully achieve a desired learning outcome, and perceive support from their environment, they are likely to be strongly motivated to learn
To develop mastery, students must acquire component skills, practice integrating them, and know when to apply what they have learned.
Students must develop not only the component skills and knowledge necessary to perform complex tasks, they must also practice combining and integrating them to develop greater fluency and automaticity. Finally, students must learn when and how to apply the skills and knowledge they learn. As instructors, it is important that we develop conscious awareness of these elements of mastery so as to help our students learn more effectively.
Goal - directed practice coupled with targeted feedback enhances the quality of students ’ learning.
Learning and performance are best fostered when students engage in practice that focuses on a specifi c goal or criterion, targets an appropriate level of challenge, and is of suffi cient quantity and frequency to meet the performance criteria. Practice must be coupled with feedback that explicitly communicates about some aspect(s) of students ’ performance relative to specifi c target criteria, provides information to help students progress in meeting those criteria, and is given at a time and frequency that allows it to be useful.
Students ’ current level of development interacts with the social, emotional, and intellectual climate of the course to impact learning.
Students are not only intellectual but also social and emotional beings, and they are still developing the full range of intellectual, social, and emotional skills. While we cannot control the developmental process, we can shape the intellectual, social, emotional, and physical aspects of the classroom climate in developmentally appropriate ways. In fact, many studies have shown that the climate we create has implications for our students. A negative climate may impede learning and performance, but a positive climate can energize students ’ learning.
To become self - directed learners, students must learn to monitor and adjust their approaches to learning.
Learners may engage in a variety of metacognitive processes to monitor and control their learning — assessing the task at hand, evaluating their own strengths and weaknesses, planning their approach, applying and monitoring various strategies, and refl ecting on the degree to which their current approach is working. Unfortunately, students tend not to engage in these processes naturally. When students develop the skills to engage these processes, they gain intellectual habits that not only improve their performance but also their effectiveness as learners.
WHAT IS LEARNING?
Any set of learning principles is predicated on a defi nition of learning. In this book, we defi ne learning as a process that leads to change , which occurs as a result of experience and increases the potential for improved performance and future learning (adapted from Mayer, 2002 ). There are three critical components to this definition:
1. Learning is a process , not a product. However, because this process takes place in the mind, we can only infer that it has occurred from students ’ products or performances.
2. Learning involves change in knowledge, beliefs, behaviors, or attitudes. This change unfolds over time; it is not fl eeting but rather has a lasting impact on how students think and act.
3. Learning is not something done to students, but rather something students themselves do. It is the direct result of how students interpret and respond to their experiences — conscious and unconscious, past and present.
1. Learning is a process , not a product. However, because this process takes place in the mind, we can only infer that it has occurred from students ’ products or performances.
2. Learning involves change in knowledge, beliefs, behaviors, or attitudes. This change unfolds over time; it is not fl eeting but rather has a lasting impact on how students think and act.
3. Learning is not something done to students, but rather something students themselves do. It is the direct result of how students interpret and respond to their experiences — conscious and unconscious, past and present.
วันพุธที่ 16 มิถุนายน พ.ศ. 2553
Bridging Learning Research and Teaching Practice
Learning results from what the student does and thinks and only from what the student does and thinks. The teacher can advance learning only by infl uencing what the student does to learn.
HERBERT A. SIMON, 1 one of the founders of the fi eld of Cognitive Science, Nobel Laureate, and University Professor (deceased) at Carnegie Mellon University
As the quotation above suggests, any conversation about effective teaching must begin with a consideration of how students learn. Yet instructors who want to investigate the mechanisms and conditions that promote student learning may fi nd themselves caught between two kinds of resources: research articles with technical discussions of learning, or books and Web sites with concrete strategies for course design and classroom pedagogy. Texts of the fi rst type focus on learning but are often technical, inaccessible, and lack clear application to the classroom, while texts of the second type are written in accessible language but often leave instructors without a clear sense of why (or even whether) particular strategies promote learning. Neither of these genres offers what many instructors really need — a model of student learning that enables them to make sound teaching decisions. In other words, instructors need a bridge between research and practice, between teaching and learning.
We wrote this book to provide such a bridge. The book grew out of over twenty - nine years of experience consulting with faculty colleagues about teaching and learning. In these consultations, we encountered a number of recurring problems that spanned disciplines, course types, and student skill levels. Many of these problems raised fundamental questions about student learning. For example: Why can ’ t students apply what they have learned? Why do they cling so tightly to misconceptions? Why are they not more engaged by material I fi nd so interesting? Why do they claim to know so much more than they actually know? Why do they continue to employ the same ineffective study strategies?
As we worked with faculty to explore the sources of these problems, we turned to the research on learning, and from this research we distilled seven principles, each of which crystallizes a key aspect of student learning. These principles have become the foundation for our work. Not only have we found them indispensable in our own teaching and in our consultations with faculty, but as we have talked and worked with thousands of faculty from around the world, we have also found that the principles resonate across disciplines, institution types, and cultures, from Latin America to Asia. In our experience, these principles provide instructors with an understanding of student learning that can help them (a) see why certain teaching approaches are or are not supporting students ’ learning, (b) generate or refi ne teaching approaches and strategies that more effectively foster student learning in specifi c contexts, and (c) transfer and apply these principles to new courses.
In this blog, we offer these principles of learning, along with a discussion of the research that supports them, their implications for teaching, and a set of instructional strategies targeting each principle. Before briefl y summarizing the full set of principles and discussing the characteristics they share and some ways that this book can be used, we begin by discussing what we mean by learning.
HERBERT A. SIMON, 1 one of the founders of the fi eld of Cognitive Science, Nobel Laureate, and University Professor (deceased) at Carnegie Mellon University
As the quotation above suggests, any conversation about effective teaching must begin with a consideration of how students learn. Yet instructors who want to investigate the mechanisms and conditions that promote student learning may fi nd themselves caught between two kinds of resources: research articles with technical discussions of learning, or books and Web sites with concrete strategies for course design and classroom pedagogy. Texts of the fi rst type focus on learning but are often technical, inaccessible, and lack clear application to the classroom, while texts of the second type are written in accessible language but often leave instructors without a clear sense of why (or even whether) particular strategies promote learning. Neither of these genres offers what many instructors really need — a model of student learning that enables them to make sound teaching decisions. In other words, instructors need a bridge between research and practice, between teaching and learning.
We wrote this book to provide such a bridge. The book grew out of over twenty - nine years of experience consulting with faculty colleagues about teaching and learning. In these consultations, we encountered a number of recurring problems that spanned disciplines, course types, and student skill levels. Many of these problems raised fundamental questions about student learning. For example: Why can ’ t students apply what they have learned? Why do they cling so tightly to misconceptions? Why are they not more engaged by material I fi nd so interesting? Why do they claim to know so much more than they actually know? Why do they continue to employ the same ineffective study strategies?
As we worked with faculty to explore the sources of these problems, we turned to the research on learning, and from this research we distilled seven principles, each of which crystallizes a key aspect of student learning. These principles have become the foundation for our work. Not only have we found them indispensable in our own teaching and in our consultations with faculty, but as we have talked and worked with thousands of faculty from around the world, we have also found that the principles resonate across disciplines, institution types, and cultures, from Latin America to Asia. In our experience, these principles provide instructors with an understanding of student learning that can help them (a) see why certain teaching approaches are or are not supporting students ’ learning, (b) generate or refi ne teaching approaches and strategies that more effectively foster student learning in specifi c contexts, and (c) transfer and apply these principles to new courses.
In this blog, we offer these principles of learning, along with a discussion of the research that supports them, their implications for teaching, and a set of instructional strategies targeting each principle. Before briefl y summarizing the full set of principles and discussing the characteristics they share and some ways that this book can be used, we begin by discussing what we mean by learning.
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