Improving Students’ Learning With Effective Learning Techniques

I’m stuck on a Psychology question and need an explanation.

lease read the attached paper titled:  “Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology”

After reading the paper, please share your view on a particular technique that has proven to you useful to retain and apply new knowledge when you study – i.e. that helps you to learn!

Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology Psychological Science in the Public Interest 14(1) 4­–58 © The Author(s) 2013 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1529100612453266 http://pspi.sagepub.com John Dunlosky1, Katherine A. Rawson1, Elizabeth J. Marsh2 , Mitchell J. Nathan3, and Daniel T. Willingham4 1 Department of Psychology, Kent State University; 2Department of Psychology and Neuroscience, Duke University; Department of Educational Psychology, Department of Curriculum & Instruction, and Department of Psychology, University of Wisconsin–Madison; and 4Department of Psychology, University of Virginia 3 Summary Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: 1. General description of the technique and why it should work 2. How general are the effects of this technique? 2a. Learning conditions 2b. Student characteristics 2c. Materials 2d. Criterion tasks 3. Effects in representative educational contexts 4. Issues for implementation 5. Overall assessment Corresponding Author: John Dunlosky, Psychology, Kent State University, Kent, OH 44242 E-mail: [email protected] Improving Student Achievement 5 The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students’ performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and selfexplanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness.The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students’ performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research. Introduction If simple techniques were available that teachers and students could use to improve student learning and achievement, would you be surprised if teachers were not being told about these techniques and if many students were not using them? What if students were instead adopting ineffective learning techniques that undermined their achievement, or at least did not improve it? Shouldn’t they stop using these techniques and begin using ones that are effective? Psychologists have been developing and evaluating the efficacy of techniques for study and instruction for more than 100 years. Nevertheless, some effective techniques are underutilized—many teachers do not learn about them, and hence many students do not use them, despite evidence suggesting that the techniques could benefit student achievement with little added effort. Also, some learning techniques that are popular and often used by students are relatively ineffective. One potential reason for the disconnect between research on the efficacy of learning techniques and their use in educational practice is that because so many techniques are available, it would be challenging for educators to sift through the relevant research to decide which ones show promise of efficacy and could feasibly be implemented by students (Pressley, Goodchild, Fleet, Zajchowski, & Evans, 1989). Toward meeting this challenge, we explored the efficacy of 10 learning techniques (listed in Table 1) that students could use to improve their success across a wide variety of content domains.1 The learning techniques we consider here were chosen on the basis of the following criteria. We chose some techniques (e.g., self-testing, distributed practice) because an initial survey of the literature indicated that they could improve student success across a wide range of conditions. Other techniques (e.g., rereading and highlighting) were included because students report using them frequently. Moreover, students are responsible for regulating an increasing amount of their learning as they progress from elementary grades through middle school and high school to college. Lifelong learners also need to continue regulating their own learning, whether it takes place in the context of postgraduate education, the workplace, the development of new hobbies, or recreational activities. Thus, we limited our choices to techniques that could be implemented by students without assistance (e.g., without requiring advanced technologies or extensive materials that would have to be prepared by a teacher). Some training may be required for students to learn how to use a technique with fidelity, but in principle, students should be able to use the techniques without supervision. We also chose techniques for which a sufficient amount of empirical evidence was available to support at least a preliminary assessment of potential efficacy. Of course, we could not review all the techniques that meet these criteria, given the in-depth nature of our reviews, and these criteria excluded some techniques that show much promise, such as techniques that are driven by advanced technologies. Because teachers are most likely to learn about these techniques in educational psychology classes, we examined how some educational-psychology textbooks covered them (Ormrod, 2008; Santrock, 2008; Slavin, 2009; Snowman, 6 Dunlosky et al. Table 1. Learning Techniques Technique 1. Elaborative interrogation 2. Self-explanation 3. Summarization 4. Highlighting/underlining 5. Keyword mnemonic 6. Imagery for text 7. Rereading 8. Practice testing 9. Distributed practice 10. Interleaved practice Description Generating an explanation for why an explicitly stated fact or concept is true Explaining how new information is related to known information, or explaining steps taken during problem solving Writing summaries (of various lengths) of to-be-learned texts Marking potentially important portions of to-be-learned materials while reading Using keywords and mental imagery to associate verbal materials Attempting to form mental images of text materials while reading or listening Restudying text material again after an initial reading Self-testing or taking practice tests over to-be-learned material Implementing a schedule of practice that spreads out study activities over time Implementing a schedule of practice that mixes different kinds of problems, or a schedule of study that mixes different kinds of material, within a single study session Note. See text for a detailed description of each learning technique and relevant examples of their use. Table 2. Examples of the Four Categories of Variables for Generalizability Materials Vocabulary Translation equivalents Lecture content Science definitions Narrative texts Expository texts Mathematical concepts Maps Diagrams Learning conditions Amount of practice (dosage) Open- vs. closed-book practice Reading vs. listening Incidental vs. intentional learning Direct instruction Discovery learning Rereading lagsb Kind of practice testsc Group vs. individual learning Student characteristicsa Age Prior domain knowledge Working memory capacity Verbal ability Interests Fluid intelligence Motivation Prior achievement Self-efficacy Criterion tasks Cued recall Free recall Recognition Problem solving Argument development Essay writing Creation of portfolios Achievement tests Classroom quizzes a Some of these characteristics are more state based (e.g., motivation) and some are more trait based (e.g., fluid intelligence); this distinction is relevant to the malleability of each characteristic, but a discussion of this dimension is beyond the scope of this article. b Learning condition is specific to rereading. c Learning condition is specific to practice testing. McCown, & Biehler, 2009; Sternberg & Williams, 2010; Woolfolk, 2007). Despite the promise of some of the techniques, many of these textbooks did not provide sufficient coverage, which would include up-to-date reviews of their efficacy and analyses of their generalizability and potential limitations. Accordingly, for all of the learning techniques listed in Table 1, we reviewed the literature to identify the generalizability of their benefits across four categories of variables—materials, learning conditions, student characteristics, and criterion tasks. The choice of these categories was inspired by Jenkins’ (1979) model (for an example of its use in educational contexts, see Marsh & Butler, in press), and examples of each category are presented in Table 2. Materials pertain to the specific content that students are expected to learn, remember, or comprehend. Learning conditions pertain to aspects of the context in which students are interacting with the to-belearned materials. These conditions include aspects of the learning environment itself (e.g., noisiness vs. quietness in a classroom), but they largely pertain to the way in which a learning technique is implemented. For instance, a technique could be used only once or many times (a variable referred to as dosage) when students are studying, or a technique could be used when students are either reading or listening to the to-belearned materials. Any number of student characteristics could also influence the effectiveness of a given learning technique. For example, in comparison to more advanced students, younger students in early grades may not benefit from a technique. Students’ basic cognitive abilities, such as working memory capacity or general fluid intelligence, may also influence the efficacy of a given technique. In an educational context, domain knowledge refers to the valid, relevant knowledge a student brings to a lesson. Domain knowledge may be required for students to use some of the learning techniques listed in Table 1. For instance, 7 Improving Student Achievement the use of imagery while reading texts requires that students know the objects and ideas that the words refer to so that they can produce internal images of them. Students with some domain knowledge about a topic may also find it easier to use self-explanation and elaborative interrogation, which are two techniques that involve answering “why” questions about a particular concept (e.g., “Why would particles of ice rise up within a cloud?”). Domain knowledge may enhance the benefits of summarization and highlighting as well. Nevertheless, although some domain knowledge will benefit students as they begin learning new content within a given domain, it is not a prerequisite for using most of the learning techniques. The degree to which the efficacy of each learning technique obtains across long retention intervals and generalizes across different criterion tasks is of critical importance. Our reviews and recommendations are based on evidence, which typically pertains to students’ objective performance on any number of criterion tasks. Criterion tasks (Table 2, rightmost column) vary with respect to the specific kinds of knowledge that they tap. Some tasks are meant to tap students’ memory for information (e.g., “What is operant conditioning?”), others are largely meant to tap students’ comprehension (e.g., “Explain the difference between classical conditioning and operant conditioning”), and still others are meant to tap students’ application of knowledge (e.g., “How would you apply operant conditioning to train a dog to sit down?”). Indeed, Bloom and colleagues divided learning objectives into six categories, from memory (or knowledge) and comprehension of facts to their application, analysis, synthesis, and evaluation (B. S. Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956; for an updated taxonomy, see L. W. Anderson & Krathwohl, 2001). In discussing how the techniques influence criterion performance, we emphasize investigations that have gone beyond demonstrating improved memory for target material by measuring students’ comprehension, application, and transfer of knowledge. Note, however, that although gaining factual knowledge is not considered the only or ultimate objective of schooling, we unabashedly consider efforts to improve student retention of knowledge as essential for reaching other instructional objectives; if one does not remember core ideas, facts, or concepts, applying them may prove difficult, if not impossible. Students who have forgotten principles of algebra will be unable to apply them to solve problems or use them as a foundation for learning calculus (or physics, economics, or other related domains), and students who do not remember what operant conditioning is will likely have difficulties applying it to solve behavioral problems. We are not advocating that students spend their time robotically memorizing facts; instead, we are acknowledging the important interplay between memory for a concept on one hand and the ability to comprehend and apply it on the other. An aim of this monograph is to encourage students to use the appropriate learning technique (or techniques) to accomplish a given instructional objective. Some learning techniques are largely focused on bolstering students’ memory for facts (e.g., the keyword mnemonic), others are focused more on improving comprehension (e.g., self-explanation), and yet others may enhance both memory and comprehension (e.g., practice testing). Thus, our review of each learning technique describes how it can be used, its effectiveness for producing long-term retention and comprehension, and its breadth of efficacy across the categories of variables listed in Table 2. Reviewing the Learning Techniques In the following series of reviews, we consider the available evidence for the efficacy of each of the learning techniques. Each review begins with a brief description of the technique and a discussion about why it is expected to improve student learning. We then consider generalizability (with respect to learning conditions, materials, student characteristics, and criterion tasks), highlight any research on the technique that has been conducted in representative educational contexts, and address any identified issues for implementing the technique. Accordingly, the reviews are largely modular: Each of the 10 reviews is organized around these themes (with corresponding headers) so readers can easily identify the most relevant information without necessarily having to read the monograph in its entirety. At the end of each review, we provide an overall assessment for each technique in terms of its relatively utility—low, moderate, or high. Students and teachers who are not already doing so should consider using techniques designated as high utility, because the effects of these techniques are robust and generalize widely. Techniques could have been designated as low utility or moderate utility for any number of reasons. For instance, a technique could have been designated as low utility because its effects are limited to a small subset of materials that students need to learn; the technique may be useful in some cases and adopted in appropriate contexts, but, relative to the other techniques, it would be considered low in utility because of its limited generalizability. A technique could also receive a low- or moderate-utility rating if it showed promise, yet insufficient evidence was available to support confidence in assigning a higher utility assessment. In such cases, we encourage researchers to further explore these techniques within educational settings, but students and teachers may want to use caution before adopting them widely. Most important, given that each utility assessment could have been assigned for a variety of reasons, we discuss the rationale for a given assessment at the end of each review. Finally, our intent was to conduct exhaustive reviews of the literature on each learning technique. For techniques that have been reviewed extensively (e.g., distributed practice), however, we relied on previous reviews and supplemented them with any research that appeared after they had been published. For many of the learning techniques, too many articles have been published to cite them all; therefore, in our discussion of most of the techniques, we cite a subset of relevant articles. 8 1 Elaborative interrogation Anyone who has spent time around young children knows that one of their most frequent utterances is “Why?” (perhaps coming in a close second behind “No!”). Humans are inquisitive creatures by nature, attuned to seeking explanations for states, actions, and events in the world around us. Fortunately, a sizable body of evidence suggests that the power of explanatory questioning can be harnessed to promote learning. Specifically, research on both elaborative interrogation and selfexplanation has shown that prompting students to answer “Why?” questions can facilitate learning. These two literatures are highly related but have mostly developed independently of one another. Additionally, they have overlapping but nonidentical strengths and weaknesses. For these reasons, we consider the two literatures separately. 1.1 General description of elaborative interrogation and why it should work. In one of the earliest systematic studies of elaborative interrogation, Pressley, McDaniel, Turnure, Wood, and Ahmad (1987) presented undergraduate students with a list of sentences, each describing the action of a particular man (e.g., “The hungry man got into the car”). In the elaborative-interrogation group, for each sentence, participants were prompted to explain “Why did that particular man do that?” Another group of participants was instead provided with an explanation for each sentence (e.g., “The hungry man got into the car to go to the restaurant”), and a third group simply read each sentence. On a final test in which participants were cued to recall which man performed each action (e.g., “Who got in the car?”), the elaborative-interrogation group substantially outperformed the other two groups (collapsing across experiments, accuracy in this group was approximately 72%, compared with approximately 37% in each of the other two groups). From this and similar studies, Seifert (1993) reported average effect sizes ranging from 0.85 to 2.57. As illustrated above, the key to elaborative interrogation involves prompting learners to generate an explanation for an explicitly stated fact. The particular form of the explanatory prompt has differed somewhat across studies—examples include “Why does it make sense that…?”, “Why is this true?”, and simply “Why?” However, the majority of studies have used prompts following the general format, “Why would this fact be true of this [X] and not some other [X]?” The prevailing theoretical account of elaborative-interrogation effects is that elaborative interrogation enhances learning by supporting the integration of new information with existing prior knowledge. During elaborative interrogation, learners presumably “activate schemata . . . These schemata, in turn, help to organize new information which facilitates retrieval” (Willoughby & Wood, 1994, p. 140). Although the integration of new facts with prior knowledge may facilitate the organization (Hunt, 2006) of that information, organization alone is not sufficient—students must also be able to discriminate among related facts to be accurate when identifying or using the Dunlosky et al. learned information (Hunt, 2006). Consistent with this account, note that most elaborative-interrogation prompts explicitly or implicitly invite processing of both similarities and differences between related entities (e.g., why a fact would be true of one province versus other provinces). As we highlight below, processing of similarities and differences among to-be-learned facts also accounts for findings that elaborative-interrogation effects are often larger when elaborations are precise rather than imprecise, when prior knowledge is higher rather than lower (consistent with research showing that preexisting knowledge enhances memory by facilitating distinctive processing; e.g., Rawson & Van Overschelde, 2008), and when elaborations are self-generated rather than provided (a finding consistent with research showing that distinctiveness effects depend on self-generating item-specific cues; Hunt & Smith, 1996). 1.2 How general are the effects of elaborative interrogation? 1.2a Learning conditions. The seminal work by Pressley et al. (1987; see also B. S. Stein & Bransford, 1979) spawned a flurry of research in the following decade that was primarily directed at assessing the generalizability of elaborative-interrogation effects. Some of this work focused on investigating elaborative-interrogation effects under various learning conditions. Elaborative-interrogation effects have been consistently shown using either incidental or intentional learning instructions (although two studies have suggested stronger effects for incidental learning: Pressley et al., 1987; Woloshyn, Willoughby, Wood, & Pressley, 1990). Although most studies have involved individual learning, elaborative-interrogation effects have also been shown among students working in dyads or small groups (Kahl & Woloshyn, 1994; Woloshyn & Stockley, 1995). 1.2b Student characteristics. Elaborative-interrogation effects also appear to be relatively robust across different kinds of learners. Although a considerable amount of work has involved undergraduate students, an impressive number of studies have shown elaborative-interrogation effects with younger learners as well. Elaborative interrogation has been shown to improve learning for high school students, middle school students, and upper elementary school students (fourth through sixth graders). The extent to which elaborative interrogation benefits younger learners is less clear. Miller and Pressley (1989) did not find effects for kindergartners or first graders, and Wood, Miller, Symons, Canough, and Yedlicka (1993) reported mixed results for preschoolers. Nonetheless, elaborative interrogation does appear to benefit learners across a relatively wide age range. Furthermore, several of the studies involving younger students have also established elaborative-interrogation effects for learners of varying ability levels, including fourth through twelfth graders with learning disabilities (C. Greene, Symons, & Richards, 1996; Scruggs, Mastropieri, & Sullivan, 1994) and sixth through eighth graders with mild 9 Improving Student Achievement cognitive disabilities (Scruggs, Mastropieri, Sullivan, & Hesser, 1993), although Wood, Willoughby, Bolger, Younger, and Kaspar (1993) did not find effects with a sample of lowachieving students. On the other end of the continuum, elaborative-interrogation effects have been shown for high-achieving fifth and sixth graders (Wood & Hewitt, 1993; Wood, Willoughby, et al., 1993). Another key dimension along which learners differ is level of prior knowledge, a factor that has been extensively investigated within the literature on elaborative interrogation. Both correlational and experimental evidence suggest that prior knowledge is an important moderator of elaborative-interrogation effects, such that effects generally increase as prior knowledge increases. For example, Woloshyn, Pressley, and Schneider (1992) presented Canadian and German students with facts about Canadian provinces and German states. Thus, both groups of students had more domain knowledge for one set of facts and less domain knowledge for the other set. As shown in Figure 1, students showed larger effects of elaborative interrogation in their high-knowledge domain (a 24% increase) than in their low-knowledge domain (a 12% increase). Other studies manipulating the familiarity of to-belearned materials have reported similar patterns, with significant effects for new facts about familiar items but weaker or nonexistent effects for facts about unfamiliar items. Despite some exceptions (e.g., Ozgungor & Guthrie, 2004), the overall conclusion that emerges from the literature is that high-knowledge learners will generally be best equipped to profit from the elaborative-interrogation technique. The benefit for lowerknowledge learners is less certain. One intuitive explanation for why prior knowledge moderates the effects of elaborative interrogation is that higher Elaborative Interrogation Reading Control 80 Final-Test Performance (%) 70 60 50 40 30 20 10 0 High Knowledge Low Knowledge Fig. 1. Mean percentage of correct responses on a final test for learners with high or low domain knowledge who engaged in elaborative interrogation or in reading only during learning (in Woloshyn, Pressley, & Schneider, 1992). Standard errors are not available. knowledge permits the generation of more appropriate explanations for why a fact is true. If so, one might expect final-test performance to vary as a function of the quality of the explanations generated during study. However, the evidence is mixed. Whereas some studies have found that test performance is better following adequate elaborative-interrogation responses (i.e., those that include a precise, plausible, or accurate explanation for a fact) than for inadequate responses, the differences have often been small, and other studies have failed to find differences (although the numerical trends are usually in the anticipated direction). A somewhat more consistent finding is that performance is better following an adequate response than no response, although in this case, too, the results are somewhat mixed. More generally, the available evidence should be interpreted with caution, given that outcomes are based on conditional post hoc analyses that likely reflect item-selection effects. Thus, the extent to which elaborative-interrogation effects depend on the quality of the elaborations generated is still an open question. 1.2c Materials. Although several studies have replicated elaborative-interrogation effects using the relatively artificial “man sentences” used by Pressley et al. (1987), the majority of subsequent research has extended these effects using materials that better represent what students are actually expected to learn. The most commonly used materials involved sets of facts about various familiar and unfamiliar animals (e.g., “The Western Spotted Skunk’s hole is usually found on a sandy piece of farmland near crops”), usually with an elaborativeinterrogation prompt following the presentation of each fact. Other studies have extended elaborative-interrogation effects to fact lists from other content domains, including facts about U.S. states, German states, Canadian provinces, and universities; possible reasons for dinosaur extinction; and gender-specific facts about men and women. Other studies have shown elaborative-interrogation effects for factual statements about various topics (e.g., the solar system) that are normatively consistent or inconsistent with learners’ prior beliefs (e.g., Woloshyn, Paivio, & Pressley, 1994). Effects have also been shown for facts contained in longer connected discourse, including expository texts on animals (e.g., Seifert, 1994); human digestion (B. L. Smith, Holliday, & Austin, 2010); the neuropsychology of phantom pain (Ozgungor & Guthrie, 2004); retail, merchandising, and accounting (Dornisch & Sperling, 2006); and various science concepts (McDaniel & Donnelly, 1996). Thus, elaborative-interrogation effects are relatively robust across factual material of different kinds and with different contents. However, it is important to note that elaborative interrogation has been applied (and may be applicable) only to discrete units of factual information. 1.2d Criterion tasks. Whereas elaborative-interrogation effects appear to be relatively robust across materials and learners, the extensions of elaborative-interrogation effects across measures that tap different kinds or levels of learning is somewhat more limited. With only a few exceptions, the majority of elaborative-interrogation studies have relied on the 10 following associative-memory measures: cued recall (generally involving the presentation of a fact to prompt recall of the entity for which the fact is true; e.g., “Which animal . . . ?”) and matching (in which learners are presented with lists of facts and entities and must match each fact with the correct entity). Effects have also been shown on measures of fact recognition (B. L. Smith et al., 2010; Woloshyn et al., 1994; Woloshyn & Stockley, 1995). Concerning more generative measures, a few studies have also found elaborative-interrogation effects on free-recall tests (e.g., Woloshyn & Stockley, 1995; Woloshyn et al., 1994), but other studies have not (Dornisch & Sperling, 2006; McDaniel & Donnelly, 1996). All of the aforementioned measures primarily reflect memory for explicitly stated information. Only three studies have used measures tapping comprehension or application of the factual information. All three studies reported elaborativeinterrogation effects on either multiple-choice or verification tests that required inferences or higher-level integration (Dornisch & Sperling, 2006; McDaniel & Donnelly, 1996; Ozgungor & Guthrie, 2004). Ozgungor and Guthrie (2004) also found that elaborative interrogation improved performance on a concept-relatedness rating task (in brief, students rated the pairwise relatedness of the key concepts from a passage, and rating coherence was assessed via Pathfinder analyses); however, Dornisch and Sperling (2006) did not find significant elaborative-interrogation effects on a problemsolving test. In sum, whereas elaborative-interrogation effects on associative memory have been firmly established, the extent to which elaborative interrogation facilitates recall or comprehension is less certain. Of even greater concern than the limited array of measures that have been used is the fact that few studies have examined performance after meaningful delays. Almost all prior studies have administered outcome measures either immediately or within a few minutes of the learning phase. Results from the few studies that have used longer retention intervals are promising. Elaborative-interrogation effects have been shown after delays of 1–2 weeks (Scruggs et al., 1994; Woloshyn et al., 1994), 1–2 months (Kahl & Woloshyn, 1994; Willoughby, Waller, Wood, & MacKinnon, 1993; Woloshyn & Stockley, 1995), and even 75 and 180 days (Woloshyn et al., 1994). In almost all of these studies, however, the delayed test was preceded by one or more criterion tests at shorter intervals, introducing the possibility that performance on the delayed test was contaminated by the practice provided by the preceding tests. Thus, further work is needed before any definitive conclusions can be drawn about the extent to which elaborative interrogation produces durable gains in learning. 1.3 Effects in representative educational contexts. Concerning the evidence that elaborative interrogation will enhance learning in representative educational contexts, few studies have been conducted outside the laboratory. However, outcomes from a recent study are suggestive (B. L. Smith et al., 2010). Participants were undergraduates enrolled in an Dunlosky et al. introductory biology course, and the experiment was conducted during class meetings in the accompanying lab section. During one class meeting, students completed a measure of verbal ability and a prior-knowledge test over material that was related, but not identical, to the target material. In the following week, students were presented with a lengthy text on human digestion that was taken from a chapter in the course textbook. For half of the students, 21 elaborative interrogation prompts were interspersed throughout the text (roughly one prompt per 150 words), each consisting of a paraphrased statement from the text followed by “Why is this true?” The remaining students were simply instructed to study the text at their own pace, without any prompts. All students then completed 105 true/false questions about the material (none of which were the same as the elaborative-interrogation prompts). Performance was better for the elaborative-interrogation group than for the control group (76% versus 69%), even after controlling for prior knowledge and verbal ability. 1.4 Issues for implementation. One possible merit of elaborative interrogation is that it apparently requires minimal training. In the majority of studies reporting elaborative-interrogation effects, learners were given brief instructions and then practiced generating elaborations for 3 or 4 practice facts (sometimes, but not always, with feedback about the quality of the elaborations) before beginning the main task. In some studies, learners were not provided with any practice or illustrative examples prior to the main task. Additionally, elaborative interrogation appears to be relatively reasonable with respect to time demands. Almost all studies set reasonable limits on the amount of time allotted for reading a fact and for generating an elaboration (e.g., 15 seconds allotted for each fact). In one of the few studies permitting self-paced learning, the time-on-task difference between the elaborative-interrogation and reading-only groups was relatively minimal (32 minutes vs. 28 minutes; B. L. Smith et al., 2010). Finally, the consistency of the prompts used across studies allows for relatively straightforward recommendations to students about the nature of the questions they should use to elaborate on facts during study. With that said, one limitation noted above concerns the potentially narrow applicability of elaborative interrogation to discrete factual statements. As Hamilton (1997) noted, “elaborative interrogation is fairly prescribed when focusing on a list of factual sentences. However, when focusing on more complex outcomes, it is not as clear to what one should direct the ‘why’ questions” (p. 308). For example, when learning about a complex causal process or system (e.g., the digestive system), the appropriate grain size for elaborative interrogation is an open question (e.g., should a prompt focus on an entire system or just a smaller part of it?). Furthermore, whereas the facts to be elaborated are clear when dealing with fact lists, elaborating on facts embedded in lengthier texts will require students to identify their own target facts. Thus, students may need some instruction about the kinds of content to which 11 Improving Student Achievement elaborative interrogation may be fruitfully applied. Dosage is also of concern with lengthier text, with some evidence suggesting that elaborative-interrogation effects are substantially diluted (Callender & McDaniel, 2007) or even reversed (Ramsay, Sperling, & Dornisch, 2010) when elaborative-interrogation prompts are administered infrequently (e.g., one prompt every 1 or 2 pages). 1.5 Elaborative interrogation: Overall assessment. We rate elaborative interrogation as having moderate utility. Elaborative-interrogation effects have been shown across a relatively broad range of factual topics, although some concerns remain about the applicability of elaborative interrogation to material that is lengthier or more complex than fact lists. Concerning learner characteristics, effects of elaborative interrogation have been consistently documented for learners at least as young as upper elementary age, but some evidence suggests that the benefits of elaborative interrogation may be limited for learners with low levels of domain knowledge. Concerning criterion tasks, elaborative-interrogation effects have been firmly established on measures of associative memory administered after short delays, but firm conclusions about the extent to which elaborative interrogation benefits comprehension or the extent to which elaborative-interrogation effects persist across longer delays await further research. Further research demonstrating the efficacy of elaborative interrogation in representative educational contexts would also be useful. In sum, the need for further research to establish the generalizability of elaborative-interrogation effects is primarily why this technique did not receive a high-utility rating. when the logical rules were instantiated in a set of abstract problems presented during a subsequent transfer test, the two self-explanation groups substantially outperformed the control group (see Fig. 2). In a second experiment, another control group was explicitly told about the logical connection between the concrete practice problems they had just solved and the forthcoming abstract problems, but they fared no better (28%). As illustrated above, the core component of self-explanation involves having students explain some aspect of their processing during learning. Consistent with basic theoretical assumptions about the related technique of elaborative interrogation, self-explanation may enhance learning by supporting the integration of new information with existing prior knowledge. However, compared with the consistent prompts used in the elaborative-interrogation literature, the prompts used to elicit self-explanations have been much more variable across studies. Depending on the variation of the prompt used, the particular mechanisms underlying self-explanation effects may differ somewhat. The key continuum along which selfexplanation prompts differ concerns the degree to which they are content-free versus content-specific. For example, many studies have used prompts that include no explicit mention of particular content from the to-be-learned materials (e.g., “Explain what the sentence means to you. That is, what new information does the sentence provide for you? And how does it relate to what you already know?”). On the other end of the continuum, many studies have used prompts that are much more content-specific, such that different prompts are used for Concurrent Self-Explanation Retrospective Self-Explanation 2 Self-explanation No Self-Explanation 100 90 Problem Solving Accuracy (%) 2.1 General description of self-explanation and why it should work. In the seminal study on self-explanation, Berry (1983) explored its effects on logical reasoning using the Wason card-selection task. In this task, a student might see four cards labeled “A,” “4,” “D,” and “3″ and be asked to indicate which cards must be turned over to test the rule “if a card has A on one side, it has 3 on the other side” (an instantiation of the more general “if P, then Q” rule). Students were first asked to solve a concrete instantiation of the rule (e.g., flavor of jam on one side of a jar and the sale price on the other); accuracy was near zero. They then were provided with a minimal explanation about how to solve the “if P, then Q” rule and were given a set of concrete problems involving the use of this and other logical rules (e.g., “if P, then not Q”). For this set of concrete practice problems, one group of students was prompted to self-explain while solving each problem by stating the reasons for choosing or not choosing each card. Another group of students solved all problems in the set and only then were asked to explain how they had gone about solving the problems. Students in a control group were not prompted to self-explain at any point. Accuracy on the practice problems was 90% or better in all three groups. However, 80 70 60 50 40 30 20 10 0 Concrete Practice Problems Abstract Transfer Problems Fig. 2. Mean percentage of logical-reasoning problems answered correctly for concrete practice problems and subsequently administered abstract transfer problems in Berry (1983). During a practice phase, learners self-explained while solving each problem, self-explained after solving all problems, or were not prompted to engage in self-explanation. Standard errors are not available. 12 different items (e.g., “Why do you calculate the total acceptable outcomes by multiplying?” “Why is the numerator 14 and the denominator 7 in this step?”). For present purposes, we limit our review to studies that have used prompts that are relatively content-free. Although many of the content-specific prompts do elicit explanations, the relatively structured nature of these prompts would require teachers to construct sets of specific prompts to put into practice, rather than capturing a more general technique that students could be taught to use on their own. Furthermore, in some studies that have been situated in the self-explanation literature, the nature of the prompts is functionally more closely aligned with that of practice testing. Even within the set of studies selected for review here, considerable variability remains in the self-explanation prompts that have been used. Furthermore, the range of tasks and measures that have been used to explore self-explanation is quite large. Although we view this range as a strength of the literature, the variability in self-explanation prompts, tasks, and measures does not easily support a general summative statement about the mechanisms that underlie self-explanation effects. 2.2 How general are the effects of self-explanation? 2.2a Learning conditions. Several studies have manipulated other aspects of learning conditions in addition to selfexplanation. For example, Rittle-Johnson (2006) found that self-explanation was effective when accompanied by either direct instruction or discovery learning. Concerning potential moderating factors, Berry (1983) included a group who self-explained after the completion of each problem rather than during problem solving. Retrospective self-explanation did enhance performance relative to no self-explanation, but the effects were not as pronounced as with concurrent selfexplanation. Another moderating factor may concern the extent to which provided explanations are made available to learners. Schworm and Renkl (2006) found that self-explanation effects were significantly diminished when learners could access explanations, presumably because learners made minimal attempts to answer the explanatory prompts before consulting the provided information (see also Aleven & Koedinger, 2002). 2.2b Student characteristics. Self-explanation effects have been shown with both younger and older learners. Indeed, self-explanation research has relied much less heavily on samples of college students than most other literatures have, with at least as many studies involving younger learners as involving undergraduates. Several studies have reported selfexplanation effects with kindergartners, and other studies have shown effects for elementary school students, middle school students, and high school students. In contrast to the breadth of age groups examined, the extent to which the effects of self-explanation generalize across different levels of prior knowledge or ability has not been sufficiently explored. Concerning knowledge level, Dunlosky et al. several studies have used pretests to select participants with relatively low levels of knowledge or task experience, but no research has systematically examined self-explanation effects as a function of knowledge level. Concerning ability level, Chi, de Leeuw, Chiu, and LaVancher (1994) examined the effects of self-explanation on learning from an expository text about the circulatory system among participants in their sample who had received the highest and lowest scores on a measure of general aptitude and found gains of similar magnitude in each group. In contrast, Didierjean and Cauzinille-Marmèche (1997) examined algebra-problem solving in a sample of ninth graders with either low or intermediate algebra skills, and they found self-explanation effects only for lower-skill students. Further work is needed to establish the generality of self-explanation effects across these important idiographic dimensions. 2.2c Materials. One of the strengths of the self-explanation literature is that effects have been shown not only across different materials within a task domain but also across several different task domains. In addition to the logical-reasoning problems used by Berry (1983), self-explanation has been shown to support the solving of other kinds of logic puzzles. Self-explanation has also been shown to facilitate the solving of various kinds of math problems, including simple addition problems for kindergartners, mathematical-equivalence problems for elementary-age students, and algebraic formulas and geometric theorems for older learners. In addition to improving problem solving, self-explanation improved student teachers’ evaluation of the goodness of practice problems for use in classroom instruction. Self-explanation has also helped younger learners overcome various kinds of misconceptions, improving children’s understanding of false belief (i.e., that individuals can have a belief that is different from reality), number conservation (i.e., that the number of objects in an array does not change when the positions of those objects in the array change), and principles of balance (e.g., that not all objects balance on a fulcrum at their center point). Selfexplanation has improved children’s pattern learning and adults’ learning of endgame strategies in chess. Although most of the research on self-explanation has involved procedural or problem-solving tasks, several studies have also shown selfexplanation effects for learning from text, including both short narratives and lengthier expository texts. Thus, self-explanation appears to be broadly applicable. 2.2d Criterion tasks. Given the range of tasks and domains in which self-explanation has been investigated, it is perhaps not surprising that self-explanation effects have been shown on a wide range of criterion measures. Some studies have shown self-explanation effects on standard measures of memory, including free recall, cued recall, fill-in-the-blank tests, associative matching, and multiple-choice tests tapping explicitly stated information. Studies involving text learning have also shown effects on measures of comprehension, including diagram-drawing tasks, application-based questions, and tasks in which learners must make inferences on the basis of Improving Student Achievement information implied but not explicitly stated in a text. Across those studies involving some form of problem-solving task, virtually every study has shown self-explanation effects on near-transfer tests in which students are asked to solve problems that have the same structure as, but are nonidentical to, the practice problems. Additionally, self-explanation effects on far-transfer tests (in which students are asked to solve problems that differ from practice problems not only in their surface features but also in one or more structural aspects) have been shown for the solving of math problems and pattern learning. Thus, self-explanation facilitates an impressive range of learning outcomes. In contrast, the durability of self-explanation effects is woefully underexplored. Almost every study to date has administered criterion tests within minutes of completion of the learning phase. Only five studies have used longer retention intervals. Self-explanation effects persisted across 1–2 day delays for playing chess endgames (de Bruin, Rikers, & Schmidt, 2007) and for retention of short narratives (Magliano, Trabasso, & Graesser, 1999). Self-explanation effects persisted across a 1-week delay for the learning of geometric theorems (although an additional study session intervened between initial learning and the final test; R. M. F. Wong, Lawson, & Keeves, 2002) and for learning from a text on the circulatory system (although the final test was an open-book test; Chi et al., 1994). Finally, Rittle-Johnson (2006) reported significant effects on performance in solving math problems after a 2-week delay; however, the participants in this study also completed an immediate test, thus introducing the possibility that testing effects influenced performance on the delayed test. Taken together, the outcomes of these few studies are promising, but considerably more research is needed before confident conclusions can be made about the longevity of self-explanation effects. 2.3 Effects in representative educational contexts. Concerning the strength of the evidence that self-explanation will enhance learning in educational contexts, outcomes from two studies in which participants were asked to learn course-relevant content are at least suggestive. In a study by Schworm and Renkl (2006), students in a teacher-education program learned how to develop example problems to use in their classrooms by studying samples of well-designed and poorly designed example problems in a computer program. On each trial, students in a self-explanation group were prompted to explain why one of two examples was more effective than the other, whereas students in a control group were not prompted to selfexplain. Half of the participants in each group were also given the option to examine experimenter-provided explanations on each trial. On an immediate test in which participants selected and developed example problems, the self-explanation group outperformed the control group. However, this effect was limited to students who had not been able to view provided explanations, presumably because students made minimal attempts to self-explain before consulting the provided information. 13 R. M. F. Wong et al. (2002) presented ninth-grade students in a geometry class with a theorem from the course textbook that had not yet been studied in class. During the initial learning session, students were asked to think aloud while studying the relevant material (including the theorem, an illustration of its proof, and an example of an application of the theorem to a problem). Half of the students were specifically prompted to self-explain after every 1 or 2 lines of new information (e.g., “What parts of this page are new to me? What does the statement mean? Is there anything I still don’t understand?”), whereas students in a control group received nonspecific instructions that simply prompted them to think aloud during study. The following week, all students received a basic review of the theorem and completed the final test the next day. Selfexplanation did not improve performance on near-transfer questions but did improve performance on far-transfer questions. 2.4 Issues for implementation. As noted above, a particular strength of the self-explanation strategy is its broad applicability across a range of tasks and content domains. Furthermore, in almost all of the studies reporting significant effects of selfexplanation, participants were provided with minimal instructions and little to no practice with self-explanation prior to completing the experimental task. Thus, most students apparently can profit from self-explanation with minimal training. However, some students may require more instruction to successfully implement self-explanation. In a study by Didierjean and Cauzinille-Marmèche (1997), ninth graders with poor algebra skills received minimal training prior to engaging in self-explanation while solving algebra problems; analysis of think-aloud protocols revealed that students produced many more paraphrases than explanations. Several studies have reported positive correlations between final-test performance and both the quantity and quality of explanations generated by students during learning, further suggesting that the benefit of self-explanation might be enhanced by teaching students how to effectively implement the self-explanation technique (for examples of training methods, see Ainsworth & Burcham, 2007; R. M. F. Wong et al., 2002). However, in at least some of these studies, students who produced more or better-quality self-explanations may have had greater domain knowledge; if so, then further training with the technique may not have benefited the more poorly performing students. Investigating the contribution of these factors (skill at self-explanation vs. domain knowledge) to the efficacy of self-explanation will have important implications for how and when to use this technique. An outstanding issue concerns the time demands associated with self-explanation and the extent to which self-explanation effects may have been due to increased time on task. Unfortunately, few studies equated time on task when comparing selfexplanation conditions to control conditions involving other strategies or activities, and most studies involving self-paced practice did not report participants’ time on task. In the few 14 studies reporting time on task, self-paced administration usually yielded nontrivial increases (30–100%) in the amount of time spent learning in the self-explanation condition relative to other conditions, a result that is perhaps not surprising, given the high dosage levels at which self-explanation was implemented. For example, Chi et al. (1994) prompted learners to self-explain after reading each sentence of an expository text, which doubled the amount of time the group spent studying the text relative to a rereading control group (125 vs. 66 minutes, respectively). With that said, Schworm and Renkl (2006) reported that time on task was not correlated with performance across groups, and Ainsworth and Burcham (2007) reported that controlling for study time did not eliminate effects of self-explanation. Within the small number of studies in which time on task was equated, results were somewhat mixed. Three studies equating time on task reported significant effects of selfexplanation (de Bruin et al., 2007; de Koning, Tabbers, Rikers, & Paas, 2011; O’Reilly, Symons, & MacLatchy-Gaudet, 1998). In contrast, Matthews and Rittle-Johnson (2009) had one group of third through fifth graders practice solving math problems with self-explanation and a control group solve twice as many practice problems without self-explanation; the two groups performed similarly on a final test. Clearly, further research is needed to establish the bang for the buck provided by self-explanation before strong prescriptive conclusions can be made. 2.5 Self-explanation: Overall assessment. We rate selfexplanation as having moderate utility. A major strength of this technique is that its effects have been shown across different content materials within task domains as well as across several different task domains. Self-explanation effects have also been shown across an impressive age range, although further work is needed to explore the extent to which these effects depend on learners’ knowledge or ability level. Self-explanation effects have also been shown across an impressive range of learning outcomes, including various measures of memory, comprehension, and transfer. In contrast, further research is needed to establish the durability of these effects across educationally relevant delays and to establish the efficacy of selfexplanation in representative educational contexts. Although most research has shown effects of self-explanation with minimal training, some results have suggested that effects may be enhanced if students are taught how to effectively implement the self-explanation strategy. One final concern has to do with the nontrivial time demands associated with self-explanation, at least at the dosages examined in most of the research that has shown effects of this strategy. 3 Summarization Students often have to learn large amounts of information, which requires them to identify what is important and how different ideas connect to one another. One popular technique for Dunlosky et al. accomplishing these goals involves having students write summaries of to-be-learned texts. Successful summaries identify the main points of a text and capture the gist of it while excluding unimportant or repetitive material (A. L. Brown, Campione, & Day, 1981). Although learning to construct accurate summaries is often an instructional goal in its own right (e.g., Wade-Stein & Kintsch, 2004), our interest here concerns whether doing so will boost students’ performance on later criterion tests that cover the target material. 3.1 General description of summarization and why it should work. As an introduction to the issues relevant to summarization, we begin with a description of a prototypical experiment. Bretzing and Kulhavy (1979) had high school juniors and seniors study a 2,000-word text about a fictitious tribe of people. Students were assigned to one of five learning conditions and given up to 30 minutes to study the text. After reading each page, students in a summarization group were instructed to write three lines of text that summarized the main points from that page. Students in a note-taking group received similar instructions, except that they were told to take up to three lines of notes on each page of text while reading. Students in a verbatim-copying group were instructed to locate and copy the three most important lines on each page. Students in a letter-search group copied all the capitalized words in the text, also filling up three lines. Finally, students in a control group simply read the text without recording anything. (A subset of students from the four conditions involving writing were allowed to review what they had written, but for present purposes we will focus on the students who did not get a chance to review before the final test.) Students were tested either shortly after learning or 1 week later, answering 25 questions that required them to connect information from across the text. On both the immediate and delayed tests, students in the summarization and note-taking groups performed best, followed by the students in the verbatim-copying and control groups, with the worst performance in the letter-search group (see Fig. 3). Bretzing and Kulhavy’s (1979) results fit nicely with the claim that summarization boosts learning and retention because it involves attending to and extracting the higher-level meaning and gist of the material. The conditions in the experiment were specifically designed to manipulate how much students processed the texts for meaning, with the letter-search condition involving shallow processing of the text that did not require learners to extract its meaning (Craik & Lockhart, 1972). Summarization was more beneficial than that shallow task and yielded benefits similar to those of note-taking, another task known to boost learning (e.g., Bretzing & Kulhavy, 1981; Crawford, 1925a, 1925b; Di Vesta & Gray, 1972). More than just facilitating the extraction of meaning, however, summarization should also boost organizational processing, given that extracting the gist of a text requires learners to connect disparate pieces of the text, as opposed to simply evaluating its individual components (similar to the way in which note-taking affords organizational processing; Einstein, 15 Improving Student Achievement Summarization Note-Taking Verbatim Letter Search 16 Control Number Correct (out of 25) 14 12 10 8 6 4 2 0 Immediate Test Delayed Test Fig. 3. Mean number of correct responses on a test occurring shortly after study as a function of test type (immediate or delayed) and learning condition in Bretzing and Kulhavy (1979). Error bars represent standard errors. Morris, & Smith, 1985). One last point should be made about the results from Bretzing and Kulhavy (1979)—namely, that summarization and note-taking were both more beneficial than was verbatim copying. Students in the verbatim-copying group still had to locate the most important information in the text, but they did not synthesize it into a summary or rephrase it in their notes. Thus, writing about the important points in one’s own words produced a benefit over and above that of selecting important information; students benefited from the more active processing involved in summarization and notetaking (see Wittrock, 1990, and Chi, 2009, for reviews of active/generative learning). These explanations all suggest that summarization helps students identify and organize the main ideas within a text. So how strong is the evidence that summarization is a beneficial learning strategy? One reason this question is difficult to answer is that the summarization strategy has been implemented in many different ways across studies, making it difficult to draw general conclusions about its efficacy. Pressley and colleagues described the situation well when they noted that “summarization is not one strategy but a family of strategies” (Pressley, Johnson, Symons, McGoldrick, & Kurita, 1989, p. 5). Depending on the particular instructions given, students’ summaries might consist of single words, sentences, or longer paragraphs; be limited in length or not; capture an entire text or only a portion of it; be written or spoken aloud; or be produced from memory or with the text present. A lot of research has involved summarization in some form, yet whereas some evidence demonstrates that summarization works (e.g., L. W. Brooks, Dansereau, Holley, & Spurlin, 1983; Doctorow, Wittrock, & Marks, 1978), T. H. Anderson and Armbruster’s (1984) conclusion that “research in support of summarizing as a studying activity is sparse indeed” (p. 670) is not outmoded. Instead of focusing on discovering when (and how) summarization works, by itself and without training, researchers have tended to explore how to train students to write better summaries (e.g., Friend, 2001; Hare & Borchardt, 1984) or to examine other benefits of training the skill of summarization. Still others have simply assumed that summarization works, including it as a component in larger interventions (e.g., Carr, Bigler, & Morningstar, 1991; Lee, Lim, & Grabowski, 2010; Palincsar & Brown, 1984; Spörer, Brunstein, & Kieschke, 2009). When collapsing across findings pertaining to all forms of summarization, summarization appears to benefit students, but the evidence for any one instantiation of the strategy is less compelling. The focus on training students to summarize reflects the belief that the quality of summaries matters. If a summary does not emphasize the main points of a text, or if it includes incorrect information, why would it be expected to benefit learning and retention? Consider a study by Bednall and Kehoe (2011, Experiment 2), in which undergraduates studied six Web units that explained different logical fallacies and provided examples of each. Of interest for present purposes are two groups: a control group who simply read the units and a group in which students were asked to summarize the material as if they were explaining it to a friend. Both groups received the following tests: a multiple-choice quiz that tested information directly stated in the Web unit; a short-answer test in which, for each of a list of presented statements, students were required to name the specific fallacy that had been committed or write “not a fallacy” if one had not occurred; and, finally, an application test that required students to write explanations of logical fallacies in examples that had been studied (near transfer) as well as explanations of fallacies in novel examples (far transfer). Summarization did not benefit overall performance, but the researchers noticed that the summaries varied a lot in content; for one studied fallacy, only 64% of the summaries included the correct definition. Table 3 shows the relationships between summary content and later performance. Higher-quality summaries that contained more information and that were linked to prior knowledge were associated with better performance. Several other studies have supported the claim that the quality of summaries has consequences for later performance. Most similar to the Bednall and Kehoe (2011) result is Ross and Di Vesta’s (1976) finding that the length (in words) of an oral summary (a very rough indicator of quality) correlated with later performance on multiple-choice and short-answer questions. Similarly, Dyer, Riley, and Yekovich (1979) found that final-test questions were more likely to be answered correctly if the information needed to answer them had been included in an earlier summary. Garner (1982) used a different 16 Dunlosky et al. Table 3. Correlations between Measures of Summary Quality and Later Test Performance (from Bednall & Kehoe, 2011, Experiment 2) Test Measure of summary quality Number of correct definitions Amount of extra information Multiple-choice test (factual knowledge) Short-answer test (identification) Application test .42* .31* .43* .21* .52* .40* Note. Asterisks indicate correlations significantly greater than 0. “Amount of extra information” refers to the number of summaries in which a student included information that had not been provided in the studied material (e.g., an extra example). method to show that the quality of summaries matters: Undergraduates read a passage on Dutch elm disease and then wrote a summary at the bottom of the page. Five days later, the students took an old/new recognition test; critical items were new statements that captured the gist of the passage (as in Bransford & Franks, 1971). Students who wrote better summaries (i.e., summaries that captured more important information) were more likely to falsely recognize these gist statements, a pattern suggesting that the students had extracted a higherlevel understanding of the main ideas of the text. 3.2 How general are the effects of summarization? 3.2a Learning conditions. As noted already, many different types of summaries can influence learning and retention; summarization can be simple, requiring the generation of only a heading (e.g., L. W. Brooks et al., 1983) or a single sentence per paragraph of a text (e.g., Doctorow et al., 1978), or it can be as complicated as an oral presentation on an entire set of studied material (e.g., Ross & Di Vesta, 1976). Whether it is better to summarize smaller pieces of a text (more frequent summarization) or to capture more of the text in a larger summary (less frequent summarization) has been debated (Foos, 1995; Spurlin, Dansereau, O’Donnell, & Brooks, 1988). The debate remains unresolved, perhaps because what constitutes the most effective summary for a text likely depends on many factors (including students’ ability and the nature of the material). One other open question involves whether studied material should be present during summarization. Hidi and Anderson (1986) pointed out that having the text present might help the reader to succeed at identifying its most important points as well as relating parts of the text to one another. However, summarizing a text without having it present involves retrieval, which is known to benefit memory (see the Practice Testing section of this monograph), and also prevents the learner from engaging in verbatim copying. The Dyer et al. (1979) study described earlier involved summarizing without the text present; in this study, no overall benefit from summarizing occurred, even though information that had been included in summaries was benefited (overall, this benefit was overshadowed by costs to the greater amount of information that had not been included in summaries). More generally, some studies have shown benefits from summarizing an absent text (e.g., Ross & Di Vesta, 1976), but some have not (e.g., M. C. M. Anderson & Thiede, 2008, and Thiede & Anderson, 2003, found no benefits of summarization on test performance). The answer to whether studied text should be present during summarization is most likely a complicated one, and it may depend on people’s ability to summarize when the text is absent. 3.2b Student characteristics. Benefits of summarization have primarily been observed with undergraduates. Most of the research on individual differences has focused on the age of students, because the ability to summarize develops with age. Younger students struggle to identify main ideas and tend to write lower-quality summaries that retain more of the original wording and structure of a text (e.g., A. L. Brown & Day, 1983; A. L. Brown, Day, & Jones, 1983). However, younger students (e.g., middle school students) can benefit from summarization following extensive training (e.g., Armbruster, Anderson, & Ostertag, 1987; Bean & Steenwyk, 1984). For example, consider a successful program for sixth-grade students (Rinehart, Stahl, & Erickson, 1986). Teachers received 90 minutes of training so that they could implement summarization training in their classrooms; students then completed five 45- to 50-minute sessions of training. The training reflected principles of direct instruction, meaning that students were explicitly taught about the strategy, saw it modeled, practiced it and received feedback, and eventually learned to monitor and check their work. Students who had received the training recalled more major information from a textbook chapter (i.e., information identified by teachers as the most important for students to know) than did students who had not, and this benefit was linked to improvements in note-taking. Similar training programs have succeeded with middle school students who are learning disabled (e.g., Gajria & Salvia, 1992; Malone & Mastropieri, 1991), minority high school students (Hare & Borchardt, 1984), and underprepared college students (A. King, 1992). Outcomes of two other studies have implications for the generality of the summarization strategy, as they involve individual differences in summarization skill (a prerequisite for Improving Student Achievement using the strategy). First, both general writing skill and interest in a topic have been linked to summarization ability in seventh graders (Head, Readence, & Buss, 1989). Writing skill was measured via performance on an unrelated essay, and interest in the topic (American history) was measured via a survey that asked students how much they would like to learn about each of 25 topics. Of course, interest may be confounded with knowledge about a topic, and knowledge may also contribute to summarization skill. Recht and Leslie (1988) showed that seventh- and eighth-grade students who knew a lot about baseball (as measured by a pretest) were better at summarizing a 625-word passage about a baseball game than were students who knew less about baseball. This finding needs to be replicated with different materials, but it seems plausible that students with more domain-relevant knowledge would be better able to identify the main points of a text and extract its gist. The question is whether domain experts would benefit from the summarization strategy or whether it would be redundant with the processing in which these students would spontaneously engage. 3.2c Materials. The majority of studies have used prose passages on such diverse topics as a fictitious primitive tribe, desert life, geology, the blue shark, an earthquake in Lisbon, the history of Switzerland, and fictional stories. These passages have ranged in length from a few hundred words to a few thousand words. Other materials have included Web modules and lectures. For the most part, characteristics of materials have not been systematically manipulated, which makes it difficult to draw strong conclusions about this factor, even though 15 years have passed since Hidi and Anderson (1986) made an argument for its probable importance. As discussed in Yu (2009), it makes sense that the length, readability, and organization of a text might all influence a reader’s ability to summarize it, but these factors need to be investigated in studies that manipulate them while holding all other factors constant (as opposed to comparing texts that vary along multiple dimensions). 3.2d Criterion tasks. The majority of summarization studies have examined the effects of summarization on either retention of factual details or comprehension of a text (often requiring inferences) through performance on multiple-choice questions, cued recall questions, or free recall. Other benefits of summarization include enhanced metacognition (with textabsent summarization improving the extent to which readers can accurately evaluate what they do or do not know; M. C. M. Anderson & Thiede, 2008; Thiede & Anderson, 2003) and improved note-taking following training (A. King, 1992; Rinehart et al., 1986). Whereas several studies have shown benefits of summarization (sometimes following training) on measures of application (e.g., B. Y. L. Wong, Wong, Perry, & Sawatsky, 1986), others have failed to find such benefits. For example, consider a study in which L. F. Annis (1985) had undergraduates read a passage on an earthquake and then examined the consequences of summarization for performance on questions designed to 17 tap different categories of learning within Bloom et al.’s (1956) taxonomy. One week after learning, students who had summarized performed no differently than students in a control group who had only read the passages in answering questions that tapped a basic level of knowledge (fact and comprehension questions). Students benefited from summarization when the questions required the application or analysis of knowledge, but summarization led to worse performance on evaluation and synthesis questions. These results need to be replicated, but they highlight the need to assess the consequences of summarization on the performance of tasks that measure various levels of Bloom’s taxonomy. Across studies, results have also indicated that summarization helps later performance on generative measures (e.g., free recall, essays) more than it affects performance on multiplechoice or other measures that do not require the student to produce information (e.g., Bednall & Kehoe, 2011; L. W. Brooks et al., 1983; J. R. King, Biggs, & Lipsky, 1984). Because summarizing requires production, the processing involved is likely a better match to generative tests than to tests that depend on recognition. Unfortunately, the one study we found that used a highstakes test did not show a benefit from summarization training (Brozo, Stahl, & Gordon, 1985). Of interest for present purposes were two groups in the study, which was conducted with college students in a remedial reading course who received training either in summarization or in self-questioning (in the self-questioning condition, students learned to write multiplechoice comprehension questions). Training lasted for 4 weeks; each week, students received approximately 4 to 5 hours of instruction and practice that involved applying the techniques to 1-page news articles. Of interest was the students’ performance on the Georgia State Regents’ examination, which involves answering multiple-choice reading-comprehension questions about passages; passing this exam is a graduation requirement for many college students in the University System of Georgia (see http://www2.gsu.edu/~wwwrtp/). Students also took a practice test before taking the actual Regents’ exam. Unfortunately, the mean scores for both groups were at or below passing, for both the practice and actual exams. However, the self-questioning group performed better than the summarization group on both the practice test and the actual Regents’ examination. This study did not report pretraining scores and did not include a no-training control group, so some caution is warranted in interpreting the results. However, it emphasizes the need to establish that outcomes from basic laboratory work generalize to actual educational contexts and suggests that summarization may not have the same influence in both contexts. Finally, concerning test delays, several studies have indicated that when summarization does boost performance, its effects are relatively robust over delays of days or weeks (e.g., Bretzing & Kulhavy, 1979; B. L. Stein & Kirby, 1992). Similarly, benefits of training programs have persisted several weeks after the end of training (e.g., Hare & Borchardt, 1984). 18 3.3 Effects in representative educational contexts. Several of the large summarization-training studies have been conducted in regular classrooms, indicating the feasibility of doing so. For example, the study by A. King (1992) took place in the context of a remedial study-skills course for undergraduates, and the study by Rinehart et al. (1986) took place in sixth-grade classrooms, with the instruction led by students’ regular teachers. In these and other cases, students benefited from the classroom training. We suspect it may actually be more feasible to conduct these kinds of training studies in classrooms than in the laboratory, given the nature of the time commitment for students. Even some of the studies that did not involve training were conducted outside the laboratory; for example, in the Bednall and Kehoe (2011) study on learning about logical fallacies from Web modules (see data in Table 3), the modules were actually completed as a homework assignment. Overall, benefits can be observed in classroom settings; the real constraint is whether students have the skill to successfully summarize, not whether summarization occurs in the lab or the classroom. 3.4 Issues for implementation. Summarization would be feasible for undergraduates or other learners who already know how to summarize. For these students, summarization would constitute an easy-to-implement technique that would not take a lot of time to complete or understand. The only concern would be whether these students might be better served by some other strategy, but certainly summarization would be better than the study strategies students typically favor, such as highlighting and rereading (as we discuss in the sections on those strategies below). A trickier issue would concern implementing the strategy with students who are not skilled summarizers. Relatively intensive training programs are required for middle school students or learners with learning disabilities to benefit from summarization. Such efforts are not misplaced; training has been shown to benefit performance on a range of measures, although the training procedures do raise practical issues (e.g., Gajria & Salvia, 1992: 6.5–11 hours of training used for sixth through ninth graders with learning disabilities; Malone & Mastropieri, 1991: 2 days of training used for middle school students with learning disabilities; Rinehart et al., 1986: 45–50 minutes of instruction per day for 5 days used for sixth graders). Of course, instructors may want students to summarize material because summarization itself is a goal, not because they plan to use summarization as a study technique, and that goal may merit the efforts of training. However, if the goal is to use summarization as a study technique, our question is whether training students would be worth the amount of time it would take, both in terms of the time required on the part of the instructor and in terms of the time taken away from students’ other activities. For instance, in terms of efficacy, summarization tends to fall in the middle of the pack when compared to other techniques. In direct Dunlosky et al. comparisons, it was sometimes more useful than rereading (Rewey, Dansereau, & Peel, 1991) and was as useful as notetaking (e.g., Bretzing & Kulhavy, 1979) but was less powerful than generating explanations (e.g., Bednall & Kehoe, 2011) or self-questioning (A. King, 1992). 3.5 Summarization: Overall assessment. On the basis of the available evidence, we rate summarization as low utility. It can be an effective learning strategy for learners who are already skilled at summarizing; however, many learners (including children, high school students, and even some undergraduates) will require extensive training, which makes this strategy less feasible. Our enthusiasm is further dampened by mixed findings regarding which tasks summarization actually helps. Although summarization has been examined with a wide range of text materials, many researchers have pointed to factors of these texts that seem likely to moderate the effects of summarization (e.g., length), and future research should be aimed at investigating such factors. Finally, although many studies have examined summarization training in the classroom, what are lacking are classroom studies examining the effectiveness of summarization as a technique that boosts students’ learning, comprehension, and retention of course content. 4 Highlighting and underlining Any educator who has examined students’ course materials is familiar with the sight of a marked-up, multicolored textbook. More systematic evaluations of actual textbooks and other student materials have supported the claim that highlighting and underlining are common behaviors (e.g., Bell & Limber, 2010; Lonka, Lindblom-Ylänne, & Maury, 1994; Nist & Kirby, 1989). When students themselves are asked about what they do when studying, they commonly report underlining, highlighting, or otherwise marking material as they try to learn it (e.g., Cioffi, 1986; Gurung, Weidert, & Jeske, 2010). We treat these techniques as equivalent, given that, conceptually, they should work the same way (and at least one study found no differences between them; Fowler & Barker, 1974, Experiment 2). The techniques typically appeal to students because they are simple to use, do not entail training, and do not require students to invest much time beyond what is already required for reading the material. The question we ask here is, will a technique that is so easy to use actually help students learn? To understand any benefits specific to highlighting and underlining (for brevity, henceforth referred to as highlighting), we do not consider studies in which active marking of text was paired with other common techniques, such as note-taking (e.g., Arnold, 1942; L. B. Brown & Smiley, 1978; Mathews, 1938). Although many students report combining multiple techniques (e.g., L. Annis & Davis, 1978; Wade, Trathen, & Schraw, 1990), each technique must be evaluated independently to discover which ones are crucial for success. Improving Student Achievement 4.1 General description of highlighting and underlining and why they should work. As an introduction to the relevant issues, we begin with a description of a prototypical experiment. Fowler and Barker (1974, Exp. 1) had undergraduates read articles (totaling about 8,000 words) about boredom and city life from Scientific American and Science. Students were assigned to one of three groups: a control group, in which they only read the articles; an active-highlighting group, in which they were free to highlight as much of the texts as they wanted; or a passive-highlighting group, in which they read marked texts that had been highlighted by yoked participants in the active-highlighting group. Everyone received 1 hour to study the texts (time on task was equated across groups); students in the active-highlighting condition were told to mark particularly important material. All subjects returned to the lab 1 week later and were allowed to review their original materials for 10 minutes before taking a 54-item multiple-choice test. Overall, the highlighting groups did not outperform the control group on the final test, a result that has unfortunately been echoed in much of the literature (e.g., Hoon, 1974; Idstein & Jenkins, 1972; Stordahl & Christensen, 1956). However, results from more detailed analyses of performance in the two highlighting groups are informative about what effects highlighting might have on cognitive processing. First, within the active-highlighting group, performance was better on test items for which the relevant text had been highlighted (see Blanchard & Mikkelson, 1987; L. L. Johnson, 1988 for similar results). Second, this benefit to highlighted information was greater for the active highlighters (who selected what to highlight) than for passive highlighters (who saw the same information highlighted, but did not select it). Third, this benefit to highlighted information was accompanied by a small cost on test questions probing information that had not been highlighted. To explain such findings, researchers often point to a basic cognitive phenomenon known as the isolation effect, whereby a semantically or phonologically unique item in a list is much better remembered than its less distinctive counterparts (see Hunt, 1995, for a description of this work). For instance, if students are studying a list of categorically related words (e.g., “desk,” “bed,” “chair,” “table”) and a word from a different category (e.g., “cow”) is presented, the students will later be more likely to recall it than they would if it had been studied in a list of categorically related words (e.g., “goat,” “pig,” “horse,” “chicken”). The analogy to highlighting is that a highlighted, underlined, or capitalized sentence will “pop out” of the text in the same way that the word “cow” would if it were isolated in a list of words for types of furniture. Consistent with this expectation, a number of studies have shown that reading marked text promotes later memory for the marked material: Students are more likely to remember things that the experimenter highlighted or underlined in the text (e.g., Cashen & Leicht, 1970; Crouse & Idstein, 1972; Hartley, Bartlett, & Branthwaite, 1980; Klare, Mabry, & Gustafson, 1955; see Lorch, 1989 for a review). 19 Actively selecting information should benefit memory more than simply reading marked text (given that the former would capitalize on the benefits of generation, Slamecka & Graf, 1978, and active processing more generally, Faw & Waller, 1976). Marked text draws the reader’s attention, but additional processing should be required if the reader has to decide which material is most important. Such decisions require the reader to think about the meaning of the text and how its different pieces relate to one another (i.e., organizational processing; Hunt & Worthen, 2006). In the Fowler and Barker (1974) experiment, this benefit was reflected in the greater advantage for highlighted information among active highlighters than among passive recipients of the same highlighted text. However, active highlighting is not always better than receiving material that has already been highlighted by an experimenter (e.g., Nist & Hogrebe, 1987), probably because experimenters will usually be better than students at highlighting the most important parts of a text. More generally, the quality of the highlighting is likely crucial to whether it helps students to learn (e.g., Wollen, Cone, Britcher, & Mindemann, 1985), but unfortunately, many studies have not contained any measure of the amount or the appropriateness of students’ highlighting. Those studies that have examined the amount of marked text have found great variability in what students actually mark, with some students marking almost nothing and others marking almost everything (e.g., Idstein & Jenkins, 1972). Some intriguing data came from the active-highlighting group in Fowler and Barker (1974). Test performance was negatively correlated (r = –.29) with the amount of text that had been highlighted in the activehighlighting group, although this result was not significant given the small sample size (n = 19). Marking too much text is likely to have multiple consequences. First, overmarking reduces the degree to which marked text is distinguished from other text, and people are less likely to remember marked text if it is not distinctive (Lorch, Lorch, & Klusewitz, 1995). Second, it likely takes less processing to mark a lot of text than to single out the most important details. Consistent with this latter idea, benefits of marking text may be more likely to be observed when experimenters impose explicit limits on the amount of text students are allowed to mark. For example, Rickards and August (1975) found that students limited to underlining a single sentence per paragraph later recalled more of a science text than did a nounderlining control group. Similarly, L. L. Johnson (1988) found that marking one sentence per paragraph helped college students in a reading class to remember the underlined information, although it did not translate into an overall benefit. 4.2 How general are the effects of highlighting and underlining? We have outlined hypothetical mechanisms by which highlighting might aid memory, and particular features of highlighting that would be necessary for these mechanisms to be effective (e.g., highlighting only important material). However, most studies have shown no benefit of highlighting (as it 20 is typically used) over and above the benefit of simply reading, and thus the question concerning the generality of the benefits of highlighting is largely moot. Because the research on highlighting has not been particularly encouraging, few investigations have systematically evaluated the factors that might moderate the effectiveness of the technique—for instance, we could not include a Learning Conditions (4.2a) subsection below, given the lack of relevant evidence. To the extent the literature permits, we sketch out the conditions known to moderate the effectiveness of highlighting. We also describe how our conclusion about the relative ineffectiveness of this technique holds across a wide range of situations. 4.2b Student characteristics. Highlighting has failed to help Air Force basic trainees (Stordahl & Christensen, 1956), children (e.g., Rickards & Denner, 1979), and remedial students (i.e., students who scored an average of 390 on the SAT verbal section; Nist & Hogrebe, 1987), as well as prototypical undergraduates (e.g., Todd & Kessler, 1971). It is possible that these groups struggled to highlight only relevant text, given that other studies have suggested that most undergraduates overmark text. Results from one study with airmen suggested that prior knowledge might moderate the effectiveness of highlighting. In particular, the airmen read a passage on aircraft engines that either was unmarked (control condition) or had key information underlined (Klare et al., 1955). The experimenters had access to participants’ previously measured mechanical-aptitude scores and linked performance in the experiment to those scores. The marked text was more helpful to airmen who had received high scores. This study involved premarked texts and did not examine what participants would have underlined on their own, but it seems likely that students with little knowledge of a topic would struggle to identify which parts of a text were more or less important (and thus would benefit less from active highlighting than knowledgeable students would). One other interesting possibility has come from a study in which experimenters extrinsically motivated participants by promising them that the top scorers on an exam would receive $5 (Fass & Schumacher, 1978). Participants read a text about enzymes; half the participants were told to underline key words and phrases. All participants then took a 15-item multiple-choice test. A benefit from underlining was observed among students who could earn the $5 bonus, but not among students in a control group. Thus, although results from this single study need to be replicated, it does appear that some students may have the ability to highlight effectively, but do not always do so. 4.2c Materials. Similar conclusions about marking text have come from studies using a variety of different text materials on topics as diverse as aerodynamics, ancient Greek schools, aggression, and Tanzania, ranging in length from a few hundred words to a few thousand. Todd and Kessler (1971) manipulated text length (all of the materials were relatively short, with lengths of 44, 140, or 256 words) and found that underlining was ineffective regardless of the text length. Fass Dunlosky et al. and Schumacher (1978) manipulated whether a text about enzymes was easy or difficult to read; the easy version was at a seventh-grade reading level, whereas the difficult version was at high school level and contained longer sentences. A larger difference between the highlighting and control groups was found for performance on multiple-choice tests for the difficult text as opposed to the easy text. 4.2d Criterion tasks. A lack of benefit from highlighting has been observed on both immediate and delayed tests, with delays ranging from 1 week to 1 month. A variety of dependent measures have been examined, including free recall, factual multiple-choice questions, comprehension multiple-choice questions, and sentence-completion tests. Perhaps most concerning are results from a study that suggested that underlining can be detrimental to later ability to make inferences. Peterson (1992) had education majors read a 10,000-word chapter from a history textbook; two groups underlined while studying for 90 minutes, whereas a third group was allowed only to read the chapter. One week later, all groups were permitted to review the material for 15 minutes prior to taking a test on it (the two underlining groups differed in whether they reviewed a clean copy of the original text or one containing their underlining). Everyone received the same test again 2 months later, without having another chance to review the text. The multiple-choice test consisted of 20 items that probed facts (and could be linked to specific references in the text) and 20 items that required inferences (which would have to be based on connections across the text and could not be linked to specific, underlined information). The three groups performed similarly on the factual questions, but students who had underlined (and reviewed their marked texts) were at a disadvantage on the inference questions. This pattern of results requires replication and extension, but one possible explanation for it is that standard underlining draws attention more to individual concepts (supporting memory for facts) than to connections across concepts (as required by the inference questions). Consistent with this idea, in another study, underliners who expected that a final test would be in a multiple-choice format scored higher on it than did underliners who expected it to be in a shortanswer format (Kulhavy, Dyer, & Silver, 1975), regardless of the actual format of the final-test questions. Underlined information may naturally line up with the kinds of information students expect on multiple-choice tests (e.g., S. R. Schmidt, 1988), but students may be less sure about what to underline when studying for a short-answer test. 4.5 Effects in representative educational contexts. As alluded to at the beginning of this section, surveys of actual textbooks and other student materials have supported the frequency of highlighting and underlining in educational contexts (e.g., Bell & Limber, 2010; Lonka et al., 1994). Less clear are the consequences of such real-world behaviors. Classroom studies have examined whether instructor-provided markings affect examination performance. For example, 21 Improving Student Achievement Cashen and Leicht (1970) had psychology students read Scientific American articles on animal learning, suicide, and group conflict, each of which contained five critical statements, which were underlined in red for half of the students. The articles were related to course content but were not covered in lectures. Exam scores on items related to the critical statements were higher when the statements had been underlined in red than when they had not. Interestingly, students

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