Generating explanations for oneself in an attempt to make sense of new information (i.e., self-explanation) is often a powerful learning technique. Despite its general effectiveness, in a growing number of studies, prompting for self-explanation improved some aspects of learning, but reduced learning of other aspects. Drawing on this recent research, as well as on research comparing self-explanation under different conditions, we propose four constraints on the effectiveness of self-explanation. First, self-explanation promotes attention to particular types of information, so it is better suited to promote particular learning outcomes in particular types of domains, such as transfer in domains guided by general principles or heuristics. Second, self-explaining a variety of types of information can improve learning, but explaining one’s own solution methods or choices may reduce learning under certain conditions. Third, explanation prompts focus effort on particular aspects of the to-be-learned material, potentially drawing effort away from other important information. Explanation prompts must be carefully designed to align with target learning outcomes. Fourth, prompted self-explanation often promotes learning better than unguided studying, but alternative instructional techniques may be more effective under some conditions. Attention to these constraints should optimize the effectiveness of self-explanation as an instructional technique in future research and practice.
We often try to explain the world around us. We draw on our observations, prior knowledge, and ideas from others to generate potential explanations. In turn, efforts to explain are predictive of learning (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Renkl, 1999). Further, prompting people to explain new information often leads them to learn more than people who are not prompted to explain across a variety of topics and age groups (e.g., Chi, de Leeuw, Chiu, & LaVancher, 1994; Rittle-Johnson, 2006). Thus, generating explanations for oneself in an attempt to make sense of new information, often called self-explanation, is a powerful learning technique. Indeed, generating explanations is a recommended study strategy (Dunlosky, Rawson, Marsh, Nathan, & Willingham, 2013; Pashler et al., 2007) and a recommended instructional practice (Common Core State Standards, 2010).
Despite its general effectiveness, a growing number of studies have reported that prompting for self-explanation improved some aspects of learning, but reduced other aspects of learning relative to learning without prompting for self-explanation (e.g., Berthold, Röder, Knörzer, Kessler, & Renkl, 2011; Kuhn & Katz, 2009; Williams, Lombrozo, & Rehder, 2013). The goal of this paper is to better understand constraints on the effectiveness of self-explanation. This will help identify ways to increase the effective use of prompted self-explanation in future research and practice.
Self-explanation is defined as generating explanations for oneself in an attempt to make sense of new information (Chi et al., 1994; Rittle-Johnson, 2006). The explanations are inferences by the learner that go beyond the given information; the inferences may be focused on the reasoning of experts presented in worked-out examples or text, or about one’s own problem solving efforts. Self-explanations are generated by the learner, rather than by an instructor, parent or other person who already knows the content, and are generated for the learner, not intended to teach the content to other people (Chi et al., 1994).
Self-explanation can clearly be an effective learning technique. Past reviews of the self-explanation literature have focused on particular contexts, and have documented the generally positive effects of prompting for self-explanation in that context. For example, Dunlosky and colleagues (Dunlosky et al., 2013) were interested in learning techniques that students could implement on their own, without specially designed materials, so reviewed only studies that used general, content-free prompts to promote self-explanation. They concluded that self-explanation in this context has moderate utility as a learning technique. Other reviews have focused on the benefits of self-explanation in areas such as scientific reasoning (Legare, 2014), or using materials such as multimedia learning materials (Wylie & Chi, 2014) or worked-examples (Atkinson, Derry, Renkl, & Wortham 2000). Further, prompted self-explanation can promote learning for learners with a range of prior knowledge levels, although it can be particularly effective for students with limited prior knowledge (McNamara, 2004; Renkl, Stark, Gruber, & Mandl, 1998).
Self-explanation is thought to promote learning via two primary processes. First, self-explanation aids comprehension by promoting knowledge integration (Chi, 2000). In particular, explanations often integrate pieces of new information together or integrate new information with prior knowledge (Lombrozo, 2006). For example, when studying text with worked examples, learners’ explanations often linked solution steps to prior knowledge or information in the text (Atkinson, Renkl, & Merrill, 2003; Chi et al., 1989; Renkl, 1997). Further, when new information conflicts with prior knowledge, learners have multiple opportunities to notice this conflict and attempt to resolve it (Chi, 2000). For example, explanations sometimes include integration of critical features that were originally overlooked or misinterpreted (Durkin & Rittle-Johnson, 2012).
Second, self-explanation aids comprehension and transfer by guiding attention to structural features over surface features of the to-be-learned content, increasing generalization (Lombrozo, 2006; Rittle-Johnson, 2006). This makes knowledge more generalizable because it is less tied to particular problem features, so it is more likely to be transferred to new problems and situations. For example, generating explanations can make learners more attentive to general characteristics of the solution method that can be used to solve problems that are structurally similar (McEldoon, Durkin, & Rittle-Johnson, 2013; Rittle-Johnson, 2006; Siegler & Chen, 2008). Overall, self-explanation supports knowledge integration and knowledge generalization, which improves future performance. Thus, it is the cognitive processes encouraged by engaging in self-explanation that facilitate learning.
Because past reviews have focused on the positive effects of self-explanation, we focused on integrating the growing number of studies that have reported mixed, negative or neutral effects of prompted self-explanation relative to learning without prompted self-explanation. We also attended more carefully to past research that has contrasted the effectiveness of different versions of self-explanation. We identified published articles in which self-explanation was experimentally manipulated by searching PsychInfo and ERIC, by checking citations in central articles on self-explanation, and by browsing psychology journals. Given our focus on experimental research, self-explanation was defined by condition (i.e., receiving explanation prompts or not), rather than the quality of the responses that learners were able to generate. We confirmed that the prompts in each study encouraged explanation (i.e., making inferences that went beyond the given information). Note that learners in the control condition may have self-explained spontaneously, so these studies primarily address the effects of prompted self-explanation, not the effects of spontaneous self-explanation.
We used this research to propose constraints on the effectiveness of self-explanation. These constraints will facilitate a priori predictions of whether prompted self-explanation will improve learning in a particular context and help optimize the effective use of prompted self-explanation in future research and practice.
Constraints on when prompting for explanation aids learning
The impact of self-explanation on learning is constrained by the content of learners’ explanations. Learners sometimes struggle to provide relevant explanations, and, in these cases, self-explanation often does not improve learning (e.g., Broers & Imbos, 2005; DeCaro & Rittle-Johnson, 2012; Matthews & Rittle-Johnson, 2009; Mwangi & Sweller, 1998). In other cases, learners’ explanations focus their attention on particular types of information, decreasing their attention to, and learning of, other information (e.g., Berthold et al., 2011; Kuhn & Katz, 2009; Williams et al., 2013). Such null and mixed results highlight the need to specify constraints on the effectiveness of self-explanation. We propose four constraints, outlined in Table 1, on self-explanation as an instructional technique.
Constraint on target outcomes and domains
Self-explanation promotes attention to particular types of information, so it is better suited to promoting particular learning outcomes in particular types of domains. Explanation seems to encourage learners to look for general patterns that are not tied to particular instances (Lombrozo, 2006). This has consequences for learning, improving learning of some information while reducing learning of other information. First, self-explanation can improve transfer of information to a new task, but reduce recall of details. In one study, preschool children were prompted to self-explain on a causal reasoning task or observed the task without prompting (Legare & Lombrozo, 2014). Children in the self-explanation condition had poorer recall of details, such as the color of the objects, but better transfer of the causal mechanism to a new task. Similarly, adults were prompted to self-explain on a category-learning task or were prompted to describe the items in the task (Williams & Lombrozo, 2010). Adults in the self-explanation condition had poorer recall of details of study items, but better transfer of the categories to new items. Explanation seems to encourage learners to look for general patterns that are not tied to particular instances, supporting transfer of the patterns to new instances; however, this reduces attention to details (Legare, 2014).
Although generalization and transfer is usually desirable, reduced attention to, and recall of, details can have negative consequences for learning, such as overlooking exceptions to rules or confusing similar ideas. For example, prompting college students to explain why examples were members of a particular category caused them to overlook exceptions to rules that were probabilistic, rather than deterministic (Williams et al., 2013). People who were prompted to self-explain, rather than think aloud or prepare to explain later, had more difficulty learning to correctly categorize examples that did not follow the general category rule, were more likely to ignore exceptions to the rule in their descriptions of the categories, and were more likely to apply the rule uniformly to new instances, even when the instances had features that conflicted with the general rule. This was true in two experiments, one on learning categories of artifacts and one on learning about characteristics of people associated with a particular behavior.
These findings suggest that self-explanation may not be well suited to promote learning in domains with important exceptions to underlying rules or principles. Support for this suggestion comes from research on learning English grammar, a domain with many exceptions to general rules. Adult second-language learners did not gain better English grammar when prompted to self-explain correct examples of grammar rules rather than study the examples without explanation, even though self-explaining substantially increased time on task (Wylie, Koedinger, & Mitamura 2010; Wylie, Sheng, Mitamura, & Koedinger, 2011). Learners who self-explained were better able to state the general grammar rule, but they were not better able to apply it appropriately. We would predict similar results for learning similarity-based categories, where group members tend to share similar characteristics, but no characteristic is required for category membership, so there are not rules that define category membership (e.g., animal species).
Rather, self-explanation seems particularly effective at promoting comprehension and transfer in domains that are consistently guided by general principles or heuristics. In line with this suggestion, a majority of research on self-explanation has focused on learning in math and science domains guided by general principles or heuristics. For example, prompts to self-explain have helped preschool children learn about mimicry (Brown & Kane, 1988), elementary-school children learn about addition and mathematical equivalence (Calin-Jageman & Ratner, 2005; McEldoon et al., 2013; Rittle-Johnson, 2006), a range of students learn about biology concepts (Chi et al., 1994; de Koning, Tabbers, Rikers, & Pass, 2011; McNamara, 2004), high-school students learn geometry, algebra and probability (Aleven & Koedinger, 2002; Atkinson et al., 2003; Kramarski & Dudai, 2009) and undergraduate students learn scientific argumentation, mathematical proofing, computer programming, and chemistry knowledge (Bielaczyc, Pirolli, & Brown, 1995; Chou & Liang, 2009; Hilbert, Renkl, Kessler, & Reiss, 2008; Schworm & Renkl, 2007; Yeh, Chen, Hung, & Hwang, 2010). Note that some of these topics are guided by heuristics rather than principles that always support optimal solutions (e.g., scientific argumentation and mathematical proofing). Two unpublished meta-analyses of the self-explanation literature focused on math and science topics confirmed a moderate effect of prompted self-explanation, relative to no prompts to explain, on transfer performance (Durkin, 2011; Mugford, Corey, Bennell, & Martens, 2009). Certainly, domains outside of math and science are guided by general principles or heuristics, and prompted self-explanation can improve transfer for other topics, such as theory of mind (Amsterlaw & Wellman, 2006; Tenenbaum, Alfieri, Brooks, & Dunne, 2008), designing worked example (Schworm & Renkl, 2006), and aviation safety rules (Molesworth, Bennett, & Kehoe, 2011). What may be critical is that the domain be guided by general principles or heuristics, with few exceptions, so that generalization and transfer of those ideas is appropriate. Because self-explanation prompts seem to encourage learners to ignore details of individual items and exceptions to general rules, self-explanation seems less appropriate in domains not consistently guided by general rules, principles or heuristics (e.g., English grammar and similarity-based categories, such as animal species). Overall, self-explanation is selective in the type of learning outcomes it promotes, so it is most effective in improving learning in domains aligned with those types of outcomes.
Constraint on what is being explained
The nature of the information that is being explained also matters. Self-explaining a variety of types of information can improve learning, including explaining worked-out examples of solutions to problems, text, demonstrations of causal relations and one’s own problem-solving efforts. However, a recent study suggests that explaining ones own solution methods or choices may reduce learning under certain conditions. In particular, self-explanation of ones’ own ideas can focus learners’ attention on their preexisting idea, reducing learning and transfer of new information (Kuhn & Katz, 2009). When predicting outcomes on a scientific reasoning task, middle-school students were prompted to self-explain why they thought each variable in an experiment would or would not have a causal impact on the predicted outcome or completed the prediction task without explaining. Prompting children to explain their own predictions, which were often incorrect, reduced their subsequent success at making evidence-based claims relative to a no-explanation condition. Self-explanation prompts seemed to focus students’ attention on their preexisting theories, which were often incorrect, and may have reduced attention to new information and evidence that contradicted their theories. Explaining information brings added attention to that information; this can have negative consequences when the information is incorrect but the learner does not recognize its inaccuracy.
Other research indicates that self-explanation of ones’ own ways of thinking does not promote learning and transfer as well as self-explanation of correct information (Calin-Jageman & Ratner, 2005; Siegler, 1995, 2002; Siegler & Chen, 2008). For example, 5-year-old children were (1) prompted to explain correct solutions after first attempting to solve each problem; (2) prompted to explain their own solution prior to feedback on solution accuracy; or (3) solved the problems with feedback, but without prompts to explain (Siegler, 1995). Children who explained correct solutions solved substantially more transfer problems correctly than children who explained their own solutions, who did not differ from children who were not prompted to explain. Children’s own solutions were often incorrect, so children in the explain-own condition spent time justifying and making inferences about information that was not correct.
Under some conditions, prompted self-explanation of ones own ideas can promote learning relative to no prompting, such as when learners are likely to solve the to-be-explained problems correctly (e.g., Berry, 1983; Gagné & Smith, 1962) or are provided accuracy feedback before being prompted to self-explain (Johnson & Mayer, 2010; Mayer & Johnson, 2010). For example, in Gagné and Smith (1962), high-school boys were either asked to provide a reason for each move they made when solving relatively easy problems, or to solve the problems without providing explanations. Learners usually solved the easy problems correctly. Those who had explained their solution methods had greater success on subsequent, more difficult problems. Thus, it seems important to promote self-explanation of ones own ideas in limited circumstances.
Additional research has contrasted learning from self-explaining correct information alone to self-explaining correct as well as why incorrect information is incorrect. In all of these studies, learners who were prompted to explain both correct and incorrect information gained better comprehension and transfer (Atkinson et al., 2000; Booth, Lange, Koedinger, & Newton, 2013; Durkin & Rittle-Johnson, 2012; Gadgil, Nokes-Malach, & Chi, 2012; Große & Renkl, 2007; Siegler, 2002; Siegler & Chen, 2008; Yeh et al., 2010). Other research indicates that prompting learners to explain why incorrect exemplars were incorrect led to greater transfer than prompting learners to explain why correct exemplars were correct (Ball, Hoyle, & Towse, 2010). As an example, in Siegler & Chen (2008), children made predictions and observed outcomes on a water displacement task. Some were prompted to explain the correct outcome and some were prompted to explain both the correct outcome and why a common incorrect prediction for the outcome was incorrect. Prompts to explain both correct and incorrect exemplars led to the best transfer. In two studies, sufficient prior knowledge was needed to learn more from explaining both correct and incorrect information than only explaining correct information (Große & Renkl, 2007; Yeh et al., 2010). In other research, prompts to explain both why correct information was correct and why incorrect information was incorrect improved comprehension and transfer relative to no explanation prompts (de Bruin, Rikers, & Schmidt, 2007; Howie & Vicente, 1998; Huk & Ludwigs, 2009; McEldoon et al., 2013; Pillow, Mash, Aloian, & Hill, 2002; Rittle-Johnson, 2006). Including incorrect examples may surprise learners and can spark greater attempts to explain compared to correct examples alone (Legare, Gelman, & Wellman, 2010). Contrasting correct and incorrect examples can also help learners distinguish correct and incorrect ideas by supporting inferences about their differences (Durkin & Rittle-Johnson, 2012) and reducing selection of incorrect ideas in the future (Siegler, 2002).
Overall, self-explanation is often more effective when learners are explaining content that they know is correct or incorrect rather than their own ideas. This supports integration and generalization of accurate information. Self-explanation of ones own ideas can be effective under some circumstances. However, self-explaining ones’ own ideas can focus learners’ attention on their preexisting idea; if those ideas are frequently incorrect, this can fail to improve learning or even reduce learning and transfer of new information. To optimize the effectiveness of self-explanation, we recommend explaining correct information rather than ones own, potentially incorrect, ideas. Explaining why incorrect information is incorrect can also improve learning.
Constraint on explanation prompts
Learners are rarely prompted to “explain,” with no elaboration on what to explain. Recent research highlights how explanation prompts influence what is learned. Explanation prompts that focused attention on key concepts increased comprehension of domain principles, but also reduced success on transfer problems (Berthold & Renkl, 2009; Berthold et al., 2011). In one study, high school students studied worked-out examples for probability problems with prompts to self-explain or to take notes (Berthold & Renkl, 2009). In the other, college students studied an e-learning module on tax law with prompts to self-explain or to take notes (Berthold et al., 2011). Self-explanation prompts focused on linking steps and ideas to underlying principles (why prompts, such as “Why do you calculate the total possible outcome by multiplying?”). The why-explanation prompts supported more detailed explanations than unguided note taking, including a greater number of elaborations on domain principles. However, the prompts also decreased the number of calculations performed during learning (Berthold et al., 2011). In contrast, prompts that focused attention on solution procedures (e.g., “Where do you see commonalities and differences between the two solution methods?”) reduced comprehension relative to studying the examples without generating explanations (Große & Renkl, 2006). These results highlight the importance of the type of explanation prompt, and suggest that particular types of prompts may guide attention to one type of knowledge at the expense of attention to other types of knowledge.
These findings highlight how explanation prompts focus attention on particular aspects of the to-be-learned material, impacting the content of the explanations. There can be hidden costs, drawing attention away from other important information. We hypothesize that why-explanation prompts should promote inferences about causes behind outcomes, expanding comprehension of domain concepts (i.e., conceptual knowledge) because they focus attention on identifying relevant and underlying principles and regularities (Williams & Lombrozo, 2013). How-explanation prompts should promote generation, adoption and transfer of rules and procedures (i.e., procedural transfer) because they focus attention on generating rules and procedures and on figuring out when they are applicable (Rittle-Johnson, 2006).
We identified only one study that directly contrasted different types of explanation prompts. High-school students were prompted to explain worked-out examples to physics problems (Nokes, Hausmann, VanLehn, & Gershman, 2011). How-explanation-prompts focused learners on generating inferences for missing information in the example steps (e.g., “Could you restate or summarize that step in your own words?”). Why-explanation-prompts focused learners on revising their prior knowledge based on the new information (e.g., “What new information does each step provide for you?”). The how-explanation prompts led to greater procedural transfer than the why-explanation prompts (conceptual knowledge was not assessed). The authors argued that their how-explanation-prompts were better matched to the target learning content because the reasoning behind solution steps was not obvious and because misconceptions were not prevalent.
Explanation prompts direct attention to particular information, so explanation prompts can reduce attention to other important information. Thus, explanation prompts must be designed with care so that efforts to direct attention towards one type of information does not come at the expense of learning less about another important type of information.
Constraint on effectiveness relative to alternative instructional techniques
Finally, the substantial time demands of self-explanation raises the question of when alternative activities would more easily or effectively achieve the same learning outcomes. In a majority of past research, the self-explanation condition was compared to a condition that spent considerably less time on task. For example, in a seminal study by Chi et al. (1994), learners in the self-explanation condition spent almost twice as long reading the text as learners in the control condition (2 h, 5 min vs. 1 h 6 min on average), even though learners in the control condition re-read the text to attempt to control for time on task. However, prompting for self-explanation does not improve learning outcomes simply by increasing time on task. In studies that have controlled for time on task, self-explanation prompts continued to improve comprehension and transfer (e.g., Atkinson et al., 2003; Bielaczyc et al., 1995; de Bruin et al., 2007; de Koning et al., 2011). When time on task is controlled, learners in the control group are most often asked to use their own study techniques for a fixed amount of time. Thus, prompted self-explanation promotes comprehension and transfer better than unguided studying for the same amount of time.
Some studies have compared prompted self-explanation to other specific instructional techniques. Self-explanation has most often been compared to receiving instructional explanations, which are explanations provided by an expert. In a meta-analysis of six studies that compared learning from worked examples in conjunction with self-explanation prompts vs. instructional explanations, there was no effect of condition (Wittwer & Renkl, 2010). This reflected mixed findings across studies, some of which favored instructional explanations, others that favored self-explanations, and others that found no difference. In one study, college students studied worked examples from probability theory that included instructional explanations; some students were prompted to self-explain as well. Students who were prompted to self-explain had lower transfer performance compared to students who were not prompted to explain (Gerjets, Sheiter, & Catrambone, 2006). The authors argued that self-explanation was redundant with processing the instructional explanations. Comparable learning from self-explanation prompts and instructional explanations has been reported for a variety of other learning contexts, ranging from preschoolers learning about identifying rules in repeating patterns of objects, to adults learning from animations of the human circulatory system (Cho & Jonassen, 2012; Crowley & Siegler, 1999; De Koning, Tabbers, Rikers, & Paas, 2010; Rittle-Johnson, Fyfe, Loehr, & Miller, 2015; Tenenbaum et al., 2008).
Why might the evidence comparing prompted self-explanation to instructional explanations be so mixed? The quality of the self-explanations is one key factor. In studies that have found an advantage for prompted self-explanations over instructional explanations, the learners were fairly successful in generating reasonable self-explanations (e.g., Brown & Kane, 1988; Schworm & Renkl, 2006). For example, in Brown and Kane (1988), only 2 of 21 children in the self-explanation condition could not generate a reasonable explanation, and both these children failed to transfer. In contrast, in studies that have not found an advantage, learners in the self-explanation condition had difficulty generating correct inferences and generalizations (Cho & Jonassen, 2012; Crowley & Siegler, 1999; De Koning et al., 2010; Rittle-Johnson et al., 2015). For example, high-school students in the self-explanation condition often provided one-sided explanations, erroneous explanations, and/or failed to make inferences when studying biology diagrams (Cho & Jonassen, 2012). Self-explanation may be more effective than instructional explanations if learners are able to generate reasonable-quality explanations, but doing so can be a considerable challenge. Self-explanation quality can be improved by providing training or structuring self-explanation responses (e.g., Berthold, Eysink, & Renkl, 2009; Bielaczyc et al., 1995; see Rittle-Johnson & Loehr, 2016 for a review). Nevertheless, generating self-explanation can take considerably more time than receiving instructional explanations (Schworm & Renkl, 2006). Future research should evaluate whether prompted self-explanation is more consistently beneficial than instructional explanations when self-explanation responses are structured and when time on task is comparable, as well as effective ways to integrate instructional- and self-explanations.
Another alternative to prompted self-explanation is solving additional non-routine problems, which can also promote comprehension and transfer (Canobi, 2009). Again, findings are mixed. At least one study found greater comprehension and transfer for the self-explanation condition (Aleven & Koedinger, 2002), another found positive effects for only some learning outcomes (McEldoon et al., 2013), and still others found comparable transfer and comprehension when students in the control condition spent an equivalent time solving more problems (DeCaro & Rittle-Johnson, 2012; Matthews & Rittle-Johnson, 2009). Both self-explanation prompts and solving unfamiliar problems can provide opportunities for thinking about correct procedures, including when each is most appropriate, and noticing patterns across problems. This is especially true when problem-solving exercises are designed with problems sequenced to support noticing of underlying concepts (Canobi, 2009; McNeil et al., 2012).
Finally, recent research explored the effectiveness of repeated-testing (i.e., retrieval practice) and prompted self-explanation in isolation and in combination for promoting retention of knowledge over a 6-month delay (Larsen, Butler, & Roediger, 2013). Both repeated-testing and prompted self-explanation supported medical-school students’ comprehension 6-months later better than unsupported studying. However, the effect of repeated-testing was much stronger. Students who engaged in both self-explanation and repeated testing had a bit higher retention than students who engaged in repeated testing without self-explanation, although the difference was not reliable. As reviewed above, self-explanation tends to promote comprehension and transfer in the near-term; this finding highlights that additional instructional techniques are useful to promote retention of this knowledge over a long delay.
Self-explanation is a powerful learning and instructional technique that supports comprehension and transfer. It has been shown to improve learning in a broad range of domains for a variety of age groups. However, we propose four constraints on the effectiveness of self-explanation as an instructional technique. First, self-explanation can reduce recall of details and promote overgeneralization, limiting its effectiveness in domains with important exceptions to general rules or principles (e.g., English grammar rules). Rather, it is more likely to promote comprehension and transfer in domains with general principles or heuristics, with few exceptions, as is true in many math and science domains. Second, self-explanation of ones own solution methods or choices may have a neutral or negative effect on learning if those methods and choices are likely to be incorrect. Rather, self-explanation of correct information is more likely to promote learning, and self-explanation of why incorrect ideas are incorrect can also promote learning. Third, explanation prompts direct attention to particular information, so explanation prompts can reduce attention to other important information. Thus, explanation prompts should be carefully designed to align with target learning outcomes. Finally, prompted self-explanation often promotes learning better than unguided studying, but alternative instructional techniques such as instructional explanations can sometimes be more effective.
Our proposed constraints on the effectiveness of self-explanation are in line with the cognitive processes self-explanation is thought to support: knowledge integration and knowledge generalization (Chi, 2000; Rittle-Johnson, 2006; Siegler & Chen, 2008). Knowledge integration and generalization should support comprehension and transfer in domains with unifying principles or heuristics. Further, it is best to integrate and generalize correct ideas (and why incorrect ideas are incorrect), rather than potentially incorrect ideas. Prompts focus attention on particular knowledge to generalize or integrate. Finally, recalling this knowledge after a long delay is challenging, so additional instructional techniques such as repeated testing may be needed to support long-term knowledge retention.
Additional research is needed to better evaluate our proposed constraints, testing the robustness of the constraints across multiple studies, domains and age groups. For example, research is needed on the effectiveness of self-explanation in a wider variety of domains not governed by a small number of principles or heuristics, such as causes of other people’s behavior. Future research also needs to identify additional constraints on the effectiveness of prompted self-explanation as an instructional technique. For example, there may be additional constraints on when prompted self-explanation helps preschool children learn. Three-year-old children may need repeated experiences with self-explanation. Three-year-olds gained greater comprehension from self-explanation prompts over 12 sessions (Amsterlaw & Wellman, 2006), but not from prompts in a single session (Honomichl & Chen, 2006). Preschool children may also benefit more if they self-explain explicit material, such as worked-out examples. Five-year-olds had better transfer after self-explaining worked-out examples of solution methods demonstrated by the experimenter, but not after self-explaining correct answers alone, relative to a condition that was not prompted to explain (Calin-Jageman & Ratner, 2005). Worked-out examples provide more information, greatly reducing how much learners must infer about how to correctly solve a problem (Atkinson et al., 2000). Given the cognitive demands of generating relevant explanations, scaffolds that reduce these demands may be particularly important for preschool children to learn from self-explanation.
Finally, too little research has compared prompted self-explanation to other effective instructional techniques, such as imagery or concept mapping. The considerable time it takes to self-explain raises concerns over the viability of learners engaging in self-explanation on their own; it is not a widely reported study strategy (e.g., Gurung, Weidert, & Jeske, 2010; Hartwig & Dunlosky, 2012). Given the substantial time demands of self-explanation, an open question is when alternative activities would more easily or efficiently achieve the same learning outcomes. Indeed, practice testing can promote better comprehension over a long delay than self-explanation. Providing instructional explanations can sometimes be more efficient and as effective as prompted self-explanation. How self-explanation compares to other instructional techniques such as imagery or concept mapping is unknown.
In conclusion, generating explanations for oneself in an attempt to make sense of new information (i.e., self-explanation) can be a powerful learning technique. However, its effectiveness may be constrained to particular learning domains and outcomes, to-be-explained information, types of prompts, and by the alternative instructional technique to which it is compared. Self-explanation seems most effective at promoting comprehension and transfer in domains guided by unifying principles or heuristics, such as many math and science topics. Further, promoting explanations of correct information is most likely to improve learning, and explanation prompts must be carefully designed to align with target learning outcomes. Fourth, prompted self-explanation is more effective than unguided studying, but alternative effective instructional techniques should also be considered.
Aleven, V. A., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147–179. doi:10.1016/S0364-0213(02)00061-7
Amsterlaw, J., & Wellman, H. M. (2006). Theories of mind in transition: A microgenetic study of the development of false belief understanding. Journal of Cognition and Development, 7(2), 139–172.
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70(2), 181–214. doi:10.2307/1170661
Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95(4), 774–783. doi:10.1037/0022-06184.108.40.2064
Ball, L. J., Hoyle, A. M., & Towse, A. S. (2010). The facilitatory effect of negative feedback on the emergence of analogical reasoning abilities. British Journal of Developmental Psychology, 28(3), 583–602. doi:10.1348/026151009x461744
Berry, D. C. (1983). Metacognitive experience and transfer of logical reasoning. The Quarterly Journal of Experimental Psychology Section A, 35(1), 39–49. doi:10.1080/14640748308402115
Berthold, K., Eysink, T. H. S., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37(4), 345–363.
Berthold, K., & Renkl, A. (2009). Instructional aids to support a conceptual understanding of multiple representations. Journal of Educational Psychology, 101(1), 70–87. doi:10.1037/a0013247
Berthold, K., Röder, H., Knörzer, D., Kessler, W., & Renkl, A. (2011). The double-edged effects of explanation prompts. Computers in Human Behavior, 27(1), 69–75. doi:10.1016/j.chb.2010.05.025
Bielaczyc, K., Pirolli, P. L., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigatin the effects of knowledge acquisition activities on problem-solving. Cognition and Instruction, 13(2), 221–252. doi:10.1207/s1532690xci1302_3
Booth, J. L., Lange, K. E., Koedinger, K. R., & Newton, K. J. (2013). Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning and Instruction, 25, 24–34. doi:10.1016/j.learninstruc.2012.11.002
Broers, N. J., & Imbos, T. (2005). Charting and manipulating propositions as methods to promote self-explanation in the study of statistics. Learning and Instruction, 15(6), 517–538. doi:10.1016/j.learninstruc.2005.08.005
Brown, A. L., & Kane, M. J. (1988). Preschool children can learn to transfer: Learning to learn and learning from example. Cognitive Psychology, 20(4), 493–523. doi:10.1016/0010-0285(88)90014-x
Calin-Jageman, R. J., & Ratner, H. H. (2005). The role of encoding in the self-explanation effect. Cognition and Instruction, 23(4), 523–543. doi:10.1207/s1532690xci2304_4
Canobi, K. H. (2009). Concept-procedure interactions in children's addition and subtraction. Journal of Experimental Child Psychology, 102(2), 131–149.
Chi, M. T. H. (2000). Self-explaining: The dual processes of generating inference and repairing mental models. In R. Glaser (Ed.), Advances in Instructional Psychology: Educational Design and Cognitive Science (Vol. 5, pp. 161–238).
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. doi:10.1016/0364-0213(89)90002-5
Chi, M. T. H., de Leeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. doi:10.1207/s15516709cog1803_3
Cho, Y. H., & Jonassen, D. H. (2012). Learning by self-explaining causal diagrams in high-school biology. Asia Pacific Education Review, 13(1), 171–184. doi:10.1007/s12564-011-9187-4
Chou, C.-Y., & Liang, H.-T. (2009). Content-free computer supports for self-explaining: Modifiable typing interface and prompting. Educational Technology & Society, 12(1), 121–133.
Common Core State Standards. (2010). Washington D.C.: National Governors Association Center for Best Practices & Council of Chief State School Officers.
Crowley, K., & Siegler, R. S. (1999). Explanation and generalization in young children's strategy learning. Child Development, 70(2), 304–316. doi:10.1111/1467-8624.00023
de Bruin, A. B., Rikers, R. M., & Schmidt, H. G. (2007). The effect of self-explanation and prediction on the development of principled understanding of chess in novices. Contemporary Educational Psychology, 32(2), 188–205. doi:10.1016/j.cedpsych.2006.01.001
De Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Paas, F. (2010). Learning by generating vs. receiving instructional explanations: Two approaches to enhance attention cueing in animations. Computers & Education, 55(2), 681–691. doi:10.1016/j.compedu.2010.02.027
de Koning, B. B., Tabbers, H. K., Rikers, R. M. J. P., & Pass, F. (2011). Improved effectiveness of cueing by self-explanations when learning from a complex animation. Applied Cognitive Psychology, 25(2), 183–194. doi:10.1002/acp.1661
DeCaro, M. S., & Rittle-Johnson, B. (2012). Exploring mathematics problems prepares children to learn from instruction. Journal of Experimental Child Psychology, 113(4), 552–568.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58. doi:10.1177/1529100612453266
Durkin, K. (2011). The self-explanation effect when learning mathematics: A meta-analysis. Paper presented at the Society for Research on Educational Effectiveness, Washington, DC.
Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22(3), 206–214. doi:10.1016/j.learninstruc.2011.11.001
Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. H. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47–61. doi:10.1016/j.learninstruc.2011.06.002
Gagné, R. M., & Smith, E. C. (1962). A study of the effects of verbalization on problem solving. Journal of Experimental Psychology, 63(1), 12–18. doi:10.1037/h0048703
Gerjets, P., Scheiter, K., & Catrambone, R. (2006). Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations? Learning and Instruction, 16, 104–121. doi:10.1016/j.learninstruc.2006.02.007
Große, C. S., & Renkl, A. (2006). Effects of multiple solution methods in mathematics learning. Learning and Instruction, 16(2), 122–138. doi:10.1016/j.learninstruc.2006.02.001
Große, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 17(6), 612–634. doi:10.1016/j.learninstruc.2007.09.008
Gurung, R. A., Weidert, J., & Jeske, A. (2010). Focusing on how students study. Journal of the Scholarship of Teaching and Learning, 10(1), 28–35.
Hartwig, M. K., & Dunlosky, J. (2012). Study strategies of college students: Are self-testing and scheduling related to achievement? Psychonomic Bulletin & Review, 19(1), 126–134. doi:10.3758/s13423-011-0181-y
Hilbert, T. S., Renkl, A., Kessler, S., & Reiss, K. (2008). Learning to prove in geometry: Learning from heuristic examples and how it can be supported. Learning and Instruction, 18(1), 54–65. doi:10.1016/j.learninstruc.2006.10.008
Honomichl, R. D., & Chen, Z. (2006). Learning to align relations: The effects of feedback and self-explanation. Journal of Cognition and Development, 7(4), 527–550. doi:10.1207/s15327647jcd0704_5
Howie, D. E., & Vicente, K. J. (1998). Making the most of ecological interface design: The role of self-explanation. International Journal of Human-Computer Studies, 49(5), 651–674. doi:10.1006/ijhc.1998.0207
Huk, T., & Ludwigs, S. (2009). Combining cognitive and affective support in order to promote learning. Learning and Instruction, 19(6), 495–505. doi:10.1016/j.learninstruc.2008.09.001
Johnson, C. I., & Mayer, R. E. (2010). Applying the self-explanation principle to multimedia learning in a computer-based game-like environment. Computers in Human Behavior, 26(6), 1246–1252. doi:10.1016/j.chb.2010.03.025
Kramarski, B., & Dudai, V. (2009). Group-metacognitive support for online inquiry in mathematics with differential self-questioning. Journal of Educational Computing Research, 40(4), 377–404. doi:10.2190/EC.40.4.a
Kuhn, D., & Katz, J. (2009). Are self-explanations always beneficial? Journal of Experimental Child Psychology, 103(3), 386–394. doi:10.1016/j.jecp.2009.03.003
Larsen, D. P., Butler, A. C., & Roediger, H. L. (2013). Comparative effects of test-enhanced learning and self-explanation on long-term retention. Medical Education, 47(7), 674–682.
Legare, C. H. (2014). The contributions of explanation and exploraton to scientific reasoning. Child Development Perspectives, 8(2), 101–106. doi:10.1111/cdep.12070
Legare, C. H., Gelman, S. A., & Wellman, H. M. (2010). Inconsistency with prior knowledge triggers children's causal explanatory reasoning. Child Development, 81(3), 929–944. doi:10.1111/j.1467-8624.2010.01443.x
Legare, C. H., & Lombrozo, T. (2014). The selective benefits of explanation on learning in early childhood. Journal of Experimental Child Psychology, 126, 198–212.
Lombrozo, T. (2006). The structure and function of explanations. TRENDS in Cognitive Science, 10(10), 464–470. doi:10.1016/j.tics.2006.08.004
Matthews, P., & Rittle-Johnson, B. (2009). In pursuit of knowledge: Comparing self-explanations, concepts, and procedures as pedagogical tools. Journal of Experimental Child Psychology, 104(1), 1–21. doi:10.1016/j.jecp.2008.08.004
Mayer, R. E., & Johnson, C. I. (2010). Adding instructional features that promote learning in a game-like environment. Journal of Educational Computing Research, 42(3), 241–265. doi:10.2190/EC.42.3.a
McEldoon, K. L., Durkin, K. L., & Rittle-Johnson, B. (2013). Is self-explanation worth the time? A comparison to additional practice. British Journal of Educational Psychology, 83(4), 615–632. doi:10.1111/j.2044-8279.2012.02083.x
McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38(1), 1–30. doi:10.1207/s15326950dp3801_1
McNeil, N. M., Chesney, D. L., Matthews, P. G., Fyfe, E. R., Petersen, L. A., Dunwiddie, A. E., & Wheeler, M. C. (2012). It pays to be organized: Organizing arithmetic practice around equivalent values facilitates understanding of math equivalence. Journal of Educational Psychology, 104(4), 1109–1121. doi:10.1037/a0028997
Molesworth, B. R. C., Bennett, L., & Kehoe, E. J. (2011). Promoting learning, memory, and transfer in a time-constrained, high hazard environment. Accident Analysis and Prevention, 43(3), 932–938. doi:10.1016/j.aap.2010.11.016
Mugford, R., Corey, S., Bennell, C., & Martens, C. (2009). A meta-analysis of the self-explanation effect. Paper presented at the 3rd International Cognitive Load Theory Conference, Heerlen, the Netherlands.
Mwangi, W., & Sweller, J. (1998). Learning to solve compare word problems: The effect of example format and generating self-explanations. Cognition and Instruction, 16(2), 173–199. doi:10.1207/s1532690xci1602_2
Nokes, T. J., Hausmann, R. G. M., VanLehn, K., & Gershman, S. (2011). Testing the instructional fit hypothesis: The case of self-explanation prompts. Instructional Science, 39(5), 645–666. doi:10.1007/s11251-010-9151-4
Pashler, H., Bain, P. M., Bottge, B. A., Graesser, A., Koedinger, K., McDaniel, M., & Metcalfe, J. (2007). Organizing instruction and study to improve student learning. Washington, DC: Institute of Education Sciences.
Pillow, B. H., Mash, C., Aloian, S., & Hill, V. (2002). Facilitating children's understanding of misinterpretation: Explanatory efforts and improvements in perspective taking. The Journal of Genetic Psychology: Research and Theory on Human Development, 163(2), 133–148. doi:10.1080/00221320209598673
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1–29. doi:10.1016/S0364-0213(99)80017-2
Renkl, A. (1999). Learning mathematics from worked-out examples: Analyzing and fostering self-explanations. European Journal of Psychology of Education, 14(4), 477–488. doi:10.1007/BF03172974
Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary Educational Psychology, 23(1), 90–108. doi:10.1006/ceps.1997.0959
Rittle-Johnson, B. (2006). Promoting transfer: Effects of self-explanation and direct instruction. Child Development, 77(1), 1–15. doi:10.1111/j.1467-8624.2006.00852.x
Rittle-Johnson, B., Fyfe, E. R., Loehr, A. M., & Miller, M. R. (2015). Beyond numeracy in preschool: Adding patterns to the equation. Early Childhood Research Quarterly, 31, 101–112. doi:10.1016/j.ecresq.2015.01.005
Rittle-Johnson, B., & Loehr, A. M. (2016). Instruction based on self-explanation. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed.). New York, NY: Routledge. in press.
Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers & Education, 46(4), 426–445. doi:10.1016/j.compedu.2004.08.011
Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99(2), 285–296. doi:10.1037/0022-06220.127.116.115
Siegler, R. S. (1995). How does change occur: A microgenetic study of number conservation. Cognitive Psychology, 28(3), 225–273. doi:10.1006/cogp.1995.1006
Siegler, R. S. (2002). Microgenetic studies of self-explanation. In N. Garnott & J. Parziale (Eds.), Microdevelopment: A process-oriented perspective for studying development and learning (pp. 31–58). Cambridge, MA: Cambridge University Press.
Siegler, R. S., & Chen, Z. (2008). Differentiation and integration: Guiding principles for analyzing cognitive change. Developmental Science, 11(4), 433–448.
Tenenbaum, H. R., Alfieri, L., Brooks, P. J., & Dunne, G. (2008). The effects of explanatory conversations on children's emotion understanding. British Journal of Developmental Psychology, 26(2), 249–263. doi:10.1348/026151007x231057
Williams, J. J., & Lombrozo, T. (2010). The role of explanation in discovery and generalization: Evidence from category learning. Cognitive Science, 34(5), 776–806. doi:10.1111/j.1551-6709.2010.01113.x
Williams, J. J., & Lombrozo, T. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology, 66(1), 55–84. doi:10.1016/j.cogpsych.2012.09.002
Williams, J. J., Lombrozo, T., & Rehder, B. (2013). The hazards of explanation: Overgeneralization in the face of exceptions. Journal of Experimental Psychology: General, 142(4), 1006–1014. doi:10.1037/a0030996
Wittwer, J., & Renkl, A. (2010). How effective are instructional explanations in example-based learning? A meta-analytic review. Educational Psychology Review, 22(4), 393–409. doi:10.1007/s10648-010-9136-5
Wylie, R., & Chi, M. T. (2014). The self-explanation principle in mulimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (Vol. 2, pp. 413–432). New York, NY: Cambridge University Press.
Wylie, R., Koedinger, K., & Mitamura, T. (2010). Analogies, explanations and practice: examining how task types affect second language grammar learning. In V. Aleven, J. Kay, & J. Mostow (Eds.), Proceedings of the 10th international conference on intelligent tutoring systems, ITS 2010 (Vol. 1). Heidelberg: Springer.
Wylie, R., Sheng, M., Mitamura, T., & Koedinger, K. R. (2011). Effects of adaptive prompted self-explanation on robust learning of second language grammar. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, Auckland, New Zealand (pp. 588–590). Berlin: Springer.
Yeh, Y.-F., Chen, M.-C., Hung, P.-H., & Hwang, G.-J. (2010). Optimal self-explanation prompt design in dynamic multi-representational learning environments. Computers & Education, 54(4), 1089–1100.
Writing of this article was supported in part by National Science Foundation grant DRL-0746565 to B.R.-J.
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Rittle-Johnson, B., Loehr, A.M. Eliciting explanations: Constraints on when self-explanation aids learning. Psychon Bull Rev 24, 1501–1510 (2017). https://doi.org/10.3758/s13423-016-1079-5