Simply defined, learning involves the use of existing knowledge to make meaning of new information (Dris-coll, 2000). The process of explaining to-be-learned materials to oneself has been called self-explanation and is considered to be a constructive activity (e.g., Calin-Jageman & Ratner, 2005; Tajika, Nakatsu, Nozaki, Neumann, & Maruno, 2007). Self-explanation facilitates learning in one of two manners. The process prompts students to form inferences beyond the provided information, extending and supporting their knowledge revision (McNamara, 2004; Taboada & Guthrie, 2006). This form of self-explanation helps students compensate for text inadequacies, inconsistencies, and incompleteness. Self-explanation also encourages students to revise their current understandings of concepts by prompting them to compare their inaccurate and/or incomplete understandings with those presented in text. New learning unfolds as students attempt to reduce inconsistencies between their existing knowledge structures and new information (Ainsworth & Burcham, 2007).
In their seminal work, Chi and colleagues (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Chi & Bassok, 1989) instructed undergraduate students to provide self-explanations in response to worked examples. The researchers observed that undergraduates who demonstrated proficiency in problem solving (82% correct or higher) produced twice as many self-explanations, confirming earlier findings that providing justifications for solution steps while problem solving improves learning (e.g., Gagné & Smith, 1962). Moreover, Chi and colleagues observed that the nature of students' explanations differed qualitatively. Successful problem solvers provided self-explanations that refined, defined, and expanded action components of the examples, and used them as references to principles and concepts outlined in the text. These students' explanations indicated self-monitoring of new understandings and misunderstandings. Students who were less successful problem solvers generated insufficient and/or superficial explanations and did not monitor their learning with subsequent analyses confirming these distinctions (Chi & VanLehn, 1991; Renkel, 1997). Equally important, these studies revealed that, in the absence of specific instructions or supports, most students either do not generate self-explanations or generate superficial ones only (e.g., Atkinson, Renkl, & Merrill, 2003; Chi & Bassok, 1989; Chi, Leeuw, Chiu, & LaVancher, 1994; McNamara, O'Reilly, Best, & Ozuru, 2006; Schworm & Renkl, 2007).
Findings from earlier research documented that undergraduates who generated self-explanations demonstrated greater learning than students who did not produce self-explanations. However, they learned less overall than students who were provided with expert-generated explanations (Stein & Bransford, 1979). Closer analysis revealed that expert-generated effects were limited to instances in which students generated imprecise elaborations, corroborating that self-explanation effects are contingent on having students produce meaningful explanations that either establish connections between text ideas or integrate them with relevant prior knowledge (Chi, 2000; King & Rosenshine, 1993; McNamara & Kintsch, 1996; Rosenshine, Meister, & Chapman, 1996; Scardamalia & Bereiter, 1992).
The quality of instructor-generated explanations is also an important instructional factor. When experts or instructors verbalize “why-type” explanations, they encourage students to practice self-explanation. This is especially true when working with young students (Crowley & Siegler, 1999; Peters, Messer, Smith, & Davey, 1999; Pine & Messer, 2000; Siegler, 1995). When prompted to describe the instructor's problem-solving strategy, kindergarten students produced superior self-explanations and used this problem-solving strategy more frequently than did students who responded to their own thinking (Calin-Jageman & Ratner, 2005). Modeling provides students with the opportunity to reflect on instructors' rationales and reasoning, and provides them with opportunities to gain proficiency in the use of self-explanation.
There is substantial evidence that all students can be taught to produce self-explanations and that doing so produces subsequent learning gains relative to not doing so or being provided with generated explanations (post-secondary: Aleven & Koedinger, 2002; Bielaczyc, Pirolli & Brown, 1995; de Bruin, Rikers, & Schmidt, 2007; Reimann & Neubert, 2000; secondary: Chi et al., 1994; Siegler, 2002; Wong, Lawson & Keeves, 2002; elementary: Davis, 2003; Tajika & Nakatsu, 2005; Tajika, Nakatsu, & Nozaki, 2006). For example, Rittle-Johnson's 2006 study (2006) demonstrated that students in grades 3 to 5 were able to solve more mathematical equivalency problems following instructions to generate self-explanations than were students who were not provided with such instructions. Students' learning gains held across measures of immediate and delayed learning and were especially pronounced for measures of procedural learning and transfer.
Providing students with instruction in the use of specific thinking prompts and learning strategies can enhance the quality of self-explanation. The use of thought-provoking question stems is one technique for assisting students to generate quality self-explanations when processing text independently, in pairs or in small groups (King, 1989; 1990; 1991; 1992; 1994). Question stems are based on higher-level thinking skills and are intended to encourage students to draw upon their existing knowledge to generate applications (e.g., “How would you use _____ to _____ ?”), explanations (e.g., “Explain why …”) evaluations (e.g., “Which one is the best _____ and why?”), and other forms of higher-level thought (e.g., “What do you think would happen if …?”). Students who generate self-explanations that extend beyond the literal level of the text demonstrate enhanced retention and understanding (e.g., National Reading Panel, 2000; Taboada & Guthrie, 2006).
Students have also benefited from receiving instruction intended to promote the use of multiple strategies that promote self-explanation and metacognitive awareness (McNamara, 2004). McNamara found that undergraduates instructed to generate elaborations and predictions, use logic, make bridging inferences, and paraphrase and monitor comprehension, demonstrated superior learning for unfamiliar materials compared to their uninstructed peers. They titled their program the Self-Explanation Reading Training (SERT). While students with high and low levels of prior knowledge benefited from instruction, gains were especially pronounced for participants with low prior knowledge when responding to text-based questions.
Self-explanation may benefit certain students more than others. Specifically, students who possess relatively low levels of prior knowledge demonstrate greater learning following self-explanation than those with higher levels of prior knowledge (Renkl, Stark, Gruber, & Mandl, 1998). The former group of students typically do not activate prior knowledge or engage in other elab-orative processes during new learning experiences (McNamara, 2001; McNamara & Kintsch, 1996). Presumably, engaging in the process of self-explanation is more helpful for these students than their peers who possess higher levels of prior knowledge as it encourages them to adopt strategic processes while studying and allows them to maximize working memory capacity (Best, Rowe, Ozuru & McNamara, 2005). There is also evidence that the accuracy of the self-explanation does not dramatically affect students' learning gains. That is, the process of producing self-explanations, regardless of the accuracy of the explanations, seems sufficient to enhance the learning process. To this end, some researchers have argued that the process of generating self-explanations itself induces greater understanding of domain principles (Chi, 2000; Chi & VanLehn, 1991).
Having instructors or experts provide students with instructions to produce self-explanations is not always feasible. However, research supports the conclusion that paraprofessionals and tutors can also promote processing that involves self-explanation. Students working with tutors trained to prompt self-explanation demonstrated superior learning relative to those whose tutors engaged in the typical processes of initiating questions, providing constructive feedback and assessing comprehension. Presumably, scaffolding and prompting in this manner encourages students to control their own learning. Self-explanations in tutoring are especially conducive to addressing students' misconceptions (Anderson, Boyle & Reiser, 1985; Chi, 1996; Chi, Siler, Jeong, Yamauchi, & Hausmann, 2001).
Peers can also encourage each other to generate self-explanations (Chin & Brown, 2000; Minick, 1989). For instance, Webb and her colleagues (Webb, 1991; Webb & Palincsar, 1996) demonstrated that students can be trained to encourage explanations from each other (rather than provide responses) when working in cooperative learning groups. In these learning situations, how instructors interact with students as part of their large group instruction influences the nature of small group interactions (Webb, Nemer, & Ing, 2006). If students are encouraged to verbalize their thinking or ask “why-type” questions as part of teacher-led instruction, they are also likely to adopt these behaviors as part of small group interactions. In essence, students' small-group behaviors mirror instructors' discourse and expectations (Webb et al., 2006). Instructional environments that are entrenched in cultures of low-level questions and sparse explanations produce long-term learning effects that are difficult to overcome.
Computer-based learning environments also provide alternative instructional venues for the promotion of self-explanations. One of the earliest computer models devised to account for and reproduce the self-explanation effect through the use of analogies was Cascade (Van-Lehn, Jones, & Chi, 1992). More recent computer-based learning environments provide students with tutorial-like dialogues that analyze their explanations while problem solving, recognize omissions in their explanations, and provide appropriate feedback. For instance, students who explained their problem-solving steps using Cognitive Tutor demonstrated greater learning for target questions and transfer problems than students who did not use the program and were not required to explain their problem-solving steps (Anderson, Corbett, Koedinger, & Pelletier, 1995; Aleven & Koedinger, 2002; Aleven, Popescu & Koedinger, 2001a; 2001b).
Other computer-based learning environments promote self-explanation through the use of inquiry and metacognitive prompts (Graesser, McNamara, & Van-Lehn, 2005). In the computer-based program, Point & Query (Graesser, Langston, & Baggett, 1993) students are encouraged to ask questions and form deep causal questions. Students control the question-answering process through hypertext, hypermedia, and other learning environments. Similarly, the computer-based program, Autotutor (Graesser, Lu, Jackson, Mitchell, Ventura, Olney, & Louwerse, 2004; Graesser, Person, & Harter, 2001; Graesser, VanLehn, Rose, Jordan, & Harter, 2001), coaches students to produce explanations by responding to their questions in natural language.
One of the most recent computer-based learning environments to assist students' acquisition of metacog-nitive strategies and reading comprehension is iSTART. The iSTART program (Interactive Strategy Trainer for Active Reading and Thinking; McNamara, Levinstein, & Boonthum, 2004; McNamara et al., 2006) is a computer-based learning environment modeled after the face-to-face reading comprehension instructional program SERT. The program is designed to assess the quality of students' self-explanations, provide them with elaborative feedback, and promote the use of active reading strategies.
As part of the iSTART program, students observe a simulated classroom, identifying the strategies that simulated learners use to explain the actions of the simulated instructor. They then produce self-explanations under the guidance of the simulated instructor (McNamara et al., 2004). Adolescents using iSTART demonstrated greater learning for science text than their peers who were provided with a demonstration on how to self-explain text (McNamara, et al., 2006). Applications of iSTART confirm that all students benefited from participating in this program, although learning gains vary as a function of students' prior knowledge. Those with low prior knowledge demonstrate substantial improvement when responding to text-based questions while students with high levels of prior knowledge demonstrate greatest growth when responding to inferential questions (McNa-mara et al., 2006).
In summary, self-explanation is a versatile, effective strategy that students can use independently or as part of group processing to enhance their learning across a variety of instructional tasks, including the processing of expository texts, mathematical problem solving, and the studying of worked examples. Students from kindergarten through postsecondary school have benefited from instruction in the generation of self-explanations, demonstrating enhanced learning and metacognitive awareness. Learning gains associated with self-explanation are especially powerful for students who possess relatively low levels of prior knowledge for target materials. Instructors, tutors, and peers can effectively encourage students to adopt self-explanation practices with minimal training. Alternatively, computer-based learning environments also hold great promise with respect to enhancing students' use of this effective learning strategy.
See also:Cognitive Strategies
Ainsworth, S., & Burcham, S. (2007). The impact of text coherence on learning by self-explanation. Learning and Instruction, 17, 286–303.
Aleven, V., & Koedinger, K. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179.
Aleven, V., Popescu, O., & Koedinger, K. R. (2001a). Towards tutorial dialog to support self-explanations: adding natural language understanding to a cognitive tutor. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.), Artificial intelligence in education: AI-ED in the wired and wireless future (pp. 246– 255). Amsterdam: IOS Press.
Aleven V., Popescu, O. & Koedinger, K. R. (2001b). Pedagogical content knowledge in a tutorial dialogue system to support self-explanation. In Papers of the AIED-2001 Workshop on Tutorial Dialogue Systems, 59–70.
Anderson, J. R., Boyle, C. F., & Reiser, B. J. (1985). Intelligent tutoring systems. Science, 228, 456–468.
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4, 167–207.
Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Combining fading with prompting fosters learning. Journal of Educational Psychology, 95, 774–783.
Best, R.M., Rowe, M., Ozuru, Y., & McNamara, D. (2005). Deep-level comprehension of science texts. Topics in Language Disorders, 25(1), 65–83.
Bielaczyc, K., Pirolli, P., & Brown, A. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221–252.
Calin-Jageman, R., & Ratner, H. (2005). The role of encoding in the self-explanation effect. Cognition and Instruction, 23(4), 523–543.
Chi, M. T. H. (1996). Constructing self-explanations and scaffolded explanations in tutoring. Applied Cognitive Psychology, 10, 33–49.
Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science (Vol. 5, pp. 161–238). Mahwah, NJ: Erlbaum.
Chi, M. T. H., & Bassok, M. (1989). Learning from examples via self-explanations. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 251–282). Hillsdale, NJ: Erlbaum.
Chi, M. T. H., Bassok, M., Lewis, M., Reimann, P., & Glasser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.
Chi, M. T. H., Leeuw, N., Chiu, M., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.
Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533.
Chi, M. T. H., & VanLehn, K. A. (1991). The content of physics self-explanations. The Journal of the Learning Sciences, 1(1), 69–105.
Chin, C., & Brown, D. (2000). Learning in science: A comparison of deep and surface approaches. Journal of Research in Science Teaching, 37(2), 109–138.
Crowley, K., & Siegler, R. (1999). Explanation and generalization in young children's strategy learning. Child Development, 70, 304–316.
Davis, E. (2003). Prompting middle school science students for productive reflection: generic and directed prompts. The Journal of the Learning Sciences, 12(1), 91–142.
de Bruin, A., Rikers, R., & Schmidt, H. (2007). The effect of self-explanation and prediction on the development of principled understanding of chess in novices. Contemporary Educational Psychologist, 32, 188–205.
Driscoll, M. (2000). Psychology of learning for instruction. (2nd ed.). Boston: Allyn & Bacon.
Gagné, R. M., & Smith, E. C. Jr. (1962). A study of the effects of verbalization on problem solving. Journal of Experimental Psychology, 63, 12–18.
Graesser, A. C., Langston, M. C., & Baggett, W. B. (1993). Exploring information about concepts by asking questions. In G. V. Nakamura, R. M. Taraban, & D. Medin (Eds.), The psychology of learning and motivation: Vol. 29. Categorization by humans and machines (pp. 411–436). Orlando, FL: Academic Press.
Graesser, A. C., Lu, S., Jackson, G. T., Mitchell, H., Ventura, M., Olney, A., et al. (2004). AutoTutor: A tutor with dialogue in natural language. Behavioral Research Methods, Instruments, and Computers, 36, 180–192.
Graesser, A., McNamara, D., & VanLehn, K. (2005). Scaffolding deep comprehension strategies through Point & Query, AutoTutor, and iSTART. Educational Psychologist, 40(4), 225–234.
Graesser, A. C., Person, N. K., & Harter, D. (2001). Teaching tactics and dialogue in AutoTutor. International Journal of Artificial Intelligence in Education, 12, 257–279.
Graesser, A. C., VanLehn, K., Rose, C., Jordan, P., & Harter, D. (2001). Intelligent tutoring systems with conversational dialogue. AI Magazine, 22, 39–51.
King, A. (1989). Effects of self-questioning training on college students' comprehension of lectures. Contemporary Educational Psychology, 14(4), 1–16.
King, A. (1990). Enhancing peer interaction and learning in the classroom. American Educational Research Journal, 27, 664–687.
King, A. (1991). Improving lecture comprehension: Effects of a metacognitive strategy. Applied Cognitive Psychology, 5, 331–346.
King, A. (1992). Facilitating elaborative learning through guided student-generated questioning. Educational Psychologist, 27(1), 111–126.
King, A. (1994). Guiding knowledge construction in the classroom: Effects of teaching children how to question and how to explain. American Educational Research Journal, 31(2), 338–368.
King, A., & Rosenshine, B. (1993). Effects of guided cooperative questioning on children's knowledge construction. Journal of Experimental Education, 61, 127–148.
McNamara, D. S. (2001). Reading both high-coherence and low coherence texts: Effects of text sequence and prior knowledge. Canadian Journal of Experimental Psychology 55, 55, 51–62.
McNamara, D. S. (2004). SERT: Self-explanation reading training. Discourse Processes, 38(1), 1–30.
McNamara, D. S., & Kintsch, W (1996). Learning from text: Effects of prior knowledge and text coherence. Discourse Processes, 22, 247–287.
McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy trainer for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222–233.
McNamara, D. S., O'Reilly, T. Best, R., & Ozuru, Y. (2006). Improving adolescent students' reading comprehensions with iSTART. Journal of Educational Computing Research, 34(2), 147–171.
Minick, N. (1989). Mind and activity in Vygotsky's work: an expanded frame of reference. Cultural Dynamics, 2, 162–187.
National Reading Panel. (2000). Teaching children to read: An evidence-based assessment of the scientific research literature on reading and its implications for reading instruction.
Washington, DC: National Institute of Child Health and Human Development, and Department of Education.
Peters, L., Messer, D., Smith, P., & Davey, N. (1999). An investigation into Karmiloff-Smith's RR model: The effects of structure tuition. British Journal of Developmental Psychology, 17, 277–292.
Pine, K., & Messer, D. (2000). The effect of explaining another's actions on children's implicit theories of balance. Cognition and Instruction, 18, 35–51.
Reimann, P., & Neubert, C. (2000). The role of self-explanation in learning to use a spreadsheet through examples. Journal of Computer Assisted Learning, 16, 316–325.
Renkl, A. (1997). Learning from worked out examples: A study on individual differences. Cognitive Science, 21(1), 1–2.
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, 90–108.
Rittle-Johnson, B. (2006). Promoting transfer: Effects of self-explanation and direct instruction. Child Development, 77(1), 1–15.
Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of intervention studies. Review of Educational Research, 66, 181–221.
Scardamalia, M., & Bereiter, C. (1992). Text-based and knowledge-based questioning by children. Cognition and Instruction, 9, 177–199.
Schworn, S., & Renkl, A. (2007). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers and Education, 46, 426–445.
Siegler, R. (1995). How does change occur: A microgenetic study of number conservation. Cognitive Psychology, 28, 225–273.
Siegler, R. (2002). Microgenetic studies of self-explanation. In N. Granott & J. Parziale (Eds.), Microdevelopment: Transition processes in development and learning (pp. 31–58). Cambridge, England: Cambridge University Press.
Stein, B. S., & Bransford, J. D. (1979). Constraints on effective elaboration: Effects of precision and subject generation. Journal of Verbal Learning and Verbal Behavior, 18, 769–777.
Taboada, A., & Guthrie, T. (2006). Contributions of student questioning and prior knowledge to construction of knowledge from reading information text. Journal of Literacy Research, 38(1), 1–35.
Tajika, H., & Nakatsu, N. (2005). Using a metacognitive strategy to solve mathematical problems. Bulletin of the Aichi University of Education, 54, 1–9.
Tajika, H., Nakatsu, N., & Nozaki, H. (2006). The effect of self-explanation on solving mathematical word problems. Bulletin of Educational Research and Curriculum Development of the Aichi University of Education, 9, 1–8.
Tajika, H., Nakatsu, N., Nozaki, H., Neumann, E., & Maruno, S. (2007). Effects of self-explanation as a metacognitive strategy for solving mathematical word problems. Japanese Psychological Research, 49(3), 222–233.
VanLehn, K., Jones, R., & Chi, M. T. H. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2(1), 1–59.
Webb, N. M. (1991). Task-related verbal interaction and mathematics learning in small groups. Journal for Research in Mathematics Education, 22, 366–389.
Webb, N. M., Nemer, K., & Ing, M. (2006). Small-group reflections: Parallels between teacher discourse and student behavior in peer-directed groups. The Journal of the Learning Sciences, 15(1), 63–119.
Webb, N. M., & Palincsar, A. S. (1996). Group processes in the classroom. In D. Berliner & R. Calfee (Eds.), Handbook of educational psychology (pp. 841–873). New York: Macmillan.
Wong, R., Lawson, M. J., & Keeves, J. P. (2002). The effects of self-explanation training on students' problem solving in high school mathematics. Learning and Instruction, 12, 233–262.
Washington Virtual Academies
Tuition-free online school for Washington students.
- Kindergarten Sight Words List
- Signs Your Child Might Have Asperger's Syndrome
- 10 Fun Activities for Children with Autism
- Social Cognitive Theory
- Problems With Standardized Testing
- First Grade Sight Words List
- Child Development Theories
- April Fools! The 10 Best Pranks to Play on Your Kids
- Theories of Learning
- Nature and Nurture