Inducing Self-Explanation: a Meta-Analysis


Self-explanation is a process by which learners generate inferences about causal connections or conceptual relationships. A meta-analysis was conducted on research that investigated learning outcomes for participants who received self-explanation prompts while studying or solving problems. Our systematic search of relevant bibliographic databases identified 69 effect sizes (from 64 research reports) which met certain inclusion criteria. The overall weighted mean effect size using a random effects model was g = .55. We coded and analyzed 20 moderator variables including type of learning task (e.g., solving problems, studying worked problems, and studying text), subject area, level of education, type of inducement, and treatment duration. We found that self-explanation prompts are a potentially powerful intervention across a range of instructional conditions. Due to the limitations of relying on instructor-scripted prompts, we recommend that future research explore computer-generation of self-explanation prompts.

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Articles included in the meta-analyses are marked with an asterisk

  1. *Ainsworth, S., & Burcham, S. (2007). The impact of text coherence on learning by self-explanation. Learning and Instruction, 17(3), 286–303.

  2. 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.

    Article  Google Scholar 

  3. 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.

    Article  Google Scholar 

  4. *Berthold, K., & Renkl, A. (2009). Instructional aides to support a conceptual understanding of multiple representations. Journal of Educational Psychology, 101(1), 70–87.

  5. *Berthold, K., Nueckles, M., & Renkl, A. (2007). Do learning protocols support learning strategies and outcomes? The role of cognitive and metacognitive prompts. Learning and Instruction, 17(5), 564–577.

  6. *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: An International Journal of the Learning Sciences, 37(4), 345–363.

  7. Biemans, H., & Van Mil, M. (2008). Learning styles of Chinese and Dutch students compared within the context of Dutch higher education in life sciences. Journal of Agricultural Education and Extension, 14(3), 265–278.

    Article  Google Scholar 

  8. *Bodvarsson, M. C. (2005). Prompting students to justify their response while problem solving: A nested, mixed-methods study (unpublished doctoral dissertation). University of Nebraska-Lincoln, USA.

  9. Booth, J. L., & Koedinger, K. R. (2012). Are diagrams always helpful tools? Developmental and individual differences in the effect of presentation format on student problem solving. British Journal of Educational Psychology, 82(3), 492–511.

    Article  Google Scholar 

  10. *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.

  11. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2005). Comprehensive meta-analysis, version 3.3.070 [computer software]. Englewood, NJ: Biostat Inc (Englewood, NJ).

  12. Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis. Chichester: Wiley.

    Google Scholar 

  13. Carpenter, S. K., Cepeda, N. J., Rohrer, D., Kang, S. H., & Pashler, H. (2012). Using spacing to enhance diverse forms of learning: review of recent research and implications for instruction. Educational Psychology Review, 24(3), 369–378.

    Article  Google Scholar 

  14. *Chamberland, M., St-Onge, C., Setrakian, J., Lanthier, L., Bergeron, L., Bourget, A., et al. (2011). The influence of medical students’ self-explanations on diagnostic performance. Medical Education, 45(7), 688–695.

  15. 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 (pp. 161–238). Hillsdale: Lawrence Erlbaum Associates.

    Google Scholar 

  16. Chi, M., & Wylie, R. (2014). The ICAP framework: linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.

    Article  Google Scholar 

  17. 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.

    Article  Google Scholar 

  18. *Chi, M. T. H., DeLeeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477.

  19. *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.

  20. *Chou, C., & Liang, H. (2009). Content-free computer supports for self-explaining: Modifiable typing interface and prompting. Educational Technology & Society, 12(1), 121–133.

  21. Chu, J., Rittle-Johnson, B., & Fyfe, E. R. (2017). Diagrams benefit symbolic problem-solving. British Journal of Educational Psychology, 87(2), 273–287.

    Article  Google Scholar 

  22. Chung, S., Chung, M.J., & Severance, C. (1999). Design of support tools and knowledge building in a virtual university course: effect of reflection and self-explanation prompts. Paper presented at the WebNet 99 World Conference on the WWW and Internet Proceedings, Honolulu, Hawaii. (ERIC Document Reproduction Service No. ED448706).

  23. *Chung, S., Severance, C., & Chung, M. J. (2003). Design of support tools for knowledge building in a virtual university course. Interactive Learning Environments, 11(1), 41–57.

  24. *Coleman, E. B., Brown, A. L., & Rivkin, I. D. (1997). The effect of instructional explanations on learning from scientific texts. The Journal of the Learning Sciences, 6(4), 347–365.

  25. Conati, C., & Vanlehn, K. (2000). Toward computer-based support of meta-cognitive skills: a computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education (IJAIED), 11, 389–415.

    Google Scholar 

  26. *Craig, S. D., Sullins, J., Witherspoon, A., & Gholson, B. (2006). The deep-level-reasoning-question effect: the role of dialogue and deep-level-reasoning questions during vicarious learning. Cognition and Instruction, 24(4), 565–591.

  27. *Crippen, K. J., & Earl, B. L. (2007). The impact of web-based worked examples and self-explanation on performance, problem solving, and self-efficacy. Computers and Education, 49(3), 809–821.

  28. *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.

  29. *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.

  30. *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.

  31. Dunlosky, J., Rawson, K., Marsh, E., Nathan, M., & Willingham, D. (2013). Improving students’ learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58.

    Article  Google Scholar 

  32. Dunsworth, Q., & Atkinson, R. K. (2007). Fostering multimedia learning of science: exploring the role of an animated agent's image. Computers & Education, 49(3), 677–690.

    Article  Google Scholar 

  33. *Earley, C. E. (1998). Expertise acquisition in auditing: Training novice auditors to recognize cue relationships in real estate valuation. (9906361 Ph.D.), Ann Arbor: University of Pittsburgh.

  34. *Earley, C. E. (2001). Knowledge acquisition in auditing: training novice auditors to recognize cue relationships in real estate valuation. The Accounting Review, 76(1), 81–97.

  35. *Eckhardt, M., Urhahne, D., Conrad, O., & Harms, U. (2013). How effective is instructional support for learning with computer simulations? Instructional Science, 41(1), 105–124.

  36. Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis (2nd ed.). Cambridge: Bradford/MT Press.

    Google Scholar 

  37. *Eysink, T. H. S., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: an analysis across instructional approaches. American Educational Research Journal, 46(4), 1107–1149.

  38. Fonseca, B. A., & Chi, M. T. (2011). The self-explanation effect: A constructive learning activity. In R. Mayer & P. Alexander (Eds.), Handbook of Research on Learning and Instruction (pp. 270–321). Routeledge Press.

  39. Fox, M. C., & Charness, N. (2010). How to gain eleven IQ points in ten minutes: thinking aloud improves Raven's matrices performance in older adults. Aging, Neuropsychology, and Cognition, 17(2), 191–204.

    Article  Google Scholar 

  40. Frambach, J. M., Driessen, E. W., Chan, L. C., & van der Vleuten, C. P. (2012). Rethinking the globalisation of problem-based learning: how culture challenges self-directed learning. Medical Education, 46(8), 738–747.

    Article  Google Scholar 

  41. *Fukaya, T. (2013). Explanation generation, not explanation expectancy, improves metacomprehension accuracy. Metacognition and Learning, 8(1), 1–18.

  42. 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.

    Article  Google Scholar 

  43. *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(2), 104–121.

  44. Gestsdottir, S., & Lerner, R. M. (2008). Positive development in adolescence: the development and role of intentional self-regulation. Human Development, 51(3), 202–224.

    Article  Google Scholar 

  45. *Graf, E. (2000). Designing a computer tutorial to correct a common student misconception in mathematics (unpublished doctoral dissertation). University of Washington, USA.

  46. *Griffin, T. D., Wiley, J., & Thiede, K. W. (2008). Individual differences, rereading, and self-explanation: concurrent processing and cue validity as constraints on metacomprehension accuracy. Memory & Cognition, 36(1), 93–103.

  47. *Große, C. S., & Renkl, A. (2006). Effects of multiple solution methods in mathematics learning. Learning and Instruction, 16(2), 122–138.

  48. Hartman, H. J. (2001). Developing students’ metacognitive knowledge and skills. In Metacognition in learning and instruction (pp. 33–68). Netherlands: Springer.

    Google Scholar 

  49. Hartman, H. J., Everson, H. T., Toblas, S., & Gourgey, A. F. (1996). Self-concept and metacognition in ethnic minorities: predictions from the BACEIS model. Urban Education, 31(2), 222–238.

    Article  Google Scholar 

  50. Hattie, J. (2009). Visible learning: A synthesis of 800+ meta-analyses on achievement. Abingdon. Abingdon: Routledge.

    Google Scholar 

  51. *Hausmann, R. G., & Vanlehn, K. (2007). Explaining self-explaining: a contrast between content and generation. Frontiers in Artificial Intelligence and Applications, 158, 417–424.

  52. *Hausmann, R. G., & VanLehn, K. (2010). The effect of self-explaining on robust learning. International Journal of Artificial Intelligence in Education, 20(4), 303–332.

  53. Hedges, L., & Olkin, I. (1985). Statistical models for meta-analysis (Vol. 6). New York: Academic Press.

    Google Scholar 

  54. Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 61–83.

    Article  Google Scholar 

  55. *Hilbert, T. S., & Renkl, A. (2009). Learning how to use a computer-based concept-mapping tool: Self-explaining examples helps. Computers in Human Behavior, 25(2), 267–274.

  56. *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.

  57. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

    Google Scholar 

  58. *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.

  59. *Huk, T., & Ludwigs, S. (2009). Combining cognitive and affective support in order to promote learning. Learning and Instruction, 19(6), 495–505.

  60. *Ionas, I. G., Cernusca, D., & Collier, H. L. (2012). Prior knowledge influence on self-explanation effectiveness when solving problems: an exploratory study in science learning. International Journal of Teaching and Learning in Higher Education, 24(3), 349–358.

  61. *Kapli, N. V. (2010). The effects of segmented multimedia worked examples and self-explanations on acquisition of conceptual knowledge and problem-solving performance in an undergraduate engineering course. (3442880 Ph.D.), The Pennsylvania State University, Ann Arbor.

  62. *Kastens, K. A., & Liben, L. S. (2007). Eliciting self-explanations improves children's performance on a field-based map skills task. Cognition and Instruction, 25(1), 45–74.

  63. Kopp, C. B. (1982). Antecedents of self-regulation: a developmental perspective. Developmental Psychology, 18(2), 199–214.

    Article  Google Scholar 

  64. *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.

  65. *Kwon, K., Kumalasari, C. D., & Howland, J. L. (2011). Self-explanation prompts on problem-solving performance in an interactive learning environment. Journal of Interactive Online Learning, 10(2), 96–112.

  66. *Lee, A. Y., & Hutchison, L. (1998). Improving learning from examples through reflection. Journal of Experimental Psychology: Applied, 4(3), 187–210.

  67. Lin, L., & Atkinson, R. K. (2013). Enhancing learning from different visualizations by self-explanation prompts. Journal of Educational Computing Research, 49(1), 83–110.

    Article  Google Scholar 

  68. Lindberg, D., Popowich, F., Nesbit, J., & Winne, P. (2013). Generating natural language questions to support learning on-line. In Proceedings of the 14th European Workshop on Natural Language Generation (pp. 105-114). Sofia, Bulgaria.

  69. Lipsey, M., & Wilson, D. (2001). Practical meta-analysis (Vol. 49). Thousand Oaks: Sage publications.

    Google Scholar 

  70. Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: a meta-analysis. Journal of Educational Psychology, 106(4), 901–918.

    Article  Google Scholar 

  71. Marambe, K. N., Vermunt, J. D., & Boshuizen, H. P. (2012). A cross-cultural comparison of student learning patterns in higher education. Higher Education, 64(3), 299–316.

    Article  Google Scholar 

  72. *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.

  73. *Mayer, R. E., Dow, G. T., & Mayer, S. (2003). Multimedia learning in an interactive self-explaining environment: what works in the design of agent-based microworlds? Journal of Educational Psychology, 95(4), 806–812.

  74. 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.

    Article  Google Scholar 

  75. *McNamara, D. S. (2004). SERT: self-explanation reading training. Discourse Processes, 38(1), 1–30.

  76. *Molesworth, B. R. C., Bennett, L., & Kehoe, E. J. (2011). Promoting learning, memory, and transfer in a time-constrained, high hazard environment. Accident Analysis & Prevention, 43(3), 932–938.

  77. *Moreno, K. K., Bhattacharjee, S., & Brandon, D. M. (2007). The effectiveness of alternative training techniques on analytical procedures performance. Contemporary Accounting Research, 24(3), 983–1014.

  78. *Nathan, M.J. Mertz, K., & Ryan, B. (1993). Learning through self-explanation of mathematical examples: Effects of cognitive load. Paper presented at the 1994 Annual Meeting of the American Educational Research Association.

  79. Nesbit, J. C., & Winne, P. H. (2003). Self-regulated inquiry with networked resources. Canadian Journal of Learning and Technology, 29, 71–92.

    Google Scholar 

  80. Odilinye, L., Popowich, F., Zhang, E., Nesbit, J., & Winne, P. H. (2015). Aligning automatically generated questions to instructor goals and learner behaviour. Paper presented at the Semantic Computing (ICSC), 2015 I.E. International Conference on Semantic Computing.

  81. *O'Reilly, T., Symons, S., & MacLatchy-Gaudet, H. (1998). A comparison of self-explanation and elaborative interrogation. Contemporary Educational Psychology, 23(4), 434–445.

  82. Orwin, R. G. (1983). A fail-safe N for effect size. Journal of Educational Statistics, 8, 147–159.

    Google Scholar 

  83. *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, 163(2), 133–148.

  84. *Pine, K. J., & Messer, D. J. (2000). The effect of explaining another's actions on children's implicit theories of balance. Cognition and Instruction, 18(1), 35–51.

  85. Raffaelli, M., Crockett, L. J., & Shen, Y. L. (2005). Developmental stability and change in self-regulation from childhood to adolescence. The Journal of Genetic Psychology, 166(1), 54–76.

    Article  Google Scholar 

  86. Renkl, A. (1997). Learning from worked-out examples: a study on individual differences. Cognitive Science, 21(1), 1–29.

    Article  Google Scholar 

  87. Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12(5), 529–556.

    Article  Google Scholar 

  88. *Rittle-Johnson, B. (2004). Promoting flexible problem solving: the effects of direct instruction and self-explaining. Proceedings of the Cognitive Science Society, 26(26).

  89. *Rittle-Johnson, B. (2006). Promoting transfer: effects of self-explanation and direct instruction. Child Development, 77(1), 1–15.

  90. Rittle-Johnson, B., & Loehr, A. (2017). Eliciting explanations: constraints on when self-explanation aids learning. Psychonomic Bulletin & Review, 24(5), 1501–1510.

    Article  Google Scholar 

  91. Rittle-Johnson, B., Loehr, A., & Durkin, M. (2017). Promoting self-explanation to improve mathematics learning: a meta-analysis and instructional design principles. ZDM, 49(4), 599–611.

    Article  Google Scholar 

  92. Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118(2), 183–192.

    Article  Google Scholar 

  93. Schneider, W. (2008). The development of metacognitive knowledge in children and adolescents: major trends and implications for education. Mind, Brain, and Education, 2(3), 114–121.

    Article  Google Scholar 

  94. Schroeder, N. L., & Gotch, C. M. (2015). Persisting issues in pedagogical agent research. Journal of Educational Computing Research, 53(2), 183–204

    Article  Google Scholar 

  95. *Schwonke, R., Ertelt, A., Otieno, C., Renkl, A., Aleven, V., & Salden, R. J. C. M. (2013). Metacognitive support promotes an effective use of instructional resources in intelligent tutoring. Learning and Instruction, 23, 136–150.

  96. *Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: when instructional explanations reduce self-explanations. Computers and Education, 46(4), 426–445.

  97. *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.

  98. Siegler, R. (1995). How does change occur: a microgenetic study of number conservation. Cognitive Psychology, 25, 225–273.

    Article  Google Scholar 

  99. Siegler, R. (2002). Microgenetic studies of self-explanation. In N. Garnott & J. Parziale (Eds.), Microdevelopment: Transition processes in development and learning (pp. 31–58). Cambridge: Cambridge University Press.

    Google Scholar 

  100. *Siegler, R., & Chen, Z. (2008). Differentiation and integration: guiding principles for analyzing cognitive change. Developmental Science, 11(4), 433–448.

  101. Smith, S. W., Daunic, A. P., & Taylor, G. G. (2007). Treatment fidelity in applied educational research: expanding the adoption and application of measures to ensure evidence-based practice. Education and Treatment of Children, 30(4), 121–134.

    Article  Google Scholar 

  102. *Tajika, H., Nakatsu, N., Nozaki, H., Neumann, E., & Marumo, S. (2007). Effects of self-explanation as a metacognitive strategy for solving mathematical word programs. Japanese Psychological Research, 49(3), 222–233.

  103. *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.

  104. *Vandervelde, C. K. (2009). The importance of causal antecedent emotional state inferences to narrative reading comprehension. (3374081 Ph.D.), The University of Iowa, Ann Arbor.

  105. VanLehn, K., Jones, R. M., & Chi, M. T. (1992). A model of the self-explanation effect. The Journal of the Learning Sciences, 2(1), 1–59.

    Article  Google Scholar 

  106. Vermunt, J. D. (1996). Metacognitive, cognitive and affective aspects of learning styles and strategies: a phenomenographic analysis. Higher Education, 31(1), 25–50.

    Article  Google Scholar 

  107. Vermunt, J. D., & Donche, V. (2017). A learning patterns perspective on student learning in higher education: state of the art and moving forward. Educational Psychology Review, 29(2), 269–299.

    Article  Google Scholar 

  108. *Weerasinge, A., & Mitrovic, A. (2005). Supporting deep learning in an open-ended domain. In M. Gh. Negoita & B. Reusch (Eds.), Real World Applications of Computational Intelligence, studies in fuzziness and soft computing (Vol. 179, pp. 105–152). Berlin: Springer.

  109. Weil, L. G., Fleming, S. M., Dumontheil, I., Kilford, E. J., Weil, R. S., Rees, G., Dolan, R., & Blakemore, S. J. (2013). The development of metacognitive ability in adolescence. Consciousness and Cognition, 22(1), 264–271.

    Article  Google Scholar 

  110. *Wichmann, A. (2010). Multi-level support with respect to inquiry, explanations and regulation during an inquiry cycle. Dissertation. Universität Duisburg-Essen, Fachbereich Bildungswissenschaften, Essen.

  111. *Wichmann, A., & Leutner, D. (2009). Inquiry learning multilevel support with respect to inquiry, explanations and regulation during an inquiry cycle. Zeitschrift Fur Padagogische Psychologie, 23(2), 117–127.

  112. *Williams, J. J., & Lombrozo, T. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology, 66(1), 55–84.

  113. 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.

    Article  Google Scholar 

  114. Wong, R. M. F., Lawson, M. J., & Keeves, J. (2002). The effects of self-explanation training on students' problem solving in high-school mathematics. Learning and Instruction, 12(2), 233–262.

    Article  Google Scholar 

  115. *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 (AIED’11) (pp. 588–590). Berlin: Springer.

  116. *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.

  117. *Yerushalmi, E., Cohen, E., Mason, A., & Singh, C. (2012). What do students do when asked to diagnose their mistakes? Does it help them? I. An atypical quiz context. Physical Review Special Topics-Physics Education Research, 8(2), 020109–1–020109-19.

  118. *Yuasa, M. (1993). The effects of active learning exercises on the acquisition of SQL query writing procedures (unpublished doctoral dissertation). Georgia Institute of Technology, USA.

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This study was funded by Social Sciences and Humanities Research Council of Canada (grant number 435–2012-0723).

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Bisra, K., Liu, Q., Nesbit, J.C. et al. Inducing Self-Explanation: a Meta-Analysis. Educ Psychol Rev 30, 703–725 (2018).

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  • Self-explanation
  • Instructional explanation
  • Meta-analysis
  • Prompts