## Key Ideas > [!abstract] Core Concepts > > - **Explain the why, not the what**: Focused prompts about principles and reasoning are more effective than open descriptions (Chi et al., 1994; Renkl, 1997) > - **Germane cognitive load**: Self-explanation increases productive mental effort for schema construction and knowledge integration (Sweller et al., 2019) > - **Prerequisite knowledge required**: Students need sufficient background knowledge to generate meaningful explanations (Renkl, 1997) ## Definition **Self-Explanation Effect**: Students learn more from [[Worked Examples]] when they explain to themselves what is happening at key decision points, especially with principle-based prompts (Chi et al., 1989; Renkl, 1997). ## Connected To [[Worked Examples]] | [[Cognitive Load Theory]] | [[Schema]] | [[Expertise Reversal Effect]] | [[Scaffolding]] | [[Prior Knowledge]] --- ## Research evidence Research reveals both the power and limitations of self-explanation (Bisra et al., 2018). Meta-analytic evidence shows moderate effects for self-explanation prompts in mathematics (Rittle-Johnson, 2006). The approach supports conceptual understanding development by connecting procedures to principles (Chi et al., 1989), enables transfer to novel problems by helping students apply knowledge in new contexts (Renkl et al., 1998), and reduces errors through identifying flawed reasoning (Siegler, 2002). Several boundary conditions constrain effectiveness. Self-explanation is most effective for novice learners, though the expertise reversal effect applies as students develop (Kalyuga et al., 2003). Students require adequate prior knowledge as a foundation; they cannot explain what they do not understand (Renkl, 1997). Benefits diminish without proper scaffolding and training in explanation techniques (Berthold et al., 2009). Evidence for long-term retention and classroom implementation remains limited, as most research occurs in laboratory settings (Bisra et al., 2018). These findings suggest self-explanation works best as one tool among many, not a universal solution. ## Cognitive mechanisms Self-explanation works by increasing [[Cognitive Load|germane cognitive load]], the productive mental effort that builds understanding (Sweller et al., 2019). The approach promotes schema construction and knowledge integration by forcing active processing (Chi et al., 1989), connects new information to prior knowledge through explicit retrieval and comparison (Renkl, 1997), develops metacognitive awareness and self-monitoring of understanding (Chi, 2000), and builds transferable problem-solving strategies that extend beyond specific examples (Renkl et al., 1998). This cognitive effort is productive only when learners have sufficient working memory capacity and foundational knowledge (Renkl, 1997). Without adequate prerequisites, self-explanation becomes unproductive struggle. ## Implementation in practice Students need sufficient background knowledge to generate meaningful explanations. Self-explanation helps connect theory to examples, not teach foundational concepts initially. Scaffolding should progress systematically: teachers model explanation processes first, then provide fill-in-the-blank explanation templates, followed by structured prompts with sentence stems, and gradually more open-ended questioning as students develop proficiency. Different prompt types serve distinct purposes. Principle-based prompts such as "Why were we able to use the distributive property here?" connect procedures to underlying concepts. Process-focused prompts like "What mathematical reasoning justifies this step?" develop logical thinking. Connection prompts such as "How does this relate to solving linear equations?" build schema connections. Error-anticipation prompts like "What would happen if we didn't find a common denominator?" prevent misconceptions. Two design principles distinguish effective from ineffective self-explanation prompts. Focused questions guide thinking without overwhelming, whilst open-ended questions often prove too vague. For example, "Explain why we multiply both sides by the same number" targets a specific principle, whilst "Explain what you did in this step" invites superficial description. Example-independent prompts transfer across contexts and build general understanding: "How did we know which operation to use first?", "What principle allows us to make this transformation?", "How do we verify our answer makes sense?", and "What would change if the given information was different?" focus on transferable reasoning rather than example-specific details. > [!tip] Implications for Teaching > > - Provide focused, principle-based prompts when students study worked examples > - Use incorrect worked examples with explanation prompts to address misconceptions > - Start with highly scaffolded prompts and gradually reduce support > - Ensure students have sufficient prior knowledge before implementing self-explanation ## References 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. https://doi.org/10.1007/s11251-008-9051-z Bisra, K., Liu, Q., Nesbit, J. C., Salimi, F., & Winne, P. H. (2018). Inducing self-explanation: A meta-analysis. *Educational Psychology Review*, 30(3), 703-725. https://doi.org/10.1007/s10648-018-9434-x 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). Lawrence Erlbaum Associates. 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. https://doi.org/10.1207/s15516709cog1302_1 Chi, M. T. H., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. *Cognitive Science*, 18(3), 439-477. https://doi.org/10.1207/s15516709cog1803_3 Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. *Educational Psychologist*, 38(1), 23-31. https://doi.org/10.1207/S15326985EP3801_4 Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. *Cognitive Science*, 21(1), 1-29. https://doi.org/10.1207/s15516709cog2101_1 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. https://doi.org/10.1006/ceps.1997.0959 Rittle-Johnson, B. (2006). Promoting transfer: Effects of self-explanation and direct instruction. *Child Development*, 77(1), 1-15. https://doi.org/10.1111/j.1467-8624.2006.00852.x Siegler, R. S. (2002). Microgenetic studies of self-explanation. In N. Granott & J. Parziale (Eds.), *Microdevelopment: Transition processes in development and learning* (pp. 31-58). Cambridge University Press. Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. *Educational Psychology Review*, 31(2), 261-292. https://doi.org/10.1007/s10648-019-09465-5