Abstract
Learning with multiple representations is usually employed in order to foster understanding. However, it also imposes high demands on the learners and often does not lead to the expected results, especially because the learners do not integrate the different representations. Thus, it is necessary to support the learners’ self-explanation activity, which concerns the integration and understanding of multiple representations. In the present experiment, we employed multi-representational worked-out examples and tested the effects of two types of self-explanation prompts as help procedures for integrating and understanding multiple representations. The participants (N = 62) learned about probability theory under three conditions: (a) open self-explanation prompts, (b) self-explanation prompts in an assistance-giving-assistance-withholding procedure (assisting self-explanation prompts), or (c) no prompts (control group). Both types of self-explanation prompts fostered procedural knowledge. This effect was mediated by self-explanations directed to domain principles. Conceptual knowledge was particularly fostered by assisting self-explanation prompts which was mediated by self-explanations on the rationale of a principle. Thus, for enhancing high-quality self-explanations and both procedural knowledge and conceptual understanding, we conclude that assisting self-explanation prompts should be provided. We call this the assisting self-explanation prompt effect which refers to the elicitation of high-quality self-explanations and the acquisition of deep understanding.
This is a preview of subscription content, access via your institution.
References
Ainsworth, S. E. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183–198.
Ainsworth, S. E., Bibby, P. A., & Wood, D. J. (1998). Analysing the costs and benefits of multi-representational learning environments. In H. Spada, P. Reimann, E. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 120–134). Oxford: Elsevier Science.
Ainsworth, S. E., Bibby, P. A., & Wood, D. J. (2002). Examining the effects of different multiple representational systems in learning primary mathematics. The Journal of the Learning Sciences, 11, 25–61.
Ainsworth, S. E., & Loizou, A. (2003). The effects of self-explaining when learning with text or diagrams. Cognitive Science, 27, 669–681.
Aleven, V., & Koedinger, K. R. (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. (2001). Towards tutorial dialog to support self-explanation: 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, Proceedings of AI-ED 2001 (pp. 246–255). Amsterdam: IOS Press.
Anderson, J. R., Reder, L. M., & Simon, H. A. (1998). Radical constructivism and cognitive psychology. In D. Ravitch (Ed.), Brooking papers on education policy 1998 (pp. 227–278). Washington, DC: Brookings Institute Press.
Atkinson, R. K., & Catrambone, R. (2000). Subgoal learning and the effect of conceptual vs. computational equations on transfer. In L. R. Gleitman & A. K. Joshi (Eds.), Proceedings of the 22nd Annual Conference of the Cognitive Science Society (pp. 591–596). Mahwah, NJ: Erlbaum.
Atkinson, R. K., Catrambone, R., & Merrill, M. M. (2003a). Aiding transfer in statistics. Examining the use of conceptually oriented equations and elaborations during subgoal learning. Journal of Educational Psychology, 95, 762–773.
Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003b). Transitioning from studying examples to solving problems: Combining fading with prompting fosters learning. Journal of Educational Psychology, 95, 774–783.
Ayres, P., & Sweller, J. (2005). The split-attention principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 135–146). Cambridge, UK: Cambridge University Press.
Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.
Bielaczyc, K., Pirolli, P., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem-solving. Cognition and Instruction, 13, 221–252.
Butcher, K. R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98, 182–197.
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 (pp. 161–238). Mahwah, NJ: 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, 145–182.
Chi, M. T. H., de Leeuw, N., Chiu, M. H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439–477.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates.
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, 11, 398–415.
de Jong, T., Ainsworth, S., Dobson, M., van der Hulst, A., Levonen, J., Reimann, P., Sime, J.-A., van Someren, M. W., Spada, H., & Swaak, J. (1998). Acquiring knowledge in science, mathematics: The use of multiple representations in technology-based learning environments. In M. van Someren, P. Reimann, H. P. A. Boshuizen, & T. de Jong (Eds.), Learning with multiple representations (pp. 9–40). Amsterdam: Pergamon.
de Jong, T., & Ferguson-Hessler, M. G. M. (1996). Types and qualities of knowledge. Educational Psychologist, 31, 105–113.
Gerjets, P., Scheiter, K., & Catrambone, R. (2003). Reducing cognitive load and fostering cognitive skill acquisition: Benefits of category-avoiding examples. In F. Schmalhofer, R. Young, & G. Katz (Eds.), Proceedings of the European Cognitive Science Conference (pp. 133–139). Mahwah, NJ: Erlbaum.
Hilbert, T. S., Schworm, S., & Renkl, A. (2004). Learning from worked-out examples: The transition from instructional explanations to self-explanation prompts. In P. Gerjets, J. Elen, R. Joiner, & P. Kirschner (Eds.), Instructional design for effective and enjoyable computer-supported learning (pp. 184–192). Tübingen: Knowledge Media Research Center.
Jeung, H., Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode instruction. Educational Psychology, 17, 329–343.
Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13, 351–371.
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experimental, and inquiry-based teaching. Educational Psychologist, 42(2), 75–86.
Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: Effects of direct instruction and discovery learning. Psychological Science, 15, 661–667.
Koedinger, K. R., & Aleven V. (2007). Exploring the assistance dilemma in experiments with Cognitive Tutors. Educational Psychology Review, 19(3), 239–264.
MacKinnon, D. P. (2002). Mediating variable. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopaedia of the social and behavioral sciences (pp. 9503–9507). New York: Elsevier.
MacKinnon, D. P., & Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144–158.
Mayer, R. E. (2005). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 31–48). Cambridge, UK: Cambridge University Press.
Moreno, R., & Mayer, R. E. (1999). Multimedia-supported metaphors for meaning making in mathematics. Cognition and Instruction, 17, 215–248.
Pressley, M., Wood, E., Woloshyn, V. E., Martin, V., King, A., & Menke, D. (1992). Encouraging mindful use of prior knowledge: Attempting to construct explanatory answers facilitates learning. Educational Psychologist, 27, 91–109.
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.
Renkl, A. (2002). Learning from worked-out examples: Instructional explanations support self-explanations. Learning and Instruction, 12, 529–556.
Renkl, A. (2005). The worked-out example principle in multimedia learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 229–247). Cambridge, UK: Cambridge University Press.
Rosenthal, R., & Rosnow, R. L. (1985). Contrast analysis: Focused comparisons in the analysis of variance. Cambridge, UK: Cambridge University Press.
Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000). Contrast and effect sizes in behavioural research. A correlational approach. Cambridge, UK: Cambridge University Press.
Roy, M., & Chi, M. T. H. (2005). The self-explanation principle in multi-media learning. In R. E. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 271–287). Cambridge, UK: Cambridge University Press.
Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers and Education, 46, 426–445.
Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99, 285–296.
Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13, 227–237.
Seufert, T., & Brünken, R. (2004). Supporting coherence formation in multimedia learning. In P. Gerjets, J. Elen, R. Joiner, & P. Kirschner (Eds.), Instructional design for effective and enjoyable computer-supported learning (pp. 138–147). Tübingen: Knowledge Media Research Center.
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). New York: Cambridge University Press.
Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. In S. Leinhardt (Ed), Sociological methodology (pp. 290–312). Washington, DC: American Sociological Association.
Spiro, R. J., & Jehng, J.-C. (1990). Cognitive flexibility and hypertext: Theory and technology for nonlinear and multi-dimensional traversal of complex subject matter. In D. Nix & R. J. Spiro (Eds.), Cognition, education, and multi-media: Exploring ideas in high technology. Hillsdale, NJ: LEA.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds. and Trans.). Cambridge, MA: Harvard University Press.
Acknowledgments
The research reported in this article was funded by the “Deutsche Forschungsgemeinschaft” (DFG 1040/11-1). We would like to thank the members of the Dutch-German LEMMA cooperation project (project leaders: Peter Gerjets, Ton de Jong, Jeroen van Merriënboer, and Fred Paas) for jointly constructing the tasks of the learning environment as well as Julian Kappich, Norman Marko, Heidi Roeder, Tim Rohe, and Stephan Rueckert for their assistance in programming, conducting the experiment, coding the self-explanations, and analyzing the tests.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Berthold, K., Eysink, T.H.S. & Renkl, A. Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instr Sci 37, 345–363 (2009). https://doi.org/10.1007/s11251-008-9051-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11251-008-9051-z