The Effect of Self-Explaining on Robust Learning

Article type: Research Article

Authors: Hausmann, Robert G. M. | VanLehn, Kurt

Affiliations: Carnegie Learning, Inc., Pittsburgh, PA, USA. bhausmann@carnegielearning.com | School of Computing and Informatics, Arizona State University, Tempe, AZ, USA. kurt.vanlehn@asu.edu

Abstract: Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional content that does not exist in the instructional materials. Second, when compared to comprehension, generation of content increases understanding and recall. An in vivo experiment was designed to distinguish between these potentially orthogonal hypotheses. Students were instructed to use one of two learning strategies, self-explaining and paraphrasing, to study either a completely justified example or an incomplete example. Learning was assessed at multiple time points and levels of granularity. The results were consistent, favoring the generation account of self-explanation. This suggests that examples should be designed to encourage the active generation of missing content information.

Keywords: Self-explanation, physics, worked-out examples, problem solving

DOI: 10.3233/JAI-2010-010

Journal: International Journal of Artificial Intelligence in Education, vol. 20, no. 4, pp. 303-332, 2010