Explanation-based Mechanisms for Learning: An Interdisciplinary Approach

Explanation-based Mechanisms for Learning: An Interdisciplinary Approach Michelene Chi (mtchi@asu.edu) Gerald DeJong (mrebl@illinois.edu) Department of Psychology, Arizona State University Department of Computer Science, University of Illinois at Urbana-Champaign Cristine Legare (legare@psy.utexas.edu) Tania Lombrozo* (lombrozo@berkeley.edu) Joseph Jay Williams* (joseph_williams@berkeley.edu) Department of Psychology, University of Texas At Austin Department of Psychology, University of California at Berkeley Abstract The significant role of explanation in learning and generalization is ubiquitous and well documented: explanation promotes student learning in educational settings, drives conceptual development in young children, is accorded a central role in theories of conceptual representation, and has a long history in artificial intelligence. Despite this, relatively little is known about the precise mechanisms that underlie explanation’s effects, and there is a paucity of discourse between the disciplines of cognitive science that study explanation. This interdisciplinary symposium brings together key researchers from education, development, cognitive psychology and computer science to synthesize the progress from these disciplines, forging connections between ongoing research programs to identify promising future directions. Keywords: explanation, self-explanation, understanding, learning, transfer, generalization, explanation-based learning Investigating the processes that foster learning and generalization is one of the core questions in cognitive science: how does a child, adult, or computer acquire knowledge and understanding that is readily generalized to novel situations, rather than lying inert? Explanation has been repeatedly shown to play a key role across a range of core cognitive science disciplines, including education, cognitive development, cognitive psychology, and artificial intelligence. In real-world educational contexts, researchers have documented a self-explanation effect: whether studying math, physics or biology, learners who generate explanations for what they are learning (spontaneously or in response to prompts) are more likely to acquire accurate concepts and transfer their knowledge to novel problems and situations (Chi et al, 1994). Developmentally, generating explanations for the behavior or reasoning of others has been shown to foster conceptual change in young children’s understanding of theory of mind and number conservation (Wellman & Liu, 2006; Siegler, 2002). Research in cognitive psychology also invokes explanation as a powerful force in conceptual representation and revision, even when explanations are not explicitly solicited. In particular, theories of conceptual representation accord a central role to explanation in understanding how concepts and causal relationships between concepts are represented, especially in knowledge-rich domains (e.g. Murphy & Medin, 1985; Lombrozo, 2009). Understanding how explaining promotes learning can thus shed light on concept acquisition and causal learning, and in particular the role of prior knowledge in these processes. Finally, work on explanation-based learning in artificial intelligence and machine learning has isolated different aspects of explanation and formally characterized how these can support generalization (DeJong & Mooney, 1986; Mitchell et al, 1986). However, the developments in these formalisms have not been fully exploited to interpret current educational and psychological work on explanation and learning, nor been directed at understanding current findings and issues in these fields (for a rare exception, see Ahn, Brewer & Mooney, 1992). Despite extensive documentation of the powerful effects of explanation in education and development and the relevance of explanation and learning to current research, little is known about why and how explaining exerts its effects (Lombrozo, 2006). This symposium provides a timely forum for addressing this gap in current theories by bringing together researchers to synthesize empirical findings in education and development, theories and models from artificial intelligence, and investigation using the experimental methodology of cognitive psychology. Michelene Chi synthesizes research in education to give insight into the nature of self-explanation by contrasting it with other learning activities. Cristine Legare provides a developmental perspective on how explanations selectively guide children’s exploration and learning, particularly of causal mechanisms. Joseph Williams and Tania Lombrozo provide a novel account of explanation’s role in generalization in terms of pattern discovery, drawing on philosophical theories and methodology in cognitive psychology. Gerald DeJong presents current machine learning research on how explanations can integrate statistical and logical inference. The constructive nature of self-explanation Michelene Chi conducted the pioneering work on explanation in education with seminal papers on the self- explanation effect. Her talk reviews and synthesizes research in education to shed light on why self-explanation is beneficial, through a comparison to other active, constructive, and interactive learning activities (Chi, 2009). The constructive nature of explanation is proposed as an * Denotes organizers.