Toward Computer-Based Support of Meta-Cognitive Skills: a Computational Framework to Coach Self-Explanation

We present a computational framework designed to improve learning from examples by supporting self-explanation o the process of clarifying and making more complete to oneself the solution of an example. The framework is innovative in two ways. First, it represents the first attempt to provide computer support to example studying instead of problem solving. Second, it explicitly coaches a domain-general, meta-cognitive skill that many studies in cognitive science have shown to greatly improve learning. The framework includes solutions to three main problems: (1) to design an interface that effectively monitors and supports self-explanation; (2) to devise a student model that allows the assessment of example understanding from reading and self-explanation actions; (3) to effectively elicit further self-explanation that improves studentâs example understanding. In this paper, we describe how these solutions have been implemented in a computer tutor that coaches self-explanation within Andes, a tutoring system for Newtonian physics. We also present the results of a formal study to evaluate the usability and effectiveness of the system. Finally, we discuss some hypotheses to explain the obtained results, based on the analysis of the data collected during the study. (http://aied.inf.ed.ac.uk/members00/archive/vol_11/conati/full.html)