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We present the formal evaluation of a framework that helps students learn from analogical problem solving, i.e., from problem-solving activities that involve worked-out examples. The framework incorporates an innovative example-selection mechanism, which tailors the choice of example to a given student so as to trigger studying behaviors that are known to foster learning. This involves a two-phase process based on 1) a probabilistic user model and 2) a decision-theoretic mechanism that selectsdblp:conf/ijcai/MuldnerC07 fatcat:kl4ln66skng6llfw5vause2lka