Abstract
AbstractOftentimes learners are unable to engage in effective self-regulated learning (SRL) strategies while learning about complex topics. To combat this, intelligent tutoring systems (ITSs) incorporate pedagogical agents to guide learners in understanding how to engage in several SRL strategies effectively and efficiently throughout learning. To identify how ITSs can best support learners’ SRL strategy usage, data from 105 undergraduate students across several North American public universities were collected as they learned with MetaTutor, a hypermedia-based ITS about the human circulatory system. Participants were randomly assigned to two conditions – a prompt and feedback condition in which pedagogical agents prompted learners to engage in specific cognitive and metacognitive SRL strategies and provided feedback to performance in addition to learners’ self-initiated SRL strategy usage, and the control condition in which learners were not prompted nor were provided feedback on their performance of self-initiated SRL strategies. Results found that learners receiving external support from pedagogical agents had greater learning gains and deployed a greater number of both cognitive and metacognitive SRL strategies than learners who only self-initiated strategies. While probabilities obtained from a Markov model did not find differences between conditions in learners’ sequential transitions between SRL strategies, metrics from auto-recurrence quantification analysis found that learners receiving external support enacted less repetitive interactions of SRL strategies throughout their entire time interacting with MetaTutor. Implications of these results encourage the use of pedagogical agents in prompting more novel SRL strategies to increase learning within ITSs.KeywordsSelf-regulated learningPedagogical agentsIntelligent tutoring systemsAuto-recurrence quantification analysis
Published Version
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