An Investigation of Accuracy of Metacognitive Judgments during Learning with an Intelligent Multi-Agent Hypermedia Environment


Research suggests that students are rather dysregulated in their learning. One major source of dysregulation is the inaccurate metacognitive judgments made during learning. This study investigated learners’ accuracy and confidence in metacognitive judgments made in the context of learning about the human circulatory system with MetaTutor, a multi-agent intelligent hypermedia learning system. 83 participants took part in this study, and their interactions within MetaTutor in the two-hour learning session provided data for this study. In general, the results revealed that learners were overconfident to differing degrees in their JOLs and FOKs. Moreover, receiving timely prompts and feedback from the artificial agent in MetaTutor improved the accuracy of metacognitive judgments. Learners in Prompt and Feedback condition (PF) were less overconfident than those in other conditions (Prompt Only [PO] and Control). Finally, analyses indicated that learners in PF condition attained significantly better learning efficiency scores than learners in Control and PO conditions.

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