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This paper examines procedural and conditional metacognitive knowledge and student motivation across two ITSs (logic and probability). Students were categorized by metacognitive knowledge and motivation level. Interventions (nudges and worked examples) supported backward-chaining strategy. Results led to an MMI framework combining metacognitive instruction, motivation, and prompting to support effective knowledge transfer.
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Evaluates DKT models’ ability to track individual knowledge components (KCs) in programming tasks. Proposes two enhancements—adding an explicit KC layer and code features—and shows that the KC layer yields modest improvements in KC-level interpretability, especially when tracking incorrect submissions.more » « less
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Proposes a DRL-based pedagogical policy to choose when to present or skip training problems in a logic tutor. Four conditions are compared: control, adaptive DRL, random skipping, and DRL with worked-example choice. DRL policy reduces training time while maintaining posttest performance.more » « less
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Learning to derive subgoals reduces the gap between experts and students and prepares students for future problem solving. This paper explores a training strategy using backward worked examples (BWE) and backward problem solving (BPS) within an intelligent logic tutor to support backward strategy learning, with analysis of student experience, performance, and proof construction. Results show that students trained with both BWE and BPS outperform those receiving none or only BWE, demonstrating more efficient subgoal derivation.more » « less
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We explore eXplainable AI (XAI) to enhance user experience and understand the value of explanations in AI-driven pedagogical decisions within an Intelligent Pedagogical Agent (IPA). Our real-time and personalized explanations cater to students' attitudes to promote learning. In our empirical study, we evaluate the effectiveness of personalized explanations by comparing three versions of the IPA: (1) personalized explanations and suggestions, (2) suggestions but no explanations, and (3) no suggestions. Our results show the IPA with personalized explanations significantly improves students' learning outcomes compared to the other versions.more » « less
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Deep Reinforcement Learning (Deep RL) has revolutionized the field of Intelligent Tutoring Systems by providing effective pedagogical policies. However, the ``black box'' nature of Deep RL models makes it challenging to understand these policies. This study tackles this challenge by applying fuzzy logic to distill knowledge from Deep RL-induced policies into interpretable IF-THEN Fuzzy Logic Controller (FLC) rules. Our experiments show that these FLC policies significantly outperform expert policy and student decisions, demonstrating the effectiveness of our approach. We propose a Temporal Granule Pattern (TGP) mining algorithm to increase the FLC rules' interpretability further. This work highlights the potential of fuzzy logic and TGP analysis to enhance understanding of Deep RL-induced pedagogical policies.more » « less
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