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Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies.

, , , , and . UMAP, page 284-292. ACM, (2020)

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Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction., , , , and . Int. J. Artif. Intell. Educ., 32 (2): 454-500 (2022)Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning., , , , and . CoRR, (2023)Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards., , and . EDM, International Educational Data Mining Society, (2020)Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning., , , , and . EDM, International Educational Data Mining Society (IEDMS), (2019)Big, Little, or Both? Exploring the Impact of Granularity on Learning for Students with Different Incoming Competence., , and . CogSci, page 3206-3212. cognitivesciencesociety.org, (2019)Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor., , , , and . AIED, volume 9112 of Lecture Notes in Computer Science, page 726-729. Springer, (2015)Evaluating Critical Reinforcement Learning Framework in the Field., , , , and . AIED (1), volume 12748 of Lecture Notes in Computer Science, page 215-227. Springer, (2021)Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract)., , , , and . IJCAI, page 4691-4695. ijcai.org, (2020)Sister Conferences Best Papers.Going Deep and Far: Gaze-based Models Predict Multiple Depths of Comprehension During and One Week Following Reading., , , , and . EDM, International Educational Data Mining Society, (2022)Student Subtyping via EM-Inverse Reinforcement Learning., , , , and . EDM, International Educational Data Mining Society, (2020)