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What do Students' Interactions with Online Lecture Videos Reveal about their Learning?

, , и . Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, стр. 295-305. ACM, (2022)User tracing provide evidence of learning, but scalar parameters are better than sequences.
DOI: 10.1145/3503252.3531315

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Zhou Zhou

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Другие публикации лиц с тем же именем

Leveraging Granularity: Hierarchical Reinforcement Learning for Pedagogical Policy Induction., , , , и . Int. J. Artif. Intell. Educ., 32 (2): 454-500 (2022)Pick the Moment: Identifying Critical Pedagogical Decisions Using Long-Short Term Rewards., , и . EDM, International Educational Data Mining Society, (2020)Identifying Critical Pedagogical Decisions through Adversarial Deep Reinforcement Learning., , , , и . 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., , и . CogSci, стр. 3206-3212. cognitivesciencesociety.org, (2019)Data-Driven Worked Examples Improve Retention and Completion in a Logic Tutor., , , , и . AIED, том 9112 из Lecture Notes in Computer Science, стр. 726-729. Springer, (2015)Evaluating Critical Reinforcement Learning Framework in the Field., , , , и . AIED (1), том 12748 из Lecture Notes in Computer Science, стр. 215-227. Springer, (2021)What do Students' Interactions with Online Lecture Videos Reveal about their Learning?, , и . UMAP, стр. 295-305. ACM, (2022)Improving Student-System Interaction Through Data-driven Explanations of Hierarchical Reinforcement Learning Induced Pedagogical Policies., , , , и . UMAP, стр. 284-292. ACM, (2020)The Impact of Granularity on the Effectiveness of Students' Pedagogical Decisions., , , , и . CogSci, cognitivesciencesociety.org, (2016)Hierarchical Reinforcement Learning for Pedagogical Policy Induction (Extended Abstract)., , , , и . IJCAI, стр. 4691-4695. ijcai.org, (2020)Sister Conferences Best Papers.