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Algorithmes d’espacement adaptatif de l’apprentissage pour l’optimisation de la maîtrise à long terme de composantes de connaissance

Abstract : Between acquiring new knowledge and reviewing old knowledge to mitigate forgetting, learners may find it difficult to organize their learning time effectively. Adaptive spacing algorithms, like SuperMemo, can help learners deal with this trade-off. Such algorithms sequentially plan reviews of a given piece of knowledge to adapt to the specific and ongoing needs of each learner. Compared to a fixed and identical temporal spacing between reviews, several experiments have shown that adaptive spacing improves long-term memory retention of the piece of knowledge.To date, research on adaptive spacing algorithms has focused on the pure memorization of simple pieces of knowledge, which are often represented by flashcards. However, several studies in cognitive psychology have shown that the benefits of spacing out learning episodes on long-term retention also extend to more complex knowledge, such as learning concepts and procedures in mathematics. In this thesis, we have therefore sought to develop adaptive and personalized spacing algorithms for optimizing long-term mastery of knowledge components (KCs).First, we develop and present a new statistical model of learning and forgetting of knowledge components, coined DAS3H, and we empirically show that DAS3H has better predictive performance than several learner models in educational data mining. Second, we develop several adaptive spacing heuristics for long-term mastery of KCs and compare their performance on simulated data. Two of these heuristics use the DAS3H model to select which KC should be reviewed by a given learner at a given time. In addition, we propose a new greedy procedure to select the most promising subset of KCs instead of the best KC to review. Finally, in the last chapter of this thesis, we develop AC4S, a deep reinforcement learning algorithm for adaptive spacing for KCs. We compare this data-driven approach to the heuristic methods that we presented previously.
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Submitted on : Tuesday, May 4, 2021 - 11:24:08 AM
Last modification on : Tuesday, January 4, 2022 - 6:42:58 AM
Long-term archiving on: : Thursday, August 5, 2021 - 7:28:40 PM


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  • HAL Id : tel-03216648, version 1


Benoît Choffin. Algorithmes d’espacement adaptatif de l’apprentissage pour l’optimisation de la maîtrise à long terme de composantes de connaissance. Apprentissage [cs.LG]. Université Paris-Saclay, 2021. Français. ⟨NNT : 2021UPASG001⟩. ⟨tel-03216648⟩



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