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Learning Path Planning Algorithm Based on Learner Behavior Analysis

Published: 29 June 2021 Publication History

Abstract

Internet online education platforms are developing at an unprecedented speed. Ordinary people can also have zero-distance access to excellent courses from excellent universities that were inaccessible in the past. However, learners are unable to quickly find content that suits their learning interests and abilities in the massive learning materials, resulting in greatly reduced learning effects. This article conducts research on how to fill the knowledge gap between learners and educational resources, and aims to plan individualized and highly compatible learning paths for learners. Combining the prediction results of the exercises and answering questions, the knowledge points and courses are modeled. Based on the established model, a learning path planning algorithm with review strategies based on the knowledge graph is proposed, and the topological sorting is used to show the completeness for the learners at the end. Personalized learning path. The experimental results show that the method has satisfactory accuracy. It can provide learners with a personalized learning path.

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Cited By

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  • (2024)CeKT: Knowledge Tracing for Predicting Collective Performance on Exercise SequenceIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340604811:6(7244-7256)Online publication date: Dec-2024
  • (2022)Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted InstructionsApplied Sciences10.3390/app1218937612:18(9376)Online publication date: 19-Sep-2022
  • (2022)Personalized Learning Path Recommendation for E-Learning Based on Knowledge Graph and Graph Convolutional NetworkInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402250068133:01(109-131)Online publication date: 22-Dec-2022
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      cover image ACM Other conferences
      ICBDE '21: Proceedings of the 2021 4th International Conference on Big Data and Education
      February 2021
      130 pages
      ISBN:9781450389389
      DOI:10.1145/3451400
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      New York, NY, United States

      Publication History

      Published: 29 June 2021

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      Author Tags

      1. Learning path
      2. Smart education
      3. Time series

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      Cited By

      View all
      • (2024)CeKT: Knowledge Tracing for Predicting Collective Performance on Exercise SequenceIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.340604811:6(7244-7256)Online publication date: Dec-2024
      • (2022)Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted InstructionsApplied Sciences10.3390/app1218937612:18(9376)Online publication date: 19-Sep-2022
      • (2022)Personalized Learning Path Recommendation for E-Learning Based on Knowledge Graph and Graph Convolutional NetworkInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402250068133:01(109-131)Online publication date: 22-Dec-2022
      • (2022)A Systematic Literature Review on Personalised Learning in the Higher Education ContextTechnology, Knowledge and Learning10.1007/s10758-022-09628-428:2(449-476)Online publication date: 17-Nov-2022

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