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在线教育学习者知识追踪综述

赵雅娟,孟繁军,徐行健   

  1. 内蒙古师范大学
  • 收稿日期:2023-07-01 修回日期:2023-10-26 发布日期:2023-11-07 出版日期:2023-11-07
  • 通讯作者: 孟繁军
  • 基金资助:
    国家自然科学基金资助项目;内蒙古自然科学基金资助项目;内蒙古师范大学基本科研业务费专项资金资助;内蒙古自治区军民融合重点科研项目及软科学研究项目;内蒙古自然科学基金资助项目

Review of online education learner knowledge tracing

  • Received:2023-07-01 Revised:2023-10-26 Online:2023-11-07 Published:2023-11-07
  • Supported by:
    National Natural Science Foundation of China;Natural Science Foundation of Inner Mongolia;Fundamental Research Funds for Inner Mongolia Normal University;Inner Mongolia JMRH Project;Natural Science Foundation of Inner Mongolia

摘要: 知识追踪是在线教育中一项基础且关键具有挑战性的任务,同时也是从学习者的学习历史中建立学习者知识状态模型的任务,可以帮助学习者更好地了解自己的知识状态,使教师能够更好地了解学习者的学习情况,针对相关领域的研究进展对在线教育学习者知识追踪研究进行综述。首先,介绍了知识追踪的主要任务及发展历程,其次,从传统知识追踪模型和深度学习知识追踪模型两个方面展开叙述,之后归纳总结了相关数据集以及评价指标并对知识追踪的相关应用进行了汇总,最后,对知识追踪做出了总结并对其未来发展进行了展望以及对本文的不足之处和未来的改进方向进行了讨论。

关键词: 知识追踪, 学习者, 知识状态, 在线教育, 深度学习

Abstract: Knowledge tracing was considered a fundamental and challenging task in online education, and it involved the establishment of a learner's knowledge state model based on their learning history. Learners could benefit from better understanding their knowledge status, while teachers could gain insights into their learning progress. A retrospective survey of knowledge tracing research for online education was presented, with a focus on developments in the relevant field. The main tasks and historical progression of knowledge tracing were introduced. Subsequently, traditional knowledge tracing models and deep learning knowledge tracing models were discussed. Furthermore, relevant datasets and evaluation metrics were summarized, alongside a compilation of the applications of knowledge tracing. In conclusion, the current state of knowledge tracing were summarized, and prospects for future advancements were explored, along with a discussion of the limitations of the study and potential directions for future improvements.

Key words: knowledge tracing, learners, state of knowledge, online education, deep learning

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