跳到主要导航 跳到搜索 跳到主要内容

LF-LKT: A logistic regression knowledge tracing model integrating learning and forgetting

  • Ting Zhang
  • , Bo Jiang*
  • *此作品的通讯作者
  • East China Normal University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In the process of learning, learning behavior and forgetting behavior are interwoven, and students' forgetting behavior has great influence on Knowledge Tracing(KT). In order to accurately model learning and forgetting behaviors, this paper proposes a learning-forgetting logistic knowledge tracing(LF-LKT) model that integrated students' forgetting factors. Three factors affecting knowledge forgetting, including the time interval of KC, KC presentation sequence (that is, the recency effect), learning opportunities of KC, and students' response of KC are considered in this paper. We also give different level of weights to time interval. The proposed model is compared with other four models on three dataset. Results shows LF-LKT improves the predictive performance as compared to AFM, PFA. Moreover, the ablation study was conducted to investigate the influence of different factors and the result suggest the combination of three factors results in performance improvement.

源语言英语
主期刊名International Collaboration toward Educational Innovation for All
主期刊副标题Overarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022
编辑Clark Chinn, Edna Tan, Carol Chan, Yael Kali
出版商International Society of the Learning Sciences (ISLS)
949-952
页数4
ISBN(电子版)9781737330653
出版状态已出版 - 2022
活动16th International Conference of the Learning Sciences, ICLS 2022 - Virtual, Online, 日本
期限: 6 6月 202210 6月 2022

出版系列

姓名Proceedings of International Conference of the Learning Sciences, ICLS
ISSN(印刷版)1814-9316

会议

会议16th International Conference of the Learning Sciences, ICLS 2022
国家/地区日本
Virtual, Online
时期6/06/2210/06/22

指纹

探究 'LF-LKT: A logistic regression knowledge tracing model integrating learning and forgetting' 的科研主题。它们共同构成独一无二的指纹。

引用此