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

  • Ting Zhang
  • , Bo Jiang*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationInternational Collaboration toward Educational Innovation for All
Subtitle of host publicationOverarching Research, Development, and Practices - 16th International Conference of the Learning Sciences, ICLS 2022
EditorsClark Chinn, Edna Tan, Carol Chan, Yael Kali
PublisherInternational Society of the Learning Sciences (ISLS)
Pages949-952
Number of pages4
ISBN (Electronic)9781737330653
StatePublished - 2022
Event16th International Conference of the Learning Sciences, ICLS 2022 - Virtual, Online, Japan
Duration: 6 Jun 202210 Jun 2022

Publication series

NameProceedings of International Conference of the Learning Sciences, ICLS
ISSN (Print)1814-9316

Conference

Conference16th International Conference of the Learning Sciences, ICLS 2022
Country/TerritoryJapan
CityVirtual, Online
Period6/06/2210/06/22

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