Promoting Self-regulated Learning in Online Learning by Triggering Tailored Interventions

Hae Jin Lee, Paul Hur, Suma Pallathadka Bhat, Nigel Bosch

Research output: Contribution to journalConference articlepeer-review

Abstract

In online education, students are expected to be independent learners who can self-regulate and reflect on their activities during the learning process. However, not all students have self-regulated learning (SRL) skills, and students with weak SRL skills tend to underperform in distance learning environments. The aim of our pilot study was to promote self-regulated learning in online education by triggering tailored SRL interventions automatically. As a first step toward, we constructed a quantitative research design where 58 students participated in 1) learning about introductory descriptive statistical concepts and 2) interacting with a self-paced online learning software throughout the experiment. We used the participants' action log files as a dataset to extract generalizable features, including pretest grade, quiz grade, reading time, and posttest grade. Then, we trained a random forest regressor model to predict student outcome (posttest). The correlation between actual and predicted posttest score was r =.576, indicating promise for accurately predicting and intervening. In the next phase of this work, we will apply SHAP (SHapley Additive exPlanations) to personalize SRL interventions by recommending each student to review the single topic that most negatively contributes to predicted posttest grade.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume3051
StatePublished - 2021
Event2021 Joint Workshops at the International Conference on Educational Data Mining, EDM-WS 2021 - Virtual, Online
Duration: Jun 29 2021 → …

Keywords

  • Computer-based learning
  • Interventions
  • Machine learning explanations
  • Self-regulated learning

ASJC Scopus subject areas

  • Computer Science(all)

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