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Adaptive Learning Technologies generate data traces as children interact with them, offering a unique opportunity to estimate self-regulated learning (SRL) support needs. Providing timely, data-driven support for these needs may enhance children’s ability to self-regulate their learning and improve learning outcomes. While previous work has identified different levels of SRL support needs using Bayesian non-parametric clustering, these classifications were determined after completing a learning session, delaying potential support. In this study, we present a novel method for identifying children’s SRL support needs during a learning session, utilizing a Dirichlet-process Gaussian-process mixture model (DPGP). The model clustered based on children’s response count and response time, resulting in a Matthews’ correlation coefficient of 0.75 after children solve 33 problems out of an average of 71 - less than half the session. Our findings demonstrate that real-time identification of SRL support needs is feasible and effective. This work opens new possibilities for enhancing personalized, online learning experiences by enabling timely, data-driven support tailored to each child’s needs.

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Predicting Self-regulated Learning Support Needs During Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Rick Dijkstra,
  • Max Hinne,
  • Eliane Segers,
  • Josh Ring,
  • Inge Molenaar

摘要

Adaptive Learning Technologies generate data traces as children interact with them, offering a unique opportunity to estimate self-regulated learning (SRL) support needs. Providing timely, data-driven support for these needs may enhance children’s ability to self-regulate their learning and improve learning outcomes. While previous work has identified different levels of SRL support needs using Bayesian non-parametric clustering, these classifications were determined after completing a learning session, delaying potential support. In this study, we present a novel method for identifying children’s SRL support needs during a learning session, utilizing a Dirichlet-process Gaussian-process mixture model (DPGP). The model clustered based on children’s response count and response time, resulting in a Matthews’ correlation coefficient of 0.75 after children solve 33 problems out of an average of 71 - less than half the session. Our findings demonstrate that real-time identification of SRL support needs is feasible and effective. This work opens new possibilities for enhancing personalized, online learning experiences by enabling timely, data-driven support tailored to each child’s needs.