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ORIGINAL RESEARCH article

Front. Educ.
Sec. Digital Education
Volume 10 - 2025 | doi: 10.3389/feduc.2025.1487344

Using a Wider Digital Ecosystem to Improve Self-Regulated Learning

Provisionally accepted
Molly Domino Molly Domino 1*Clifford A. Shaffer Clifford A. Shaffer 2
  • 1 The Roux Institute, Northeastern University, Portland, United States
  • 2 Virginia Tech, Blacksburg, Virginia, United States

The final, formatted version of the article will be published soon.

    Self-regulated learning skills are necessary for academic success. While not all students entering post-secondary education are proficient at many of these critical skills, they can be improved upon when practiced. However, self regulation tends to be highly internal, making it difficult to measure. One form of measurement comes from using data traces collected from educational software. These allow researchers to make strong empirical inferences about a student's internal state. Automatically captured data traces also make it possible to provide automated interventions that help students practice and master self-regulated learning skills.We present a set of promising data traces that are grounded in theory to study self-regulated learning within a typical Computer Science course. We also make the case for taking a broader perspective with our data collection efforts. The traces identified in this paper are not from one source, but the full ecosystem of software tools common to CS courses. Extra attention is given to studying the skill of help-seeking, which is both a key to success in CS and requires unobtrusive observation to properly measure.

    Keywords: self-regulated learning, Learning analytics, Computing education, measurement models, Data traces, Unobtrusive

    Received: 28 Aug 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Domino and Shaffer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Molly Domino, The Roux Institute, Northeastern University, Portland, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.