University of Oulu

Inge Molenaar, Susanne de Mooij, Roger Azevedo, Maria Bannert, Sanna Järvelä, Dragan Gašević, Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data, Computers in Human Behavior, Volume 139, 2023, 107540, ISSN 0747-5632,

Measuring self-regulated learning and the role of AI : five years of research using multimodal multichannel data

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Author: Molenaar, Inge1,2; de Mooij, Susanne1; Azevedo, Roger3;
Organizations: 1Behavioural Science Institute, Radboud University, Nijmegen, the Netherlands
2National Education Lab AI, the Netherlands
3School of Modeling Simulation and Training, University of Central Florida, Orlando, FL, USA
4School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
5Department of Educational Sciences and Teacher Education, University of Oulu, Oulu, Finland
6Centre for Learning Analytics, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.6 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2023-11-01


Learning sciences are embracing the significant role technologies can play to better detect, diagnose, and act upon self-regulated learning (SRL). The field of SRL is challenged with the measurement of SRL processes to advance our understanding of how multimodal data can unobtrusively capture learners’ cognitive, metacognitive, affective, and motivational states over time, tasks, domains, and contexts. This paper introduces a self-regulated learning processes, multimodal data, and analysis (SMA) grid and maps joint and individual research of the authors (63 papers) over the last five years onto the grid. This shows how multimodal data streams were used to investigate SRL processes. The two-dimensional space on the SMA grid is helpful for visualizing the relations and possible combinations between the data streams and how the measurement of SRL processes. This overview serves as an analytical introduction to the current special issue “Advancing SRL Research with Artificial Intelligence (AI)” and we encourage to position new research and unexplored frontiers. We emphasize the need for intensive and strategic collaboration to accelerate progress using new interdisciplinary methods to develop accurate measurement of SRL in educational technologies.

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Series: Computers in human behavior
ISSN: 0747-5632
ISSN-E: 0747-5632
ISSN-L: 0747-5632
Volume: 139
Article number: 107540
DOI: 10.1016/j.chb.2022.107540
Type of Publication: A1 Journal article – refereed
Field of Science: 516 Educational sciences
Funding: This E-CIR research network “Measuring and Supporting Student's Self-Regulated Learning in Adaptive Educational Technologies”, in which Inge Molenaar, Sanna Järvelä, Maria Bannert, Dragan Gašević and Roger Azevedo were involved for more than five years, was funded by EARLI-Centre for Innovative Research. The work was also in part supported by funding from Jacobs Foundation (CELLA 2 CERES) awarded to the same five authors. The work of Maria Bannert, Inge Molenaar and Dragan Gašević was in part supported by funding from Deutsche Forschungsgemeinschaft, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Economic and Social Research Council of the United Kingdom (BA 2044/10–1 | GA 2739/1-1 | MO 2698/1-1) through Open Research Area (Call 5). The work of Dragan Gasevic was in part supported by the Australia Research Council (DP220101209). The work of Maria Bannert was in part supported by the Deutsche Forschungsgemeinschaft (BA 2044/7–1, 7–2). The work of Sanna Järvelä has been supported by the Finnish Academy (Grants No: 259214 and No: 324381). The work of Inge Molenaar has been supported by the National science organisation (Grant No: 451-16-017), European Rescearch Council (ERC 948786) and the Jacobs Foundation Fellowship. The work of Roger Azevedo has been supported by several grants from the National Science Foundation (DRL#1661202, DRL#1916417, DRL#1916417, IIS#1917728, and BCS#2128684).
Academy of Finland Grant Number: 259214
Detailed Information: 259214 (Academy of Finland Funding decision)
324381 (Academy of Finland Funding decision)
Copyright information: © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (