%0 Journal Article %1 MOLENAAR2023107540 %A Molenaar, Inge %A de Mooij, Susanne %A Azevedo, Roger %A Bannert, Maria %A Järvelä, Sanna %A Gašević, Dragan %D 2023 %J Computers in Human Behavior %K artificialintelligence learninganalytics methodologies multimodaldata processmining self-regulatedlearning %P 107540 %R https://doi.org/10.1016/j.chb.2022.107540 %T Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data %U https://www.sciencedirect.com/science/article/pii/S0747563222003600 %V 139 %X 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.

@article{MOLENAAR2023107540, abstract = {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.}, added-at = {2022-10-30T16:21:26.000+0100}, author = {Molenaar, Inge and de Mooij, Susanne and Azevedo, Roger and Bannert, Maria and Järvelä, Sanna and Gašević, Dragan}, biburl = {https://www.bibsonomy.org/bibtex/253ffc01b8391392027d3d74fa65a545e/ereidt}, doi = {https://doi.org/10.1016/j.chb.2022.107540}, interhash = {88d74c511f94b0acc87a291154c2386b}, intrahash = {53ffc01b8391392027d3d74fa65a545e}, issn = {0747-5632}, journal = {Computers in Human Behavior}, keywords = {artificialintelligence learninganalytics methodologies multimodaldata processmining self-regulatedlearning}, pages = 107540, timestamp = {2022-10-30T16:21:26.000+0100}, title = {Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data}, url = {https://www.sciencedirect.com/science/article/pii/S0747563222003600}, volume = 139, year = 2023 }