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

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 measurement processes advance our understanding how multimodal data unobtrusively capture learners' cognitive, metacognitive, affective, motivational states over time, tasks, domains, contexts. This paper introduces a processes, data, analysis (SMA) grid maps joint individual research authors (63 papers) last five years onto grid. shows streams were used investigate processes. two-dimensional space on SMA helpful for visualizing relations possible combinations between overview serves as an analytical introduction current special issue “Advancing Research Artificial Intelligence (AI)” we encourage position new unexplored frontiers. We emphasize need intensive strategic collaboration accelerate progress using interdisciplinary methods develop accurate in educational technologies. • Self-regulated (SRL) started one stream analyse certain process simple statistics. diversified towards horizontal approach analysing multiple more advanced statistical techniques. As well vertical approach, investigating streams. Novel integrated aligning AI