Temporally-focused analytics of self-regulated learning: A systematic review of literature

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We present a systematic literature review of data-driven self-regulated learning (SRL) that emphasises the methodological importance of temporality and sequence, as opposed to conventional statistical analysis. Researchers seem unanimous in their view of the importance of SRL in modern online and blended educational settings; this is borne out by number of reviews of literature on the subject. There has, as yet, been no systematic treatment of SRL in the context of its conceptualisation as a phenomenon that unfolds in sequences over time. To address this limitation, this review explores the corpus of work (n = 53) in which SRL and its related dimensions are analysed through the lenses of temporality, sequence and order. The results show that, in the pursuit of validity and impact, key decisions need to be addressed in regard to theoretical grounding, data collection, and analytic methods. Based on these outcomes, we propose a framework of directives and questions to aid researchers who want to push forward the field. This framework comprises four sub-areas: i) methodological considerations, relating to data capture and analytic processes; ii) theoretical considerations, relating to the usage of models of self-regulated learning and their related dimensions; iii) validity considerations, relating to the robustness of the chosen analytic outcomes studied; and iv) temporal considerations, relating to the articulation of analytic outcomes in the context of temporality and sequence.