Abstract
This chapter examines opportunities afforded by trace data to capture dynamically changing latent states and trajectories spanning states in self-regulated learning (SRL). We catalog and analyze major challenges in temporally investigating SRL constructs related to a prominent motivational factor, achievement goals. The dynamics of potentially frequent state changes throughout a learning session and across sessions are poorly reflected by self-report survey items typically administered before and after a session or, less informatively, at the beginning of an academic term. Trace data, carefully operationalized, offer substantial benefits compensating for shortcomings of comparatively static survey data. We summarize three recent studies addressing these challenges and characterize learning analytics designed to promote SRL and motivation formed from unobtrusive traces. This approach provides a practical and continuously updatable account of SRL constructs, varying dynamically within and across study sessions. We conclude by proposing a research agenda for learning analytics focusing on guiding and supporting SRL.
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Choi, H., Winne, P.H., Brooks, C. (2023). Reconfiguring Measures of Motivational Constructs Using State-Revealing Trace Data. In: Kovanovic, V., Azevedo, R., Gibson, D.C., lfenthaler, D. (eds) Unobtrusive Observations of Learning in Digital Environments. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-30992-2_5
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