Learning Feedback Based on Dispositional Learning Analytics

Tempelaar, Dirk; Nguyen, Quan and Rienties, Bart (2019). Learning Feedback Based on Dispositional Learning Analytics. In: Virvou, Maria; Alepis, Efthimios; Tsihrintzis, George A. and Jain, Lakhmi C. eds. Machine Learning Paradigms: Advances in Learning Analytics. Intelligent Systems Reference Library, 158. Cham: Springer, pp. 69–89.

DOI: https://doi.org/10.1007/978-3-030-13743-4_5

Abstract

The combination of trace data captured from technology-enhanced learning support systems, formative assessment data and learning disposition data based on self-report surveys, offers a very rich context for learning analytics applications. In previous research, we have demonstrated how such Dispositional Learning Analytics applications not only have great potential regarding predictive power, e.g. with the aim to promptly signal students at risk, but also provide both students and teacher with actionable feedback. The ability to link predictions, such as a risk for drop-out, with characterizations of learning dispositions, such as profiles of learning strategies, implies that the provision of learning feedback is not the end point, but can be extended to the design of learning interventions that address suboptimal learning dispositions. Building upon the case studies we developed in our previous research, we replicated the Dispositional Learning Analytics analyses in the most recent 17/18 cohort of students based on the learning processes of 1017 first-year students in a blended introductory quantitative course. We conclude that the outcomes of these analyses, such as boredom being an important learning emotion, planning and task management being crucial skills in the efficient use of digital learning tools, help both predict learning performance and design effective interventions.

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