ABSTRACT
To date there has been limited research investigating student learning strategies in flipped classroom (FC) settings. Particularly lacking is research into the dynamics of students’ adopted learning strategies. The purpose of this chapter is to address this gap by proposing a learning analytics framework for identifying discrete learning strategies and their dynamics in the context of a FC. From the theoretical perspective, the adopted framework is grounded in a model of self-regulated learning; from the technical perspective, it relies on unsupervised and temporal data science techniques (e.g. clustering, latent class analysis, and sequence analysis) for the analysis of trace (log) data concerning students’ engagement with online preparatory activities. Finally, from the design perspective, the framework adopts design-based research as the study method, and FC as the general learning design. To demonstrate the potential of the approach, the chapter presents two instances of the application of the framework in the context of a FC. The chapter also includes a discussion on how educators can apply the proposed framework to their own context in order to better understand student learning behaviour and make informed changes to their teaching approach and course design.
