Using learning analytics to explore self‐regulated learning in flipped blended learning music teacher education

Abstract Blended learning (BL) is a popular e‐Learning model in higher education that has the potential to take advantage of learning analytics (LA) to support student learning. This study utilized LA to investigate fourth‐year undergraduates' ( n = 157) use of self‐regulated learning (SRL) within the online components of a previously unexamined BL discipline, Music Teacher Education. SRL behaviors were captured unobtrusively in real time through students' interaction with course materials in Moodle. Categorized by function: (1) activating —online access location, day‐of‐the‐week, time‐of‐day; (2) sustaining —online frequency; and (3) structuring —online regularity and exam review patterns, all six SRL behaviors were revealed to have weak to moderate significant relationships with academic achievement. Results indicated access day‐of‐the‐week and access frequency as the strongest predictors for student success. Findings regarding access regularity when viewed through results from previous SRL‐LA research may suggest the importance of this SRL behavior for successful students within several BL discipline areas. In addition, the role of learning design (eg, flipped instruction) in potentially scaffolding students' choices toward specific SRL behaviors, was revealed as an important context for future researchers' consideration.