Using learning analytics to explore self‐regulated learning in flipped blended learning music teacher education
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".