Blending for student engagement: Lessons learned for MOOCs and beyond
Bibliographic record
Abstract
The purpose of this ongoing, three-year action research study is to explore the digital challenges of student engagement in higher education within the experimental platform of blended learning. Research questions examine the role of digital innovation in supporting diverse learners, as well as building meaningful connections with technology for undergraduate teacher education students. Results from qualitative data collected through instructor journals and field notes and student mid-term and exit surveys during year one, indicate blended learning can be effective for modelling how to use technology to shift learners towards more active agency. The immediacy of the localised university classroom delivered a viable research setting for digital experimentation, while providing a significant lived experience for undergraduates to springboard their future technological practices with K–12 students. Four pedagogical opportunities for digital intentionality in virtual spaces emerged during data analysis and are shared as considerations for future innovation: (1) designing digital resources, (2) scaffolding student learning, (3) learner customisation, and (4) promoting the lived experience. Lessons learned could be effective in helping develop higher quality educational experiences for on-campus students, as well as scaffolding greater engagement in online formats involving more global populations (e.g., massive online open courses – MOOCs).
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".