Blending for student engagement: Lessons learned for MOOCs and beyond
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it