Blended learning in large enrolment courses: Student perceptions across four different instructional models
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
Drawing on data from five large enrolment introductory courses in a public university, we compared students’ perceptions of blended learning on design, interaction, learning, and satisfaction in four different blended models. The models, which were the result of a course redesign initiative, had different combinations of face-to-face lectures, online sessions, and small group tutorial classes. Our findings suggest that students perceived courses with fully online lectures and in-class tutorials most positively on design and overall satisfaction, while those enrolled in courses with in-class lectures and in-class tutorials, supplemented by online discussions, felt most positively about interaction. Students perceived learning in the former courses more favourably than the latter, however the differences were not statistically significant. The least preferred model overall was the one that had in-class lectures and tutorials that alternated weekly between in-class and online sessions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it