Assessing small-scale interventions in large-scale teaching
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 use of lectures is ubiquitous in higher-education institutions, but also heavily criticized from an andragogical viewpoint. A current challenge for lecturers is to provide opportunities for active learning during these sessions and to evaluate their impact on student experience. Three one-minute interventions based on the lecture materials (write down one thing you have already learnt, one question you would like answering, and take a break) were introduced approximately 20, 30 and 40 minutes into the lecture and assessed with respect to engagement over a five-week period on a final-year psychology option. Students were invited to record their current level of lecture engagement every 5 minutes. Both between-and within-subject analyses revealed a significant increase in lecture engagement for the first intervention during the first intervention week relative to baseline weeks. The data show an enhancement of student engagement with certain small-scale interventions during large-scale teaching.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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