Comparing student, instructor, classroom and institutional data to evaluate a seven-year department-wide science education initiative
Bibliographic record
Abstract
We compared seven unrelated data-sets to evaluate a major education improvement initiative. Perceptions of students in 54 course sections were surveyed regarding the helpfulness of 39 specific teaching or learning strategies, and relative workloads and enthusiasm were compared to their other courses. Classes were observed using an established protocol, instructors completed a teaching practices inventory, and their experience with evidence-based pedagogies was established. A graduation exit survey provided longitudinal indications of changes prior to the study, and institutional student ratings of instruction were obtained. This study sought to determine whether improvements were consistently revealed by these data, how perceptions depended upon class size, course improvement model and instructor experience, and whether student ratings captured consistent perceptions about effectiveness. Overall, results compared favourably. Student perceptions and observed effectiveness depended mainly upon class size and improvement strategy. Students found experiences more effective in courses taught by experienced instructors and in classes observed to be more active. Relative workloads were not correlated with any measure of effectiveness while relative enthusiasm was higher in courses perceived to be more effective. Student ratings were consistent with other data-sets, although they did not provide information specific enough for informing further improvements.
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.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".