TRANSFORMING TEACHING AND LEARNING USING AN ACTIVE LEARNING APPROACH ACROSS AN ENTIRE RESEARCH INTENSIVE FACULTY OF PHARMACY AND PHARMACEUTICAL SCIENCES
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 conventional lecture has significant limitations, often leading to a passive learning experience for students. This abstract reports a process of transforming teaching and learning with active learning strategies in a research intensive Faculty of 45 academic staff and more than 1000 students. The pilot phase involved 9 staff who developed a common vision and principles. After refinement, an implementation phase involving 12 staff, including three from the pilot group, commenced within all first year subjects. Staff use of active learning strategies in classes increased by 6‐fold and 7‐fold in the pilot and implementation phases respectively. Analysis of exam questions indicated that staff increased their use of questions addressing higher order cognitive skills by 42% (pilot phase) and 51% (implementation phase), compared to exams produced prior to the approach. After the pilot phase, only 3 out of 9 staff agreed that they “understood “what makes for an effective active learning exercise”, which rose to 7 out of 9 staff at the completion of the implementation phase. Deliberate engagement with the student body was effective in overcoming much of the initial resistance to change expressed by students: 53% of students felt they “learnt better” in traditional lectures than with active learning during the pilot phase, and this proportion fell to 34% in year one of implementation and 15% in year two. The development of an explicit, common approach and the evaluation and refinement of active learning approaches were effective elements of our transformational change management strategy.
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 | 0.058 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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