Using active learning strategies to shift student attitudes and behaviours about learning and teaching in a research intensive educational context
Bibliographic record
Abstract
Background: Active learning strategies were used to shift student attitudes and behaviours about learning and teaching in a research intensive Faculty of Pharmacy and Pharmaceutical Sciences at a large Australian University. Principles and active learning strategies were developed and tested in discrete content sections during the pilot phase, and then implemented for all students in first and second year units the following two years. Method: The impact of the approach on student perceptions of active learning, attendance in face-to-face classes and performance in exams were evaluated. Results: The majority of students perceived that active learning improved their understanding of content, developed skills in critical thinking and communication, and corrected misconceptions. Nevertheless, 53% of students felt they “ learnt better ” in traditional lectures than with active learning during the pilot phase. After strategies to improve student understanding of the generic skill benefit of active learning were implemented, this proportion fell to 34% in year one of implementation and 15% in year two. Students who reported that they “ learnt better in traditional lectures ” valued clear content presentation, whilst students who disagreed with this statement valued communication and critical thinking skills development and problem solving. Student attendance was 73% higher in active learning units than untransformed units during the implementation phase. Conclusion: The use of a coordinated and strategic approach to implement active learning led to positive changes in student attitudes to their learning and associated behaviours.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".