Promoting Active Learning in Physiology Lectures Through Student Response Systems: To Click or Not to Click
Bibliographic record
Abstract
Courses in physiology engage students through active learning strategies including small group discussions, group work, and opportunities to explore a scientific problem and explain their findings. Many of these active learning exercises take place in tutorial and laboratory settings. Unfortunately, traditional physiology lectures are often limited to conveying information through lecturing and PowerPoint slides. This approach provides little opportunity for student engagement above lower-order cognition, i.e., writing notes, listening, memorization (Freeman et al. 2014). Student response systems (e.g., clickers) are a valuable tool to facilitate active learning in the lecture setting that could enable students to take control of their learning (“Do I truly understand this topic/concept/theory?”) (Hwang, Wong, Lam & Lam 2015). In addition, clickers provide valuable instant feedback to the lecturer about student comprehension, and can be used to track participation and attendance. Many platforms are now available including clicker devices and virtual clickers to facilitate active learning and meta-cognitive exercises in the lecture setting. Student feedback response platforms may provide a way to introduce active learning into the lecture setting with physiology lectures resulting improved engagement and better achievement of learning outcomes. This workshop provides practical strategies and examples to help instructors evaluate the benefits, challenges, and methods of integrating student response systems into the physiology lecture setting.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".