Promoting Active Learning in Physiology Lectures Through Student Response Systems: To Click or Not to Click
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
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.
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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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