Incorporating higher order thinking and deep learning in a large, lecture-based human physiology course: can we do it?
Bibliographic record
Abstract
Large classes taught with didactic lectures and assessed with multiple-choice tests are commonly reported to promote lower order (LO) thinking and a surface approach (SA) to learning. Using a case study design, we hypothesized that incorporating instructional scaffolding of core physiology principles and assessing students exclusively with long-answer written tests would encourage higher order (HO) thinking and promote a deep approach (DA) to learning in a two-course physiology sequence (Phys I and II), despite their large size. Test questions were categorized as LO or HO according to the Blooming Biology Tool, and students’ LO and HO performance was determined for each of six tests across the two courses. The validated Revised Two-Factor Study Process Questionnaire survey tool was administered at the beginning and end of each course to measure student approach to learning. HO performance was maintained across Phys I (72.9 ± 19.4 vs. 74.8 ± 20.7%, P = 0.37) and significantly improved across Phys II (69.9 ± 18.4 vs. 79.4 ± 14.8%, P < 0.001). Unexpectedly, students’ LO performance declined from the beginning to end of Phys I (78.5 ± 20.6 vs. 69.4 ± 17.9%, P < 0.001) and Phys II (80.5 ± 19.6 vs. 72.2 ± 24.3%, P < 0.001). Students’ approach to learning did not change throughout Phys I or II, but at each time point students preferred a DA over a SA. Taken together, these results indicate that an intentionally designed large lecture class can support a DA to learning and suggests that this teaching and assessment structure may be particularly well suited to promote HO thinking, albeit possibly at the expense of LO thinking.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".