An Investigation of the Pedagogical Impact of Using Case-based Learning in a Undergraduate Biochemistry Course
Bibliographic record
Abstract
The use of case-based learning (CBL) provides students with diverse experiences in the classroom, including problem-solving, knowledge co-construction, communication, and group collaboration. Through these activities, students can explore and develop new knowledge, and acquire relevant skills that have application both in the classroom and beyond. While the majority of studies support the use of CBL as an active learning technique that confers positive pedagogical outcomes, most commonly the investigations compare CBL to a lecture-based method of course delivery. To address this issue, we investigated the pedagogical impact of CBL as compared to a non-CBL “mixture” of other active learning activities in an undergraduate biochemistry course, thereby allowing for a more detailed consideration of the case-specific elements of CBL. It was observed that use of CBL prevented the increase in surface approach to learning that occurred across the semester in the non-CBL group, and improved performance in the course, most notably at the knowledge level of Bloom’s taxonomy. As well, there was an improvement in student perception of the appropriateness of the course workload. Overall, these findings support the use of CBL as a preferred active learning technique, and provide valuable insight into the outcomes associated with its use.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".