14. Two Approaches to Case-Based Teaching in Science: Tales From Two Professors
Bibliographic record
Abstract
Case-based teaching and learning strategies can offer instructors effective pedagogical tools to scaffold student learning through activities designed to fulfill teaching objectives and desired student learning outcomes. In science disciplines, programs strive to impart knowledge in addition to providing students environments through which they can learn through collaboration. Case-based studies can effectively expose students to the process of science and encourage them to work through facts, analyze data, formulate solutions, draw conclusions, and predict consequences. Despite the versatility of case studies as teaching and learning tools, many factors influence their implementation in a given teaching environment. Inasmuch, the manner in which they are used is typically dependent upon specific teaching and learning objectives and the historical context of the course (e.g., student enrolment, year of instruction, lecture vs. lab vs. tutorial settings). In this article, we discuss two novel approaches for case-based teaching and learning in Biology as a means to convey lecture content, encourage students to apply fundamental concepts taught in lectures, while exposing them to the process of science in a dynamic environment.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".