An Exercise to Transfer Learning to Novel Situations: The Student Perspective
Bibliographic record
Abstract
The Tri‐Partite Problem‐Solving Exercise (TRIPSE) is an evaluation method that simulates the scientific process. Students presented with limited data are required to state hypotheses, propose experimental tests to explore them and assess their answers after given additional information. The exercise has been used in class sizes ranging from 15 to 200 (FASEB Journal. 2008; 22:767.1). A variation, the Legacy TRIPSE (FASEB Journal. 2010; 24: 633.1) was later developed to engage students, encourage them to transfer learning to novel situations and create a bank of problems as a ‘legacy’ for future classes. Students designed problems based on published data, and provided suitable answers (hypotheses and experimental tests). We report the experience of the Legacy TRIPSE from the students’ perspectives. On a score of ten, students rated the project highly (median, mode, range, n). It provided a valuable learning experience (8, 10, 10, 100) and one that was significantly superior to conventional exams (8, 10, 10, 100). It allowed students to transfer concepts from lectures to novel situations, and helped them read scientific papers more critically and understand the operations of modern scientific practice.
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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 | 0.005 | 0.013 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".