The TRIPSE (Tri‐Partite Problem‐Solving Exercise) in a Large Class Setting
Bibliographic record
Abstract
The TRIPSE is a process‐oriented problem solving exercise that mimics the scientific process: 1) Given limited information, students frame a set of possible explanations. 2) They then select their best explanation and either design experimental tests or propose avenues for further explanation. 3) Given additional information, they re‐assess their original answers. This exercise has generally been used in courses with classes of 15 to 25 students. We report our experiences using the exercise in an introductory biology class of 204 freshmen. Prior to the actual exercise, students were given a practice run followed by a feedback session. We (SN/PKR) graded all TRIPSEs independently. In an exit survey, students rated different evaluation tools used in this course (journals, critiques, abstracts, TRIPSEs, posters, etc) for their learning value in comparison to standard MCQs. The TRIPSE received the highest ratings suggesting that they valued it greatly. We later had a 3rd assessor, who was not involved in the course, grade the answers to gauge inter‐rater reliability. This exercise, which students find valuable, could be readily adapted to large classes.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".