Patterns of performance in students with frequent low stakes team based learning assessments: Do students change behavior?
Bibliographic record
Abstract
Team-based learning offers an active learning strategy that provides a structure for measurement of learning and feedback to the students. Aggregating these results provides a longitudinal pattern of student performance. In this study, we analyzed results from a sequence of assessments related to TBL IRAT and GRAT assessments and traditional quizzes in a second-year musculoskeletal course in an undergraduate medical education program to determine if there are any measurable patterns, or performance trends, that students demonstrate in the course. Analyzing results from four academic years, we found evidence supporting there is predictability in student's future week's performance based on past performances across teaching modalities. We hypothesize that students are moderating their own effort regarding weekly low-stake assessments in prioritizing their academic efforts. The results from this study highlight the role of self-efficacy in medical education and suggest a new area of research for assessment of student performance patterns. Future studies could investigate whether these performance patterns are replicated in other assessment modalities and whether the same pattern holds for high-stakes assessments.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".