Using “Concept Sorting” to Study Learning Processes and Outcomes
Bibliographic record
Abstract
PURPOSE: First, to evaluate "concept sorting" as a tool for assessing knowledge organization in the memories of first-year medical students, and second, to study the relationship between knowledge organization and examination performance. METHOD: During 2001, first-year medical students taking the Renal Course at the University of Calgary Faculty of Medicine were given a questionnaire on scheme use and were given a concept-sorting task in the domain of metabolic alkalosis. The sophistication of their concept sorting was graded using the number of physiology-based groups they formed. Review of the course's examination scores allowed correlation with concept-sorting scores. Statistical analyses used Fisher's exact test and the two-sample t-test. Pearson's correlation coefficient and the kappa statistic were used for correlation between raters. RESULTS: A total of 81 of 99 students completed the study. The concept-sorting score (mean +/- SEM) for students who used the scheme was higher than was the score for students who did not (2.5 +/- 0.14 versus 1.91 +/- 0.12, p =.016). Students who scored higher in the concept-sorting task, referred to as "deep learners," scored higher than did "surface learners" on exam questions on metabolic alkalosis (2.81 versus 2.29, p =.02). There was no difference in the overall examination performances between the two groups. CONCLUSIONS: Concept sorting may be a useful tool for studying the learning process. Scheme use by students produces a positive outcome on examination performance.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".