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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".