Psychological Factors Affecting Medical Students’ Learning with Erroneous Worked Examples
Bibliographic record
Abstract
The acquisition of diagnostic competence is seen as a major goal during the course of study in medicine. Oneinnovative method to foster this goal is problem-based learning with erroneous worked examples provided in acomputer learning environment. The present study explores the relationship of attitudinal, emotional andcognitive factors for learning with erroneous worked examples. 72 medical students from a German universityworked with six case-based examples in the domain of arterial hypertension. Domain-specific conceptual priorknowledge, anxiety of making errors, attitudes towards errors, and ambiguity tolerance were measured asindependent variables before the students worked with the examples. Diagnostic competence wasoperationalized by measuring conceptual, strategic, and conditional knowledge, which were assessed asdependent variables after working with the learning environment. A cluster analytic approach yielded threeclusters. For each, the relationship with the learning outcome was analysed. Cluster membership significantlyinfluenced the learning outcome in strategic, but not in conditional knowledge. Furthermore, cluster membershiphad a significant effect on conceptual knowledge; there was also an increase in conceptual knowledge for allclusters when conceptual knowledge measured after the treatment was compared to prior conceptual knowledge.The results clearly indicate the importance of a certain pattern of psychological factors for learning witherroneous worked examples.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".