A Cross-sectional Study Investigating Learning Approaches in Undergraduate Medical Education
Bibliographic record
Abstract
Abstract Objective The primary objective of this proof-of-concept cross-sectional study was to identify a framework for appraising the learning-approaches of undergraduate medical students in a competency based medical curriculum and correlating the results with teaching-approaches, as well as academic performance. The study was pursued at MBRU, which is a medical school in the Middle East with an undergraduate entry medical program. Results Our framework was blueprinted using the Approaches and Study Skills Inventory for Students (ASSIST) questionnaire, to which we made some modifications such that the overall cogency of the questionnaire wasn’t affected. Initial results with modified ASSIST at MBRU showed that most of our students adopted Deep or Strategic-learning approaches. This observation is in line with other studies in the literature, which shows that modified ASSIST is a suitable tool for mapping generic learning approaches with teaching approaches. Further, based on the insights from our initial results following the implementation of modified ASSIST, we have considered specific pedagogical strategies, in practice at MBRU, which cater to the generic learning approaches of majority of our undergraduate medical students. These pedagogical approaches, A. Feynman’s Technique; and B. Blended learning strategies, if implemented suitably in a curriculum will transform “Surface-learners” to “Deep/Strategic-learners”.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".