Task Demands in OSCEs Influence Learning Strategies
Bibliographic record
Abstract
THEORY: Models on pre-assessment learning effects confirmed that task demands stand out among the factors assessors can modify in an assessment to influence learning. However, little is known about which tasks in objective structured clinical examinations (OSCEs) improve students' cognitive and metacognitive processes. Research is needed to support OSCE designs that benefit students' metacognitive strategies when they are studying, reinforcing a hypothesis-driven approach. With that intent, hypothesis-driven physical examination (HDPE) assessments ask students to elicit and interpret findings of the physical exam to reach a diagnosis ("Examine this patient with a painful shoulder to reach a diagnosis"). HYPOTHESES: When studying for HDPE, students will dedicate more time to hypothesis-driven discussions and practice than when studying for a part-task OSCE ("Perform the shoulder exam"). It is expected that the whole-task nature of HDPE will lead to a hypothesis-oriented use of the learning resources, a frequent use of adjustment strategies, and persistence with learning. METHOD: In a mixed-methods study, 40 medical students were randomly paired and filmed while studying together for two hypothetical OSCE stations. Each 25-min study period began with video cues asking to study for either a part-task OSCE or an HDPE. In a crossover design, sequences were randomized for OSCEs and contents (shoulder or spine). Time-on-task for discussions or practice were categorized as "hypothesis-driven" or "sequence of signs and maneuvers." Content analysis of focus group interviews summarized students' perception of learning resources, adjustment strategies, and persistence with learning. RESULTS: When studying for HDPE, students allocate significantly more time for hypothesis-driven discussions and practice. Students use resources contrasting diagnoses and report persistence with learning. When studying for part-task OSCEs, time-on-task is reversed, spent on rehearsing a sequence of signs and maneuvers. CONCLUSIONS: OSCEs with similar contents but different task demands lead to opposite learning strategies regarding how students manage their study time. Measuring pre-assessment effects from a metacognitive perspective provides empirical evidence to redesign assessments for learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".