Effectiveness of discovery learning using a mobile otoscopy simulator on knowledge acquisition and retention in medical students: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Portable educational technologies, like simulators, afford students the opportunity to learn independently. A key question in education, is how to pair self-regulated learning (SRL) with direct instruction. A cloud-based portable otoscopy simulator was employed to compare two curricula involving SRL. Pre-clerkship medical students used a prototype smartphone application, a 3D ear attachment and an otoscope to complete either otoscopy curriculum. METHODS: Pre-clerkship medical students were recruited and randomized to two curriculum designs. The "Discovery then Instruction" group received the simulator one week before a traditional lecture, while the "Instruction then Discovery" group received it after the lecture. To assess participants' ability to identify otoscopic pathology, we used a 100-item test at baseline, post-intervention and 2-week retention time points. Secondary outcomes included self-reported comfort, time spent using the device, and a survey on learning preferences. RESULTS: = 4.61, p = 0.04), where only the discovery then instruction group's comfort improved significantly. Overall device usage was low, as the discovery then instruction group spent 21.47 ± 26.28 min, while the instruction then discovery group spent 13.84 ± 18.71 min. The discovery first group's time spent with the simulator correlated moderately with their post-test score (r = 0.42, p = 0.07). After the intervention, most participants in both groups (63-68%) stated that they would prefer the instruction then discovery sequence. CONCLUSIONS: Both curricular sequences led to improved knowledge scores with no statistically significant knowledge differences. When given minimal guidance, students engaged in discovery learning minimally. There is value in SRL in simulation education, and we plan to further improve our curricular design by considering learner behaviours identified in this study.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".