Developing the Evidence Base for M-Learning in Undergraduate Radiology Education: Identifying Learner Preferences for Mobile Apps
Bibliographic record
Abstract
PURPOSE: There is a lack of evidence for developing radiology mobile apps for medical students. This study identifies the characteristics which students perceive as most valuable to teaching radiology with mobile apps (m-learning). METHODS: An online anonymous survey was administered to second- to fourth-year medical students at a single institution. The survey, which was based on established theoretical framework, collected students' preferred content organization, content presentation, and delivery strategies. The Copeland method was used to rank student preferences and a 2-tailed t test was used to determine if student responses were related to their clinical experience, with statistical significance at P < .05. RESULTS: The response rate was 25.6% (163/635). For content organization, image interpretation (66.9%), imaging anatomy (61.3%), and common pathological conditions (50.3%) were selected as the most important. For content presentation, quizzes (49.1%) and case presentations (46.0%) were selected as the most useful. Students with clinical experience rated algorithms as more important (P < .01) and quizzes as less important (P = .03). For delivery strategies, ease of use (92.6%), navigation (90.8%), and gestural design (74.8%) were deemed the most applicable. CONCLUSION: This study documents medical students' preferences for m-learning in radiology. Although learner preferences are not the only feature to consider in the development of educational technology, these provide the initial framework for radiologists wishing to develop and incorporate mobile apps into their teaching.
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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 | 0.082 | 0.397 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| 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".