Impact of High-Fidelity E-Learning on Knowledge Acquisition and Satisfaction in Radiation Oncology Trainees
Bibliographic record
Abstract
Background: e-Learning is an underutilized tool in education for the health professions, and radiation medicine, given its reliance on technology for clinical practice, is well-suited to training simulation in online environments. The purpose of the present study was to evaluate the knowledge impact and user interface satisfaction of high-(hf) compared with low-fidelity (lf) e-learning modules (e-modules) in radiation oncology training. Methods: Two versions of an e-module on lung radiotherapy (lf and hf) were developed. Radiation oncology residents and fellows were invited to be randomized to complete either the lf or the hf module through individual online accounts over a 2-week period. A 25-item multiple-choice knowledge assessment was administered before and after module completion, and user interface satisfaction was measured using the Questionnaire for User Interaction Satisfaction (quis) tool. Results: < 0.01). Scores on the quis indicated that participants were satisfied with various aspects of the user interface. Conclusions: The hf e-module had a greater impact on knowledge acquisition, and users expressed satisfaction with the interface in both the hf and lf situations. The use of e-learning in a competency-based curriculum could have educational advantages; participants expressed benefits and drawbacks. Preferences for e-learning integration in education for the health professions should be explored further.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".