Implementation Outcomes of Reusable Learning Objects in Health Care Education Across Three Malaysian Universities: Evaluation Using the RE-AIM Framework
Bibliographic record
Abstract
Background: Current e- learning evaluation focuses on learners' knowledge gain, satisfaction, perceptions, and attitudes; few assess the implementation outcomes of e- learning resources in teaching and learning. Objective: In this study, we used the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework to systematically evaluate the implementation outcomes of reusable learning objects (RLOs) in the context of health care education. Methods: This study is a part of the Advancing Co-creation of RLOs to Digitise H ealthcare Curriculum (ACoRD) project, wherein we developed and implemented 23 RLOs across 3 Malaysian universities for medical, pharmacy, and biomedical curricula. Implementation and dissemination strategies were employed. Data were collected using a self-administered web-based questionnaire and Google Analytics. Results: Th is study report s a cumulative RLO access of 7622 users from 48 countries ( reach). Users rated RLOs as very helpful ( 1452/ 2071, 70.1%) or helpful ( 601/ 2071, 29.1 %). Pre assessments and postassessments showed a significant improvement in the knowledge score (21 RLOs, P < .05) and confidence level (17 RLOs, P < .05) (e ffectiveness). All 3 Malaysian universities adopted RLOs in the fields of professional development, primary care medicine, medicine, pediatrics, nursing, pharmacy, and biomedicine ( adoption). The percentage of users who completed RLOs ranged from 5.6% ( 10/ 179) to 8 5% ( 78/ 92), with nonbounced users (users who viewed more than one page) ranging from 16.3% ( 165/ 1014) to 8 8.5 % ( 3 70/ 4 18) ( implementation). In the 4 months following the completion of the AC oRD project, a total of 2107 users accessed RLOs ( maintenance). Conclusions: We systematically evaluated the implementation of e- learning resources by using the RE-AIM framework, informing future strategies to integrate e- learning innovations in real-world teaching and learning practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".