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 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 | 0.081 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".