The development and validation of digital medical education environment (digimee) instrument in measuring online learning environment for undergraduate medical students
Given the acknowledged role of technology-enhanced learning in medical education, there is a need for a focused evaluation approach for online learning environments specific to this field. The current lack of dedicated evaluation methods underscores the necessity for identifying key components in on...
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| Format: | Thesis |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://eprints.usm.my/61761/1/NOOR-I-KIRAN%20HARIS-TESIS%20P-UD002320-E.pdf http://eprints.usm.my/61761/ |
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| Summary: | Given the acknowledged role of technology-enhanced learning in medical education, there is a need for a focused evaluation approach for online learning environments specific to this field. The current lack of dedicated evaluation methods underscores the necessity for identifying key components in online learning environments for medical education. In response to this pressing need, this study seeks to develop and validate the Digital Medical Education Environment (Digi-MEE) Instrument, recognizing its pivotal role in addressing the existing gap and enhancing the quality of online learning experiences in medical education. The study was designed as a multi-phase mixed- methods research project conducted in online settings involving a diverse group of experts, teachers, and instructional designers and multiple medical colleges in Pakistan ( Phase 2 and 3), spanning 18 months. In Phase 1, experts in medical education and technology-enhanced learning identified and validated components of online learning environments through a scoping review and a modified e-Delphi study. Phase 2 involved undergraduate medical students and faculty members for content validation, response process validation, and refinement of Digi-MEE instrument versions v.1.0 and v.2.0. Content validity of the questionnaire items as assessed through expert reviews (S-CVI/Ave: 0.98), and response process validity through participant feedback, (S-FVI/Ave: 0.87). EFA of Digi-MEE v.3.0 revealed 9 components with 46 items, explaining 57.18% of the variance, with high internal consistency. The number of items per component were as follows: Cognitive Enhancement (5 items), Content Curation (6 items), Cybergogical Practices (6 items), Learner Characteristics (7 items), Digital Capability (4 items), Platform Usability (4 items), Facilitation Dynamics (5 items), Social Interactions (4 items), and Institutional Support (5 items). The internal consistency for these components was high, with a Cronbach’s alpha of 0.952. Factor loadings ranged from 0.404 to 0.721 for each component, indicating strong associations within each factor.CFA of the final 28-item Digi-MEE instrument showed good model fit indices, excellent internal consistency (Cronbach’s alpha: 0.952), and acceptable composite reliability (0.7). Key indices include a chi-square/df ratio of 2.4, RMR of 0.03, and GFI of 0.902. CFI and TLI are strong at 0.926 and 0.91, respectively, while RMSEA is 0.057, indicating a close fit. In Phase 3, a usability study involving a cross-sectional survey and phenomenology study validated the practical utility of the Digi-MEE instrument. In Phase 3 of the study, the usability of the Digi-MEE instrument was evaluated using a cross-sectional survey and phenomenological study, validating its practical utility. In Phase 3, the usability of the Digi-MEE instrument was validated through a survey and phenomenology study, with participants rating their online learning environment at 2.95 ± 0.84 out of 4. Findings highlighted issues with content quality, engagement, and interactivity. Recommendations included improving digital skills training, personalized feedback, and platform communication, alongside better self-regulation support and more effective applications.These insights led to a list of recommendations for the university. The findings confirmed that the Digi-MEE instrument is a useful and straightforward tool for evaluating online learning environments in medical education, guiding improvements in various components based on the results. |
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