Embedding a Learning Management System Into an Undergraduate Medical Informatics Course in Saudi Arabia: Lessons Learned
Bibliographic record
Abstract
BACKGROUND: Public universities in Saudi Arabia today are making substantial investments in e-learning as part of their educational system, especially in the implementation of learning management systems (LMS). To our knowledge, this is the first study conducted in Saudi Arabia exploring medical students' experience with an LMS, particularly as part of a medical informatics course. OBJECTIVE: This study investigates students' use of various features of the LMS embedded in a recently implemented medical informatics course. METHODS: A mixed methodology approach was employed. Survey questionnaires were distributed to all third year medical informatics students at the end of the course. In addition, two focus group sessions were conducted with twelve students. A thematic analysis of the focus group was performed. RESULTS: A total of 265 third year medical student surveys (167/265, 63% male and 98/265, 37% female) were completed and analyzed. Overall, 50.6% (134/265) of the students agreed that the course was well planned and up-to-date, had clearly stated objectives and clear evaluation methods, appropriate course assignment, and that the LMS offered easy navigation. Most of the students rated the course as good/fair overall. In general, females were 10.4% more likely to prefer the LMS, as revealed by higher odd ratios (odds ratio [OR] 1.104, 95% CI 0.86-1.42) compared to males. Survey results showed that students' use of LMS tools increased after taking the course compared to before taking the course. The full model containing all items were statistically significant (χ(2) 25=69.52, P<.001, n=243), indicating that the model was able to distinguish between students who had positive attitudes towards LMS and those who did not. The focus group, however, revealed that the students used social networking for general use rather than learning purposes, but they were using other Internet resources and mobile devices for learning. Male students showed a higher preference for using technology in general to enhance learning activities. Overall, medical student attitudes towards the LMS were generally positive. Students also wanted a reminder and notification tool to help them stay updated with course events. Interestingly, a subset of students had been running a parallel LMS of their own that has features worth exploring and could be integrated with an official LMS in the future. CONCLUSIONS: To our knowledge, this was the first time that an LMS was used in a medical informatics course. Students showed interest in adapting various LMS tools to enhance their learning and gained more knowledge through familiarity with the tool. Researching an official LMS also revealed the existence of a parallel student-created LMS. This could allow teacher-led and student-led platforms to be integrated in the future for an enhanced student-centered experience.
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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 | 0.003 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".