Embedding a Learning Management System Into an Undergraduate Medical Informatics Course in Saudi Arabia: Lessons Learned
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».