Technology Integration in Syrian Medical Education From the Perspective of Students and Faculty: A Cross-Sectional Evaluation
Notice bibliographique
Résumé
Background: Technology-enhanced learning (TEL) has become increasingly vital in global medical education, offering significant advantages in knowledge acquisition, communication, motivation, and student engagement. In Syria, a country facing prolonged crises, there is an urgent need to evaluate the integration of technology within medical education to address institutional limitations and support student learning. Objective: The aim of this study is to evaluate the awareness, perceived challenges, and needs regarding the integration of technology in medical education from the perspectives of students and faculty at Syrian medical colleges. Methods: A cross-sectional survey was conducted during the 2023-2024 academic year across Syrian universities. Stratified random sampling was used to recruit 500 medical students and 200 faculty members. Two tailored, self-administered questionnaires were used, covering motivation, perceived benefits, challenges, and suggestions for technology integration. Validity was assessed through expert review and pilot testing (n=30), and internal consistency was confirmed (Cronbach α=0.6-0.7). Quantitative data were analyzed using descriptive statistics, t tests, ANOVA, and Kruskal-Wallis tests. Results: Among medical students, 94% (470\500) agreed that integrating technology into medical education is essential, with similar agreement from 93.5% (187\200) of faculty. No significant differences were found in student responses based on specialization (P=.32) or university type (P=.11). Likewise, faculty perspectives did not significantly differ by academic qualification or years of experience (P>.05). There were several perceived benefits; for instance, 93.2% (n=466) of students reported that technology kept them up to date with new developments, 88% stated it enhanced research skills, and 86.8% found TEL more enjoyable than traditional learning methods. Most respondents (95% n=475) said TEL created a flexible, interactive environment. Among faculty, 77% (n=154) agreed TEL improves clinical skill development. Respondents noted there were some challenges; specifically, 57% (n=285) of students cited poor internet service, 33% (n=165) noted the financial burden, and 82.2% (n=411) called for behavioral guidelines. Among faculty, 85.5% (n=171) cited lack of institutional support and 90% (n=180) emphasized the need for training. Both groups supported the development of communication platforms, curriculum revisions, and faculty development programs. Conclusions: There is a strong consensus among Syrian medical students and faculty on the value and necessity of integrating technology in medical education. Despite infrastructure and administrative challenges, both groups recognize TEL as a powerful tool for improving clinical competencies, student motivation, and academic engagement. Institutional commitment, curricular reform, and tailored training are essential to achieving sustainable, effective technology integration.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».