Pharmacy Students’ Attitudes Toward Distance Learning After the COVID-19 Pandemic: Cross-Sectional Study From Saudi Arabia
Notice bibliographique
Résumé
BACKGROUND: Electronic learning refers to the use of assistive tools in offline and distance learning environments. It allows students to access learning tools and materials anytime and anywhere. However, distance learning courses depend on several factors that affect the quality of learning, which consequently affect students' preferences in the settings and tools used to deliver educational materials. OBJECTIVE: This study aimed to evaluate students' preferences for continuing distance learning after the pandemic and to assess the distance educational environment after the pandemic. It also aimed to identify the factors affecting distance learning and evaluate students' preferences regarding modes of communication with instructors. METHODS: A web-based survey was used to conduct this cross-sectional study. The target participants of this study were students in the doctor of pharmacy program at Unaizah College of Pharmacy, Qassim, Saudi Arabia. All students enrolled from December 2022 to January 2023 received an invitation with a link to the web-based survey. RESULTS: The survey was completed by 141 students (58 female students and 83 male students). The research results showed that most students (102/141, 72.3%) did not wish to continue distance education for laboratory courses, and 60.3% (85/141) did not wish to continue taking distance team-based learning after the pandemic. Additionally, 83.7% (118/141) of the students indicated that distance courses were simple. More than half of the participants (79/141, 56%) stated that having a camera on during class negatively impacted their learning, and only 29.1% (41/141) of the students stated that nonvisual communication with their fellow students impacted their learning. A large proportion of students (83/141, 58.9%) reported impairment of social engagement on campus, 44% (62/141) in-person interactions during classes, and 73.7% (104/141) were relieved that their classes were not disrupted. CONCLUSIONS: Similar to all types of education, distance learning is characterized by advantages and disadvantages, as reported by students. Students felt that the course material was intelligible, and the distance course was uncomplicated. Moreover, they expressed relief that their studies were not disrupted. However, they also reported the loss of face-to-face contact during courses as the most significant drawback of distance learning versus face-to-face learning, followed by a lack of social connection on campus.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».