How an Environment of Stress and Social Risk Shapes Student Engagement With Social Media as Potential Digital Learning Platforms: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Social media has been increasingly used as a learning tool in medical education. Specifically, when joining university, students often go through a phase of adjustment, and they need to cope with various challenges such as leaving their families and friends and trying to fit into a new environment. Research has shown that social media helps students to connect with old friends and to establish new relationships. However, managing friendships on social media might intertwine with the new learning environment that shapes students' online behaviors. Especially, when students perceive high levels of social risks when using social media, they may struggle to take advantage of the benefits that social media can provide for learning. OBJECTIVE: This study aimed to develop a model that explores the drivers and inhibitors of student engagement with social media during their university adjustment phase. METHODS: We used a qualitative method by interviewing 78 undergraduate students studying medical courses at UK research-focused universities. In addition, we interviewed 6 digital technology experts to provide additional insights into students' learning behaviors on social media. RESULTS: Students' changing relationships and new academic environment in the university adjustment phase led to various factors that affected their social media engagement. The main drivers of social media engagement were maintaining existing relationships, building new relationships, and seeking academic support. Simultaneously, critical factors that inhibited the use of social media for learning emerged, namely, collapsed online identity, uncertain group norms, the desire to present an ideal self, and academic competition. These inhibitors led to student stress when managing their social media accounts, discouraged them from actively engaging on social media, and prevented the full exploitation of social media as an effective learning tool. CONCLUSIONS: This study identified important drivers and inhibitors for students to engage with social media platforms as learning tools. Although social media supported students to manage their relationships and support their learning, the interaction of critical factors, such as collapsed online identity, uncertain group norms, the desire to present an ideal self, and academic competition, caused psychological stress and impeded student engagement. Future research should explore how these inhibitors can be removed to reduce students' stress and to increase the use of social media for learning. More specifically, such insights will allow students to take full advantage of being connected, thus facilitating a richer learning experience during their university life.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».