Social Presence in Two Massive Open Online Courses (MOOCs): A Multiple Case Study
Notice bibliographique
Résumé
The purpose of this study was to explore the role social presence plays within two Massive Open Online Courses (MOOCs) offered by two American institutions of higher education through the Canvas and Ed.X learning software consortia. Social presence is one of three presences that comprise Garrison and colleagues’ Community of Inquiry (CoI) conceptual framework (Garrison, Anderson & Archer, 2000; Garrison, 2013). Descriptive multiple case study methodology was used for the study, with data collected through surveys, individual interviews, focus group interviews, and discussion board postings. Findings show that, while participants in MOOCs felt comfortable expressing themselves “as real people” (a key indicator of social presence), the majority did not view themselves as being part of a community of learners within their respective courses. Overall, in both MOOCs, participants experienced social presence least among the three CoI presences. Participants in both MOOCs experienced social presence as it helped them to realize learning objectives (i.e., to successfully complete their respective courses). Social presence played a supportive role to cognitive presence. Factors affecting social presence included participants’ ability and/or willingness to direct their own learning, types of available technology, availability of time, and depth of course content. There were three implications for practice for MOOC designers and facilitators. The first implication is that leveraging students’ personal interests through course activities and content can help enhance social presence. The second implication is that making more varied use of the features and functionality of learning management software can afford students additional and better opportunities for social interaction. The third implication is that encouraging greater amounts and quality of collaboration through the design of assignments and other assessment and evaluation items can lead to improved social presence, and an enhanced educational experience overall. Further MOOC research should address different kinds of MOOCs than were studied as part of this research, and a greater number of MOOCs, and using different research methodologies, and including greater amounts of MOOC designer and instructor perspectives. Further research on different elements of the CoI model and the areas of overlap among the three CoI presences within MOOCs is also warranted.
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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 | 0,000 | 0,000 |
| 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 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,003 | 0,001 |
| 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 ».