Cyberincivility in the Massive Open Online Course Learning Environment: Data-Mining Study
Notice bibliographique
Résumé
BACKGROUND: Cyberincivility is a pervasive issue that demands upfront thinking and can negatively impact one's personal, professional, social, and educational well-being. Although massive open online courses (MOOCs) environments could be vulnerable to undesirable acts of incivility among students, no study has explored the phenomena of cyberincivility in this learning environment, particularly in a health-related course in which mostly current or eventual health professions students enroll. OBJECTIVE: This study aimed to analyze the characteristics of text entries posted by students enrolled in a medicine and health care MOOC. The objectives were to (1) examine the prevalence of posts deemed disrespectful, insensitive or disruptive, and inconducive to learning; (2) describe the patterns and types of uncivil posts; and (3) highlight aspects that could be useful for MOOC designers and educators to build a culture of cybercivility in the MOOC environment. METHODS: We obtained data from postings in the discussion forums from the MOOC Medical Neuroscience created by a large private university in the southeast region of the United States. After cleaning the dataset, 8705 posts were analyzed, which contained (1) 667 questions that received no responses; (2) 756 questions that received at least one answer; (3) 6921 responses that applied to 756 posts; and (4) 361 responses where the initiating post was unknown. An iterative process of coding, discussion, and revision was conducted to develop a series of a priori codes. Data management and analysis were performed with NVivo 12. RESULTS: Overall, 19 a priori codes were retained from 25 initially developed, and 3 themes emerged from the data-Annoyance, Disruption, and Aggression. Of 8705 posts included in the analysis, 7333 (84.24%) were considered as the absence of uncivil posts and 1043 (11.98%) as the presence of uncivil posts, while 329 (3.78%) were uncodable. Of 1043 uncivil posts analyzed, 466 were coded to >1 a priori codes, which resulted in 1509 instances. Of those 1509 instances, 826 (54.74%) fell into "annoyance", 648 (42.94%) into "disruption", and 35 (2.32%) into "aggression". Of 466 posts that related to >1 a priori codes, 380 were attributed to 2 or 3 themes. Of those 380 posts, 352 (92.6%) overlapped both "annoyance" and "disruption," 13 (3.4%) overlapped both "disruption" and "aggression," and 9 (2.4%) overlapped "annoyance" and "aggression," while 6 (1.6%) intersected all 3 themes. CONCLUSIONS: This study reports on the phenomena of cyberincivility in health-related MOOCs toward the education of future health care professionals. Despite the general view that discussion forums are a staple of the MOOC delivery system, students cite discussion forums as a source of frustration for their potential to contain uncivil posts. Therefore, MOOC developers and instructors should consider ways to maintain a civil discourse within discussion forums.
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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 | 0,007 | 0,003 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».