Abstracts
Abstract
Connectivism, a learning theory that reveals a new learning in the Internet environment, has become a popular academic topic at the forefront of online learning. The MOOC Research Team at the Distance Education Research Centre at Beijing Normal University designed and developed the first massive open online course in China, adapting a connectivist (cMOOC) approach. Using the data collected from six offerings of the cMOOC over 3 years, the big data paradigm was used for data analysis including complex network analysis, content analysis, text mining, behaviour sequence analysis, epistemic network analysis, and statistical and econometric models. This paper summarizes the findings of the patterns of connectivist learning, including a) the basic characteristics and evolutional patterns of complex networks, b) the characteristics and modes of knowledge production, c) the patterns of instructional interactions, and d) the relationships between “pipe” (connection) and content and between facilitators and learners. It is expected that the outcome of this study could make contributions to understanding the changes of online learning in depth and further promote the theoretical development and practical application of a connectivist approach.
Keywords:
- Connectivism,
- Online Learning,
- Principles,
- Innovative Application,
- Technology and learning
Résumé
En tant que théorie d'apprentissage qui révèle un nouvel apprentissage dans l'environnement internet, le connectivisme est devenu un sujet académique populaire à la pointe de l'apprentissage en ligne. L'équipe de recherche MOOC du Centre de Recherche sur l'Enseignement à Distance de l'Université Normale de Pékin a conçu et développé le premier cours en ligne ouvert et massif, adaptant une approche connectiviste (cMOOC) en Chine. À partir des données recueillies dans le cadre de six offres du cMOOC sur une période de trois ans, le paradigme du big data a été utilisé pour l'analyse des données, y compris l'analyse de réseaux complexes, l'analyse de contenu, l'exploration de textes, l'analyse de séquences de comportements, l'analyse de réseaux épistémiques et les modèles statistiques et économétriques. Cet article résume les résultats des modèles d'apprentissage connectiviste, incluant a) les caractéristiques de base et les modèles d'évolution des réseaux complexes, b) les caractéristiques et les modes de production de connaissances, c) les modèles d'interactions pédagogiques, et d) les relations entre le tuyau d’information et le contenu et entre les facilitateurs et les apprenants. On s'attend à ce que les résultats de cette étude puissent contribuer à une compréhension approfondie des changements de l'apprentissage en ligne et promouvoir davantage le développement théorique et l'application pratique d'une approche connectiviste.
Mots-clés :
- Connectivisme,
- application innovante,
- apprentissage en ligne,
- technologie et apprentissage
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Appendices
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