Analyzing the Role of Group Interactions in Deep Learning: Connectivity Patterns and Self-Regulated Learning in University Discussion Forums
Analyzing the Role of Group Interactions in Deep Learning: Connectivity Patterns and Self-Regulated Learning in University Discussion Forums

Summary/Abstract: As online education continues to gain popularity, it is crucial to analyze the role of group interactions in facilitating individual deep learning. This study explored the connection structure of online discussion forum messages shared by university students. To elucidate the interrelationship between intrapersonal and interpersonal forms of learning in an online environment, messages were classified according to behavioral indicators, elements of self-regulated learning, and types of threshold concepts. To this end, exponential random graph models were employed to reveal the connectivity patterns. A total of 24 messages containing threshold concepts were identified. Notably, these threshold concepts were closely associated with self-reflection in the context of self-regulated learning. Homophily in connections was evident in the metrics pertaining to message content. Messages containing threshold concepts were distributed throughout the community without any noticeable clustering. The diversity of information available within the community highlights the students’ propensity to access personally meaningful information. The community structure did not include an aggregated connection, which is reminiscent of the structure of social networking services. In contrast, the network exhibited a paired-connection structure that was highly conducive to explaining connections to academic content, thereby reinforcing conceptual transformations.

• Details
• Contents