Potential Abstract:
Understanding how students learn and interact within educational settings is crucial for designing effective teaching strategies. Social network analysis provides a valuable lens through which to examine these learning patterns. This study applies a descriptive social network analysis framework to investigate the dynamics of student interactions and knowledge sharing in a Keynesian classroom environment. By analyzing the connections and interactions among students, we aim to uncover patterns of collaboration, information flow, and knowledge acquisition. Through this analysis, we seek to identify key individuals who play pivotal roles in shaping the learning network and influencing the overall learning outcomes.
The study employs a mixed-methods approach, combining quantitative network analysis techniques with qualitative data from student interviews and observations. By integrating these multiple sources of data, we aim to provide a comprehensive understanding of how social networks influence learning processes in educational settings. The findings from this research have the potential to inform educational practitioners and policymakers on how to optimize learning environments to foster collaborative learning and knowledge sharing among students.
Potential References:
- Using social network analysis to study the knowledge sharing patterns of health professionals using web 2.0 tools
- Social network analysis to examine interaction patterns in knowledge building communities
- Social learning network analysis model to identify learning patterns using ontology clustering techniques and meaningful learning
- Knowledge sharing over social networking systems: Architecture, usage patterns and their application
- Interaction patterns determining improved information and knowledge sharing among smallholder farmers
