Enhancing Personalized Learning through Engineered Social Network Analysis of Epistemological Representations

Potential Abstract:

Abstract: With the growing interest in personalized learning, there is a need to understand how students’ epistemologies can be leveraged to create tailored educational experiences. This study explores the potential of using social network analysis (SNA) to engineer personalized learning environments based on students’ epistemological representations. By analyzing the patterns of knowledge interaction within social networks, we aim to develop a deeper understanding of the relationship between individual epistemologies and collaborative learning dynamics.

To achieve this, we propose a mixed-methods research design that integrates qualitative and quantitative approaches. Initially, we will conduct semi-structured interviews to elicit students’ epistemological beliefs and their understanding of knowledge representation. Subsequently, we will gather data from online collaborative learning environments, capturing students’ interactions and knowledge contributions. By employing SNA techniques, we will analyze these data to identify the underlying social structures and network properties that emerge from students’ epistemological representations.

The study will be conducted in a secondary school setting, involving a diverse group of students from different backgrounds and academic levels. The data collected will provide insights into the dynamics of knowledge sharing and construction within social networks, and how these dynamics are influenced by students’ epistemological beliefs. Additionally, we will investigate the impact of personalized learning experiences on students’ epistemological development and academic outcomes.

The findings of this study will contribute to both the field of education and artificial intelligence. By integrating SNA and epistemology, we aim to provide novel insights into how personalized learning environments can be engineered to support students’ diverse epistemological orientations. Furthermore, the study will inform the development of intelligent tutoring systems that can adapt to individual epistemological trajectories, promoting deeper understanding and enhancing collaborative learning experiences.

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