This study explores the potential of using learning analytics to support the development of poststructural schema in ill-structured learning environments. Ill-structured tasks, characterized by ambiguity and complex problem-solving, pose challenges to learners in constructing meaningful connections between diverse knowledge domains. By leveraging learning analytics, which involve the analysis of learner interactions and patterns in digital learning environments, this research aims to uncover underlying patterns and dynamics in the process of schema development.
Drawing on poststructural theory, which highlights the socially constructed nature of knowledge and the importance of multiple perspectives, this study proposes a novel framework for analyzing learner interactions within ill-structured networks. The framework emphasizes the interplay between multiple knowledge domains, highlighting the dynamic nature of schema development. By applying network analysis techniques to learning analytics data, we aim to identify key nodes, bridges, and network structures that contribute to the construction and transformation of schema.
To investigate the potential of this framework, an experimental study will be conducted with a diverse group of learners engaging in ill-structured problem-solving tasks within a digital learning environment. Learning analytics data, including learner interactions, contributions, and feedback, will be collected and analyzed using network analysis methods. The findings of this study will shed light on the complex interplay between ill-structured tasks, learning analytics, and poststructural schema development.
The implications of this research are significant for both educators and researchers. By uncovering the underlying patterns and dynamics of schema development in ill-structured networks, this study aims to provide a deeper understanding of how learners construct meaning in complex learning environments. Moreover, the application of learning analytics within this context has the potential to inform the design of more effective educational interventions and personalized learning experiences.
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