Potential Abstract: This research article explores the intersection of educational data mining and activity theory in the context of reifying metalogues. By employing eigenvectors as a mathematical tool for analyzing educational data, we aim to enhance our understanding of the complex interactions within educational settings. Drawing on principles of activity theory, we seek to uncover the underlying structures and relationships that shape the learning process. Through the reification of metalogues, we aim to make visible the often implicit and dynamic aspects of educational interactions, shedding light on the interconnectedness of various elements in the educational ecosystem. Our study utilizes a mixed-methods approach, combining quantitative analysis of educational data with qualitative interpretation informed by activity theory frameworks. The results of this study have the potential to inform instructional design, educational policy, and research methodologies, offering a deeper understanding of the factors influencing learning outcomes.
Potential References:
- An empirical development of critical value factors (CVF) of online learning activities: An application of activity theory and cognitive value theory
- An activity-theoretical approach to investigate learners’ factors toward e-learning systems
- Establishment of college English teachers’ teaching ability evaluation based on Clementine data mining
- Tools for educational data mining: A review
- Model-based collaborative filtering analysis of student response data: Machine-learning item response theory.
