Potential Abstract:
This research study delves into the intersection of situated mindsets and educational data mining within the educational milieu. By examining how mindsets are situated and reified in the context of data-driven educational practices, this study aims to provide insights into the ways in which educational data mining can be leveraged to support the development of adaptive teaching strategies and personalized learning experiences. Drawing on theories of situated cognition and growth mindsets, this study explores how students’ beliefs and attitudes towards their own learning can be reflected in the data generated from educational technologies. Through the analysis of large-scale educational datasets, this research seeks to uncover patterns and correlations between students’ mindsets, their engagement with learning materials, and their academic performance. By understanding the complex interplay between situated mindsets and educational data mining, this study contributes to the ongoing discourse on the potential of data-driven approaches to inform pedagogical practices and enhance student outcomes in diverse educational settings.
Potential References:
- Data mining for providing a personalized learning path in creativity: An application of decision trees
- Focusing on the Campus Milieu: A Guide for Enhancing the Graduate School Climate.
- Industry mindsets: Exploring the cultures of two macro-organizational settings
- Foregrounding family: How Salvadoran American boys formulate college‐going mindsets at the nexus of family, school, and the self
- Investigating preservice teachers’ field-specific ability beliefs: Do they believe innate talent is essential for success in their subject?
