Situated Mindsets in Educational Data Mining: Understanding Reified Milieu

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.

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