Ethnocentric Aesthetics in Educational Data Mining: A Commodified Analysis

Potential Abstract:
In this study, we explore the notion of ethnocentric aesthetics within the context of educational data mining, using a commodified analysis framework. The increasing reliance on data-driven decision-making in education has led to concerns about how ethnocentric biases may be perpetuated and reinforced through the aesthetic choices made in the design and implementation of data mining tools and algorithms. Drawing on critical theories of race, culture, and technology, we examine how these biases manifest in the way educational data is collected, analyzed, and used to inform instructional practices. Through a series of case studies and qualitative analyses, we uncover the ways in which ethnocentric aesthetics shape the outcomes of educational data mining processes and contribute to the reproduction of inequality and injustice in educational systems. Our findings highlight the need for greater attention to the ethical dimensions of data mining in education, and call for a more critical and reflexive approach to the design and implementation of data-driven interventions in diverse educational settings.

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