Leveraging Operant Boundaries for Educational Data Mining: Towards a Neoliberal Framework

Potential Abstract:
Abstract: This research article explores the intersection of operant boundaries, educational data mining (EDM), and the neoliberal framework in the context of education. As technology continues to advance, educational institutions are increasingly relying on data mining techniques to inform decision-making processes and enhance student learning experiences. However, the integration of EDM within a neoliberal framework raises important questions about the ethical implications, power dynamics, and potential consequences for students and educators.

Drawing on the theoretical foundations of operant conditioning and behavior analysis, this article proposes a novel framework that addresses the ethical concerns associated with EDM within a neoliberal context. By recognizing operant boundaries, which are the limits of control that should be respected in educational settings, this framework seeks to strike a balance between utilizing valuable data insights and upholding student autonomy and privacy.

The article reviews existing literature on operant boundaries in education and explores how they can be applied to EDM practices. It also examines the potential benefits and drawbacks of incorporating a neoliberal framework into educational data mining initiatives. The findings reveal that while EDM has the potential to provide valuable insights into student learning and promote personalized education, it also raises concerns about surveillance, data privacy, and the potential for perpetuating inequalities.

Based on the analysis of the literature, this article proposes several recommendations for educators, policymakers, and researchers regarding the ethical implementation of EDM within a neoliberal framework. These recommendations emphasize the importance of transparency, informed consent, and the establishment of clear boundaries to protect student autonomy and privacy. Furthermore, this article underscores the need for collaboration between stakeholders to develop policies and guidelines that ensure the responsible and ethical use of educational data mining techniques.

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