Unpacking Situationality in Personalized Learning: Addressing Prejudices Through Reified Cloud Ops

Potential Abstract: This paper investigates the intersection of personalized learning, prejudices, and cloud operations within educational contexts. Drawing upon critical theories of education, we examine how the reification of technology in personalized learning environments can perpetuate and potentially disrupt existing prejudices. By focusing on the situationality of personalized learning experiences, we explore the ways in which technological interventions shape and mediate educational interactions. Specifically, we analyze the implications of cloud operations in delivering personalized content and the potential for these technologies to reinforce or challenge biases in teaching and learning.

Through a critical lens, this research aims to uncover the complexities of personalized learning practices and their impact on educational equity. By interrogating the ways in which biases are embedded within personalized learning technologies, we seek to illuminate the power dynamics at play in shaping educational experiences. Ultimately, this study contributes to a deeper understanding of how personalized learning can both replicate and challenge prejudices in educational settings, offering insights for educators, policymakers, and technology developers.

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