Unveiling Algorithmic Epistemologies in Learning Analytics: Reframing Naive Assumptions

Potential Abstract: This research article explores the intersection of algorithmic epistemologies and learning analytics in education, with a focus on reframing naive assumptions. As education increasingly relies on data-driven decision-making and the use of algorithms, it is crucial to critically examine the underlying epistemologies that inform these tools. Many learning analytics platforms and algorithms are built upon assumptions that may not be adequately interrogated, leading to potential biases, hidden assumptions, and reductionist perspectives. This study aims to uncover and reframe these naive assumptions, shedding light on the epistemological foundations of learning analytics.

Drawing on critical theory and a multi-method research approach, including literature review, discourse analysis, and case studies, this article critically examines the epistemological underpinnings of algorithmic approaches in learning analytics. By deconstructing prevalent assumptions and reframing them, we seek to contribute to a more nuanced understanding of the implications and limitations of algorithmic decision-making in education.

The findings highlight the importance of critically examining the epistemological assumptions embedded in learning analytics algorithms. By unpacking these assumptions, we can identify potential biases, uncover hidden perspectives, and challenge reductionist approaches that may hinder equitable educational practices. This reframing of naive assumptions provides an opportunity for educators, researchers, and policymakers to engage in more informed and critical discussions about the use of learning analytics in educational contexts.

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