Algorithmic Frames and Perception-Centered Inference in Education

Potential Abstract:
Algorithmic decision-making tools are increasingly being utilized in education to support various processes such as student assessment, personalized learning, and administrative decision-making. However, the design and implementation of these algorithms can perpetuate biases, reinforce existing inequalities, and shape perceptions of students and educational practices. This study investigates the role of algorithmic frames in shaping educators’ perceptions and inferences about student performance and behavior. Specifically, we examine how the framing of algorithmic outputs influences educators’ interpretations and subsequent decision-making processes in educational settings.

Using a mixed-methods approach, we analyze how educators make sense of algorithmic recommendations and predictions within the context of their professional practices. Through interviews, surveys, and observations, we explore the cognitive processes involved in educators’ perception-centered inferences based on algorithmic information. By unpacking the underlying mechanisms through which algorithmic frames influence educators’ judgments and actions, this research contributes to a deeper understanding of the implications of algorithmic decision-making in education.

The findings of this study have important implications for educational policymakers, administrators, and technology developers seeking to enhance the effectiveness and fairness of algorithmic tools in educational settings. By shedding light on the ways in which algorithmic frames shape educators’ perceptions and inferences, this research aims to inform the design of algorithmic systems that support, rather than hinder, equitable and student-centered educational practices.

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