Unpacking Adaptive Learning Analytics: Examining Uncontested Constructs and Transgressions

Potential Abstract:
Adaptive learning analytics present a promising avenue for optimizing educational outcomes by tailoring instruction to individual learners. Despite the potential benefits, the field of adaptive learning is rife with contested constructs and unexplored transgressions. This study aims to unpack the complexities surrounding adaptive learning analytics by delving into the uncontestable constructs that underpin their design and implementation, while also investigating instances where these systems may inadvertently perpetuate biases or ethical dilemmas. Through a mixed-methods approach, we will analyze student data from a diverse range of educational settings to identify patterns of adaptation, effectiveness of personalized learning interventions, and implications for equity and inclusion. Our findings will contribute to the ongoing discourse on the ethical use of adaptive learning technologies and inform the design of more equitable and effective learning environments.

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