Explainable Deep Learning Framework for Understanding Distractions for Students

Abstract

Student productivity is influenced by digital, behavioral, environmental, and psychological distractions. This paper proposes an Explainable Deep Learning Framework (XDLF) integrating LSTM, SHAP, and LIME to predict productivity and explain influential factors. Experimental evaluation demonstrates superior performance compared with traditional machine learning models.

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