Personalized Educational Knowledge Tracing using Graph Attention (GAT)
Predictive model modeling prerequisite concept graphs to estimate a student's mastery level and recommend optimal practice questions.
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Project Technical Overview
Models university syllabus curricula as prerequisite knowledge graphs. Uses Graph Attention Networks (GAT) to analyze students' historical quiz response sequences, predicting mastery gaps and generating personalized learning roadmaps to maximize retention.
Read the foundational IEEE conference research paper, citations, and algorithmic architecture for this project.