Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

This paper proposes the OKCE (Optimal Knowledge Component Extracting) model that combines Elastic Net, Random Forest, and deep knowledge tracing (DKT) to discover relationships among knowledge concepts in educational curricula, generating knowledge-concept graphs to support adaptive and personalized learning paths for students.

As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for

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