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

This paper proposes the OKCE model that combines machine learning feature reduction (Elastic Net, Random Forest) with deep knowledge tracing to extract knowledge component relationships and complete knowledge-concept graphs in educational curricula. The approach aims to support adaptive and personalized learning by discovering hidden relationships among curriculum concepts based on student performance data.

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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