An Efficient Data Mining Approach to Concept Map Generation for Adaptive Learning

Data mining has recently drawn an increasing interest as an effective approach to generation of a concept map in an adaptive learning platform that provides students with personalized learning guidance. Although it has seen significant progresses, the data mining-based concept map generation needs to be further improved both in complexity and accuracy for wide acceptance in actual education services. This paper aims to improve the accuracy of concept map by considering both wrong-to-wrong and correct-to-correct relationships of questions, and by adopting more accurate formulas in calculation of relevance degrees between concepts. Through simulations using a set of concepts, questions, and student test records sampled from a practical courseware, we show that the proposed approach can generate a more accurate and robust concept map at an acceptable additional complexity.