Constructing personal concept map automatically via Correlative Test-Items Structure

Concept map model has been widely used in e-learning for various applications. However, in the past researches, there are few attentions paid on constructing the personal concept map for diagnosing learner´s learning status. Actually, it is difficult to construct the individual concept maps to reflect the real knowledge structure by learners themselves. To cope with this problem, this study proposes an approach based on Correlative Test-Items Structure to construct the personal concept map automatically. Firstly, according to the standard concept map from expert, questions for examination to test learners´ abilities are formulated. After collecting individual learner´s answers, an algorithm based on association rule is used to construct the personal concept map automatically, including the learning degree of each interrelated concept and independent concept. Finally, comparing with the standard concept map of expert, a near-optimal guidance learning path for adaptive learning is derived.