A new approach for constructing the concept map

  • Authors:

  • Shian-Shyong Tseng;Pei-Chi Sue;Jun-Ming Su;Jui-Feng Weng;Wen-Nung Tsai

  • Affiliations:

  • Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu, Taiwan 300, Taiwan and Department of Information Science and Applications, Asia University No. 500, Liuf ...;Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu, Taiwan 300, Taiwan;Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu, Taiwan 300, Taiwan;Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu, Taiwan 300, Taiwan;Department of Computer Science, National Chiao Tung University, 1001 Ta Hsueh Rd., Hsinchu, Taiwan 300, Taiwan

  • Venue:
  • Computers & Education
  • Year:
  • 2007

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Abstract

In recent years, e-learning system has become more and more popular and many adaptive learning environments have been proposed to offer learners customized courses in accordance with their aptitudes and learning results. For achieving the adaptive learning, a predefined concept map of a course is often used to provide adaptive learning guidance for learners. However, it is difficult and time consuming to create the concept map of a course. Thus, how to automatically create a concept map of a course becomes an interesting issue. In this paper, we propose a Two-Phase Concept Map Construction (TP-CMC) approach to automatically construct the concept map by learners' historical testing records. Phase 1 is used to preprocess the testing records; i.e., transform the numeric grade data, refine the testing records, and mine the association rules from input data. Phase 2 is used to transform the mined association rules into prerequisite relationships among learning concepts for creating the concept map. Therefore, in Phase 1, we apply Fuzzy Set Theory to transform the numeric testing records of learners into symbolic data, apply Education Theory to further refine it, and apply Data Mining approach to find its grade fuzzy association rules. Then, in Phase 2, based upon our observation in real learning situation, we use multiple rule types to further analyze the mined rules and then propose a heuristic algorithm to automatically construct the concept map. Finally, the Redundancy and Circularity of the concept map constructed are also discussed. Moreover, we also develop a prototype system of TP-CMC and then use the real testing records of students in junior high school to evaluate the results. The experimental results show that our proposed approach is workable.