Advertisement

The Research of Mining Association Rules Between Personality and Behavior of Learner Under Web-Based Learning Environment

  • Jin Du
  • Qinghua Zheng
  • Haifei Li
  • Wenbin Yuan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3583)

Abstract

Discovering the relationship between behavior and personality of learner in the web-based learning environment is a key to guide learners in the learning process. This paper proposes a new concept called personality mining to find the “deep” personality through the observed data about the behavior. First, a learner model which includes personality model and behavior model is proposed. Second, we have designed and implemented an improved algorithm, which is based on Apriori algorithm widely used in market basket analysis, to identify the relationship. Third, we have discussed various issues like constructing the learner model, unifying the value domain of heterogeneous model attributes, and improving Apriori algorithm with decision domain. Experiment result indicated that this algorithm for mining association rules between behavior and personality is feasible and efficient. The algorithm has been used in a web-based learning environment developed at Xi’an Jiaotong University.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    IEEE Learning Technology Standards Committee (LTSC). IEEE 1484.2, PAPI Learner Model Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. In: Proceedings of 1994 International Conference on Very Large Databases, Santiago, Chile, pp. 487–499 (1994)Google Scholar
  3. 3.
    Vance Wilson, E.: Student characteristics and computer-mediated communication. Computers & Education 34, 67–76 (2000)CrossRefGoogle Scholar
  4. 4.
    Kim, E.B.: The role of personality in Web-based distance education courses. Communications of the ACM 47(3) (March 2004)Google Scholar
  5. 5.
    Carey, J.M., Kacmar, C.: The Impact of Communication Mode and Task Complexity on Small Group Performance and Member Satisfaction. Computers in Human Behavior 13(1), 23–49 Google Scholar
  6. 6.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan kaufamnn Publishers, San Francisco (2001)Google Scholar
  7. 7.
    Jun, L., Renhou, L., Qinghua, Z.: Study on the Personality Mining Method for Learners in Network Learning. Academic Journal of Xi’an Jiaotong University 38(6) (2004) (EI indexed, AN: 04358329828) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jin Du
    • 1
  • Qinghua Zheng
    • 1
  • Haifei Li
    • 2
  • Wenbin Yuan
    • 1
  1. 1.Department of Computer ScienceXi’an Jiaotong UniversityXi’anP. R. China
  2. 2.Department of Mathematics and Computer Science at Union UniversityJacksonU.S.A

Personalised recommendations