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Closed-Ended Questionnaire Data Analysis

  • Leuo-Hong Wang
  • Chao-Fu Hong
  • Chia-Ling Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

Abstract

A KeyGraph-like algorithm, which incorporates the concept of structural importance with association rules mining, for analyzing closed-ended questionnaire data is presented in this paper. The proposed algorithm transforms the questionnaire data into a directed graph, and then applies association rules mining and clustering procedures, whose parameters are determined by gradient sensitivity analysis, as well as correlation analysis in turn to the graph. As a result, both statistically significant and other cryptic events are successfully unveiled. A questionnaire survey data from an instructional design application has been analyzed by the proposed algorithm. Comparing to the results of statistical methods, which elicited almost no information, the proposed algorithm successfully identified three cryptic events and provided five different strategies for designing instructional activities. The preliminary experimental results indicated that the algorithm works out for analyzing closed-ended questionnaire survey data.

Keywords

Association Rule Instructional Activity Chance Discovery Preliminary Experimental Result Structural Importance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Leuo-Hong Wang
    • 1
  • Chao-Fu Hong
    • 1
  • Chia-Ling Hsu
    • 2
  1. 1.Evolutionary Computation Laboratory, Department of Information ManagementAletheia UniversityTaiwan
  2. 2.Centre for Teacher EducationTamkang UniversityTaiwan

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