Analysis of clustering evaluation considering features of item response data using data mining technique for setting cut-off scores

Byoungwook Kim, Ja Mee Kim, Gangman Yi

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

The setting of standards is a critical process in educational evaluation, but it is time-consuming and expensive because it is generally conducted by an education experts group. The purpose of this paper is to find a suitable cluster validity index that considers the futures of item response data for setting cut-off scores. In this study, nine representative cluster validity indexes were used to evaluate the clustering results. Cohen's kappa coefficient is used to check the conformity between a set cut-off score using four clustering techniques and a cut-off score set by experts. We compared the cut-off scores by each cluster validity index and by a group of experts. The experimental results show that the entropy-based method considers the features of item response data, so it has a realistic possibility of applying a clustering evaluation method to the setting of standards in criterion referenced evaluation.

Original languageEnglish
Article number62
JournalSymmetry
Volume9
Issue number5
DOIs
Publication statusPublished - 2017 May 1

Fingerprint

data mining
Cluster Validity Index
Data mining
Data Mining
cut-off
Clustering
evaluation
Evaluation
Entropy
Education
Cohen's kappa
Cutset
Evaluation Method
Clustering Methods
education
entropy
Evaluate
Experimental Results
Coefficient
coefficients

Keywords

  • Clustering data mining
  • Cut-off scores
  • Item response data

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Chemistry (miscellaneous)
  • Mathematics(all)
  • Physics and Astronomy (miscellaneous)

Cite this

Analysis of clustering evaluation considering features of item response data using data mining technique for setting cut-off scores. / Kim, Byoungwook; Kim, Ja Mee; Yi, Gangman.

In: Symmetry, Vol. 9, No. 5, 62, 01.05.2017.

Research output: Contribution to journalArticle

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