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Analysis of Clustering Evaluation Considering Features of Item Response Data Using Data Mining Technique for Setting Cut-Off Scores

Authors
Kim, ByoungwookKim, JameeYi, Gangman
Issue Date
5월-2017
Publisher
MDPI
Keywords
clustering data mining; cut-off scores; item response data
Citation
SYMMETRY-BASEL, v.9, no.5
Indexed
SCIE
SCOPUS
Journal Title
SYMMETRY-BASEL
Volume
9
Number
5
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/83565
DOI
10.3390/sym9050062
ISSN
2073-8994
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.
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