Applying Genetic Algorithm to Generation of High-Dimensional Item Response Data
- Authors
- Kim, ByoungWook; Kim, JaMee; Lee, WonGyu
- Issue Date
- 2015
- Publisher
- HINDAWI LTD
- Citation
- MATHEMATICAL PROBLEMS IN ENGINEERING, v.2015
- Indexed
- SCIE
SCOPUS
- Journal Title
- MATHEMATICAL PROBLEMS IN ENGINEERING
- Volume
- 2015
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/96413
- DOI
- 10.1155/2015/589317
- ISSN
- 1024-123X
- Abstract
- The item response data is the m-dimensional data based on the responses made by m examinees to the questionnaire consisting of.. items. It is used to estimate the ability of examinees and item parameters in educational evaluation. For estimates to be valid, the simulation input data must reflect reality. This paper presents the effective combination of the genetic algorithm (GA) and Monte Carlo methods for the generation of item response data as simulation input data similar to real data. To this end, we generated four types of item response data using Monte Carlo and the GA and evaluated how similarly the generated item response data represents the real item response data with the item parameters (item difficulty and discrimination). We adopt two types of measurement, which are root mean square error and Kullback-Leibler divergence, for comparison of item parameters between real data and four types of generated data. The results show that applying the GA to initial population generated by Monte Carlo is the most effective in generating item response data that is most similar to real item response data. This study is meaningful in that we found that the GA contributes to the generation of more realistic simulation input data.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School of Education > Computer Science Education > 1. Journal Articles
- Graduate School > Department of Computer Science and Engineering > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholar.korea.ac.kr/handle/2021.sw.korea/96413)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.