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http://dx.doi.org/10.14697/jkase.2022.42.6.611

Analysis of Characteristics of Clusters of Middle School Students Using K-Means Cluster Analysis  

Jaebong, Lee (Korea Institute for Curriculum and Evaluation)
Publication Information
Journal of The Korean Association For Science Education / v.42, no.6, 2022 , pp. 611-619 More about this Journal
Abstract
The purpose of this study is to explore the possibility of applying big data analysis to provide appropriate feedback to students using evaluation data in science education at a time when interest in educational data mining has recently increased in education. In this study, we use the evaluation data of 2,576 students who took 24 questions of the national assessment of educational achievement. And we use K-means cluster analysis as a method of unsupervised machine learning for clustering. As a result of clustering, students were divided into six clusters. The middle-ranking students are divided into various clusters when compared to upper or lower ranks. According to the results of the cluster analysis, the most important factor influencing clusterization is academic achievement, and each cluster shows different characteristics in terms of content domains, subject competencies, and affective characteristics. Learning motivation is important among the affective domains in the lower-ranking achievement cluster, and scientific inquiry and problem-solving competency, as well as scientific communication competency have a major influence in terms of subject competencies. In the content domain, achievement of motion and energy and matter are important factors to distinguish the characteristics of the cluster. As a result, we can provide students with customized feedback for learning based on the characteristics of each cluster. We discuss implications of these results for science education, such as the possibility of using this study results, balanced learning by content domains, enhancement of subject competency, and improvement of scientific attitude.
Keywords
National Assessment of Educational Achievement (NAEA); cluster analysis; academic achievement; customized feedback;
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Times Cited By KSCI : 6  (Citation Analysis)
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