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http://dx.doi.org/10.9728/dcs.2018.19.9.1769

Predicting Learning Achievement Using Big Data Cluster Analysis - Focusing on Longitudinal Study  

Ko, Sujeong (Department of Computer Software, Induk University)
Publication Information
Journal of Digital Contents Society / v.19, no.9, 2018 , pp. 1769-1778 More about this Journal
Abstract
As the value of using Big Data is increasing, various researches are being carried out utilizing big data analysis technology in the field of education as well as corporations. In this paper, we propose a method to predict learning achievement using big data cluster analysis. In the proposed method, students in Korea Children and Youth Panel Survey(KCYPS) are classified into groups with similar learning habits using the Kmeans algorithm based on the learning habits of students of the first year at middle school, and group features are extracted. Next, using the extracted features of groups, the first grade students at the middle school in the test group were classified into groups having similar learning habits using the cosine similarity, and then the neighbors were selected and the learning achievement was predicted. The method proposed in this paper has proved that the learning habits at middle school are closely related to at the university, and they make it possible to predict the learning achievement at high school and the satisfaction with university and major.
Keywords
Big data; Clustering analysis; Cosine similarity; Kmeans algorithm; Predicting learning achievement;
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Times Cited By KSCI : 2  (Citation Analysis)
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