Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.1.88

Data Analysis of Dropouts of University Students Using Topic Modeling  

Jeong, Do-Heon (Department of Library and Information Science, Duksung Women's University)
Park, Ju-Yeon (Cha Mirisa College of Liberal Arts, Duksung Women's University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 88-95 More about this Journal
Abstract
This study aims to provide implications for establishing support policies for students by empirically analyzing data on university students dropouts. To this end, data of students enrolled in D University after 2017 were sampled and collected. The collected data was analyzed using topic modeling(LDA: Latent Dirichlet Allocation) technique, which is a probabilistic model based on text mining. As a result of the study, it was found that topics that were characteristic of dropout students were found, and the classification performance between groups through topics was also excellent. Based on these results, a specific educational support system was proposed to prevent dropout of university students. This study is meaningful in that it shows the use of text mining techniques in the education field and suggests an education policy based on data analysis.
Keywords
Dropouts; Topic modeling; LDA; Machine learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Y. Chung, M. S. Sun, and M. J. Jeong, "An Analysis of Institutional Factors Affecting on College Dropout Rates," Asian Journal of Education, vol. 16, no. 4, pp. 57-76, 2015.   DOI
2 M. Kang, E. Lee, and E. Lee, "Trends and influencing factors of college student's dropout intention," In Forum for Youth Culture, no. 58, pp. 5-30, 2019.
3 C. Park, "Development of Prediction Model to Improve Dropout of Cyber University," Journal of the Korea Academia-Industrial Cooperation Society, vol. 21, no. 7, pp. 380-390, 2020.
4 S. Lee and L. Park, "Analysis of Correlation between the Characteristics of University Students and Dropout," Journal of Learner-Centered Curriculum and Instruction, vol. 19, no. 11, pp. 1185-1210, 2019.
5 E. Lee, Y. Song, J. Kim, and S. Oh, "An Exploratory Study on Determinants Predicting the Dropout Rate of 4-year Universities Using Random Forest: Focusing on the Institutional Level Factors," Journal of Educational Technology, vol. 36, no. 1, pp. 191-219, 2020.   DOI
6 E. H. Lee and S. Kang, "The Research Trends and Implications of College Dropouts in Korea," Journal of Learner-Centered Curriculum and Instruction, vol. 19, no. 10, pp. 169-199, 2019.   DOI
7 J. Chung, Y. Park, and W. Kim, "Social Media Analysis Based on Keyword Related to Educational Policy Using Topic Modeling," Journal of Internet Computing and Services, vol. 19, no. 4, pp. 53-63, 2018.   DOI
8 S. Han, "Exploration of Factors that Affect College Student Drop-out and Resilience," Journal of Learner-Centered Curriculum and Instruction, vol. 18, no. 24, pp. 1369-1391, 2018.   DOI
9 D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," The Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
10 M. Jin and H. Ko, "Analysis of trends in mathematics education research using text mining," Journal of the Korean Society of Mathematical Education: Communications of Mathematical Education, vol. 3, no. 3, pp. 275-294, 2019.
11 D. H. Jeong and H. S. Joo, "Discovering interdisciplinary convergence technologies using content analysis technique based on topic modeling," Journal of the Korean Society for Information Management, vol. 35, no. 3, pp. 77-100, 2018.   DOI
12 D. H. Jeong and H. S. Joo, "Topical Prescriptive Analytics System for Automatic Recommendation of Convergence Technology," Biotechnology and Bioprocess Engineering, vol. 24, pp. 893-906, 2019.   DOI
13 M. P. Muller, G. Tomlinson, T. J. Marrie, P. Tang, A. McGeer, D. E. Low, A. S. Detsky, and W. L. Gold, "Can Routine Laboratory Tests Discriminate between Severe Acute Respiratory Syndrome and Other Causes of Community-Acquired Pneumonia?," Clinical Infectious Diseases, vol. 40, no. 8, pp. 1079-1086, 2005.   DOI
14 Z. Huang, X. Lu, and H. Duan, "Latent treatment pattern discovery for clinical processes," Journal of Medical Systems, vol. 37, no. 9915, 2013.
15 D. H. Jeong and M. Song, "Time gap analysis by the topic model-based temporal technique," Journal of Informetrics, vol. 8, no. 3, pp. 776-790, 2014.   DOI
16 D. M. Mimno, H. M. Wallach, E. M. Talley, M. Leenders, and A. K. McCallum, "Optimizing semantic coherence in topic models," In Proceedings of the Conference on Empirical Methods in Natural Language Processing: EMNLP '11, pp. 262-272, 2011.