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http://dx.doi.org/10.3745/JIPS.04.0218

Evaluation of Predictive Models for Early Identification of Dropout Students  

Lee, JongHyuk (Dept. of Artificial Intelligence and Big Data Engineering, Daegu Catholic University)
Kim, Mihye (School of Computer Software, Daegu Catholic University)
Kim, Daehak (Dept. of Artificial Intelligence and Big Data Engineering, Daegu Catholic University)
Gil, Joon-Min (School of Computer Software, Daegu Catholic University)
Publication Information
Journal of Information Processing Systems / v.17, no.3, 2021 , pp. 630-644 More about this Journal
Abstract
Educational data analysis is attracting increasing attention with the rise of the big data industry. The amounts and types of learning data available are increasing steadily, and the information technology required to analyze these data continues to develop. The early identification of potential dropout students is very important; education is important in terms of social movement and social achievement. Here, we analyze educational data and generate predictive models for student dropout using logistic regression, a decision tree, a naïve Bayes method, and a multilayer perceptron. The multilayer perceptron model using independent variables selected via the variance analysis showed better performance than the other models. In addition, we experimentally found that not only grades but also extracurricular activities were important in terms of preventing student dropout.
Keywords
Educational Data Analysis; Student Dropout; Predictive Model;
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1 N. Iam-On and T. Boongoen, "Generating descriptive model for student dropout: a review of clustering approach," Human-centric Computing and Information Sciences, vol. 7, article no. 1, 2017. https://doi.org/10.1186/s13673-016-0083-0   DOI
2 C. A. Christle, K. Jolivette, and C. M. Nelson, "School characteristics related to high school dropout rates," Remedial and Special Education, vol. 28, no. 6, pp. 325-339, 2007.   DOI
3 D. Olaya, J. Vasquez, S. Maldonado, J. Miranda, and W. Verbeke, "Uplift Modeling for preventing student dropout in higher education," Decision Support Systems, vol. 134, article no. 113320, 2020. https://doi.org/10.1016/j.dss.2020.113320   DOI
4 D. Jampen, G. Gur, T. Sutter, and B. Tellenbach, "Don't click: towards an effective anti-phishing training: a comparative literature review," Human-centric Computing and Information Sciences, vol. 10, article no. 33, 2020. https://doi.org/10.1186/s13673-020-00237-7   DOI
5 A. Dutt, M. A. Ismail, and T. Herawan, "A systematic review on educational data mining," IEEE Access, vol. 5, pp. 15991-16005, 2017.   DOI
6 National Dropout Prevention Center for Students with Disabilities, https://dropoutprevention.org/
7 C. E. L. Guarin, E. L. Guzman, and F. A. Gonzalez, "A model to predict low academic performance at a specific enrollment using data mining," IEEE Revista Iberoamericana de tecnologias del Aprendizaje, vol. 10, no. 3, pp. 119-125, 2015.   DOI
8 E. B. Costa, B. Fonseca, M. A. Santana, F. F. de Araujo, and J. Rego, "Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses," Computers in Human Behavior, vol. 73, pp. 247-256, 2017.   DOI
9 J. Vasquez and J. Miranda, "Student desertion: What is and how can it be detected on time?," in Data Science and Digital Business. Cham, Switzerland: Springer, 2019, pp. 263-283.
10 A. Omoto, Y. Lwayama, and T. Mohri, "On-campus data utilization: working on institutional research in universities," Fujitsu Science Technology, vol. 1, no. 51, pp. 42-49, 2015.
11 R. Gurusamy and V. Subramaniam, "A machine learning approach for MRI brain tumor classification," Computers, Materials and Continua, vol. 53, no. 2, pp. 91-109, 2017.
12 J. R. Turner and J. Thayer, Introduction to Analysis of Variance: Design, Analysis & Interpretation. Thousand Oaks, CA: Sage Publications, 2001.
13 D. Tang, R. Dai, L. Tang, and X. Li, "Low-rate DoS attack detection based on two-step cluster analysis and UTR analysis," Human-centric Computing and Information Sciences, vol. 10, article no. 6, 2020. https://doi.org/10.1186/s13673-020-0210-9   DOI
14 J. Kaur and K. Kaur, "A fuzzy approach for an IoT-based automated employee performance appraisal," Computers, Materials and Continua, vol. 53, no. 1, pp. 24-38, 2017.
15 D. Gasevic, V. Kovanovic, and S. Joksimovic, "Piecing the learning analytics puzzle: a consolidated model of a field of research and practice," Learning: Research and Practice, vol. 3, no. 1, pp. 63-78, 2017.   DOI
16 N. Hoff, A. Olson, and R. L. Peterson, "Dropout screening and early warning," University of Nebraska-Lincoln, NE, USA, 2015.
17 American Institutes for Research, "Early Warning Systems in Education," 2019 [Online]. Available: http://www.earlywarningsystems.org/
18 E. Yukselturk, S. Ozekes, and Y. K. Turel, "Predicting dropout student: an application of data mining methods in an online education program," European Journal of Open, Distance and e-learning, vol. 17, no. 1, pp. 118-133, 2014.   DOI
19 L. M. B Manhaes, S. M. S. Cruz, and G. Zimbrao, "WAVE: an architecture for predicting dropout in undergraduate courses using EDM," in Proceedings of the 29th Annual ACM Symposium on Applied Computing, Gyeongju, South Korea, 2014, pp. 243-247.
20 D. Kuznar and M. Gams, "Metis: system for early detection and prevention of student failure," in Proceedings of the 6th International Workshop on Combinations of Intelligent Methods and Applications (CIMA), Hague, Holland, 2016.
21 Wisconsin Department of Public Instruction, "Dropout Early Warning System," c2021 [Online]. Available: https://dpi.wi.gov/ews/dropout
22 C. Yuan, X. Li, Q. J. Wu, J. Li, and X. Sun, "Fingerprint liveness detection from different fingerprint materials using convolutional neural network and principal component analysis," Computers, Materials & Continua, vol. 53, no. 3, pp. 357-371, 2017.