ABSTRACT
The paper identified the predictors of student attrition in the Higher Education Institution (HEI) through predictive analytics approach. The prediction model used in the study includes variable optimization through Genetic Algorithm (GA) and decision tree generation phase through C4.5 algorithm. The college student leavers' data from one of the Higher Education in the Philippines from the school year 2008-2009 until the school year 2018-2019 was used as datasets of the study. Out of forty identified reasons for leaving as variables, there were nine (9) identified predictors of student attrition. Through the identified predictors, administrators of educational institutions may design intervention plans related to the student attrition.
- Hamshire, C., Jack, K., Forsyth, R., Langan, A. M., & Harris, W. E. (2019). The wicked problem of healthcare student attrition. Nursing inquiry, e12294.Google Scholar
- Mason, C., Twomey, J., Wright, D., & Whitman, L. (2018). Predicting engineering student attrition risk using a probabilistic neural network and comparing results with a backpropagation neural network and logistic regression. Research in Higher Education, 59(3), 382--400.Google Scholar
- Satterfield, D. J., Tsugawa-Nieves, M., & Kirn, A. N. (2018, October). WIP: Factors Affecting Graduate STEM Student Attrition Rates. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1--4). IEEE.Google Scholar
- De Leon, C. T. T. (2018). Navigating the Storm: Integrative Review of Attrition Factors Among Undergraduate Nursing Students. Health Notions, 2(9), 918--926.Google Scholar
- Braga, V., Hortenzi, L. F., dos Santos, F., Bastos, N. M., Carlos de Toledo, J., de Albuquerque, A. A., & Bernedo Gonzales, J. F. (2018). Influential factors in student retention: a study involving undergraduate students in Accountancy. Revista de Educação e Pesquisa em Contabilidade, 12(3).Google Scholar
- Seidman, A. (2019). Minority Student Retention: The Best of the" Journal of College Student Retention: Research, Theory & Practice". Routledge.Google Scholar
- Sunidijo, R. Y., & Kamardeen, I. (2018). Psychological challenges confronting graduate construction students in Australia. International Journal of Construction Education and Research, 1--16.Google Scholar
- Volkert, D., Candela, L., & Bernacki, M. (2018). Student motivation, stressors, and intent to leave nursing doctoral study: A national study using path analysis. Nurse education today, 61, 210--215.Google Scholar
- Barbé, T., Kimble, L. P., Bellury, L. M., & Rubenstein, C. (2018). Predicting student attrition using social determinants: Implications for a diverse nursing workforce. Journal of Professional Nursing, 34(5), 352--356.Google ScholarCross Ref
- Seidman, A. (2019). Minority Student Retention: The Best of the" Journal of College Student Retention: Research, Theory & Practice". Routledge.Google Scholar
- Bruffaerts, R., Mortier, P., Kiekens, G., Auerbach, R. P., Cuijpers, P., Demyttenaere, K., ... & Kessler, R. C. (2018). Mental health problems in college freshmen: Prevalence and academic functioning. Journal of affective disorders, 225, 97--103.Google ScholarCross Ref
- Beer, C., & Lawson, C. (2017). The problem of student attrition in higher education: An alternative perspective. Journal of Further and Higher Education, 41(6), 773--784.Google ScholarCross Ref
- Pusztai, G. (2019). The role of intergenerational social capital in diminishing student attrition. Journal of Adult Learning, Knowledge and Innovation, 3(1), 20--26.Google ScholarCross Ref
- Reyes, C. T. (2018). Developing a student support system through learning analytics for undergraduates at the university of the Philippines open university.Google Scholar
- Tani, K., & Gilbey, A. (2018). Predicting academic success for business and computing students. In Student Engagement and Participation: Concepts, Methodologies, Tools, and Applications (pp. 1098--1109). IGI Global.Google Scholar
- Yadav, S., Jain, A., & Singh, D. (2018, December). Early Prediction of Employee Attrition using Data Mining Techniques. In 2018 IEEE 8th International Advance Computing Conference (IACC) (pp. 349--354). IEEE.Google ScholarCross Ref
- Qiu, L., Liu, Y., Hu, Q., & Liu, Y. (2019). Student dropout prediction in massive open online courses by convolutional neural networks. Soft Computing, 23(20), 10287--10301.Google ScholarDigital Library
- Orong, M. Y., Sison, A. M., & Medina, R. P. (2018, October). A Hybrid Prediction Model Integrating a Modified Genetic Algorithm to K-means Segmentation and C4. 5. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1853--1858). IEEE.Google Scholar
- Chandrasekar, P., Qian, K., Shahriar, H., & Bhattacharya, P. (2017). Improving the Prediction Accuracy of Decision Tree Mining with Data Preprocessing. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), 481--484. https://doi.org/10.1109/COMPSAC.2017.146Google Scholar
- Leijoto, L. F., Rodrigues, T. A. D. O., Zaratey, L. E., & Nobre, C. N. (2014). A Genetic Algorithm for the Selection of Features Used in the Prediction of Protein Function. 2014 IEEE International Conference on Bioinformatics and Bioengineering, 168--174. https://doi.org/10.1109/BIBE.2014.42Google ScholarDigital Library
- Maldonado, S., Flores, Á., Verbraken, T., Baesens, B., & Weber, R. (2015). Profit-based feature selection using support vector machines - General framework and an application for customer retention. Applied Soft Computing Journal, 35, 240--248. https://doi.org/10.1016/j.asoc.2015.05.058Google ScholarDigital Library
- Shen, Q., Li, H., Liao, Q., Zhang, W., & Kone, K. (2014). Improving churn prediction in telecommunications using complementary fusion of multilayer features based on factorization and construction. 26th Chinese Control and Decision Conference, CCDC 2014, 2250--2255. https://doi.org/10.1109/CCDC.2014.6852544Google ScholarCross Ref
- Orong, M. Y., Sison, A. M., & Medina, R. P. (2018, May). A new crossover mechanism for genetic algorithm with rank- based selection method. In 2018 5th International Conference on Business and Industrial Research (ICBIR) (pp. 83--88). IEEE.Google ScholarCross Ref
- Almayan, H., & Al Mayyan, W. (2016). Improving accuracy of students' final grade prediction model using PSO. Proceedings of the 6th International Conference on Information Communication and Management, ICICM 2016, 35--39. https://doi.org/10.1109/INFOCOMAN.2016.7784211Google ScholarCross Ref
- Chou, C.-H., Hsieh, S.-C., & Qiu, C.-J. (2017). Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction. Applied Soft Computing, 56, 298--316. https://doi.org/https://doi.org/10.1016/j.asoc.2017.03.014Google ScholarDigital Library
- Zhang, X., Wang, X., Chen, W., Tao, J., Huang, W., & Wang, T. (2017). A Taxi Gap Prediction Method via Double Ensemble Gradient Boosting Decision Tree. 2017 IEEE 3rd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 255--260. https://doi.org/10.1109/BigDataSecurity.2017.27Google Scholar
Index Terms
- A Predictive Analytics Approach in Determining the Predictors of Student Attrition in the Higher Education Institutions in the Philippines
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