A Data Mining Approach to Predict Key Factors Impacting University Students Dropout in a Least Developed Economy

Authors

  • Harman Preet Singh Department of Management and Information Systems College of Business Administration, University of Hail PO Box 2440. Ha'il – 81451, Kingdom of Saudi Arabia http://orcid.org/0000-0003-4297-0016
  • Ibrahim Abdullah Alhamad Department of Management and Information Systems College of Business Administration, University of Hail, PO Box 2440 Ha'il – 81451, Kingdom of Saudi Arabia

DOI:

https://doi.org/10.14738/abr.1012.13556

Abstract

University students’ dropout is a complex issue with life and career ramifications, especially in least developed countries. Ethiopia, a country with one of the least developed economies, has made considerable efforts to strengthen its higher education; yet university student attrition remains a major concern. In this study, we utilized the data mining methodology to reveal the important factors that impact dropout among the Ethiopian university students. The current research results indicate that personal, institutional, and academic factors affect university student dropout. In Ethiopia, low-performing rural female students are more likely to drop out than male students, according to the findings of this study. In general, rural low- achieving students have a greater likelihood of dropping out of university. This is likely to occur during the students' first semester of study, especially if they have a poor attendance rate. This research contributes to the body of knowledge by indicating that university remedial programs may be successful in reducing the incidents of students’ dropout. The current research has implications for policymakers in the least developed nations, such as Ethiopia, to construct dropout intervention programs based on the factors identified in this research.

References

Abeyayehu, A. (1997). Problems of Gender Equity in Institutions of Higher Learning in Ethiopia. In A. Asgedom (Ed.), Quality Education in Ethiopia: Visions for the 21st Century. Institute for Educational Research, Addis Ababa University.

Anand, S. S., & Büchner, A. G. (1998). Decision support using data mining. London: Financial Times Management.

Azevedo, A., & Santos, M. F. (2008). KDD, SEMMA and CRISP-DM: A Parallel Overview [Research Paper]. https://disi.unal.edu.co/~eleonguz/cursos/md/documentos/metodologias.pdf

Bastian, J., Steer, L., & Berry, C. (2013). Accelerating Progress to 2015: Ethiopia (A Report Series to the UN Special Envoy for Global Education). http://educationenvoy.org

Biyabeyen, M. (n.d.). The Root Cause Factors and the Status of Students Drop-out in Public Primary Schools of Harari Regional State, Ethiopia. Middle Eastern & African Journal of Educational Research, 15, 16–29.

Cios, K. J., & Kurgan, L. A. (2005). Trends in Data Mining and Knowledge Discovery. In Pal N.R., Jain L. (Eds.), Advanced Techniques in Knowledge Discovery and Data Mining (Advanced Information and Knowledge Processing, pp. 1-26). London: Springer. https://doi.org/10.1007/1-84628-183-0_1

Cios, K. J., Pedrycz, W., Swiniarski, R. W., & Kurgan, L. A. (2007). Data Mining: A Knowledge Discovery Approach (1st ed.). New York, NY: Springer US. https://doi.org/10.1007/978-0-387-36795-8

Creswell, J. W., Shope, R., Clark, V. L. P., & Green, D. O. (2006). How interpretive qualitative research extends mixed methods research. Research in the Schools, 13(1), 1–11. https://psycnet.apa.org/record/2007-09345-001

Fassil, E., Adem, G., Getahun, K., & Sileshi, A. (2018). Determinants of students vulnerability to attrition in higher education: Evidence from Arba Minch University, Ethiopia. Educational Research and Reviews, 13(15), 570–581. https://doi.org/10.5897/err2018.3533

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). Knowledge Discovery and Data Mining: Towards a Unifying Framework. In KDD’96 Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Retrieved May 14, 2022, from https://www.aaai.org/Papers/KDD/1996/KDD96-014.pdf

Gentle, J. E., Härdle, W. K., & Mori, Y. (Eds.). (2012). Handbook of Computational Statistics: Concepts and Methods (2nd ed., Springer Handbooks of Computational Statistics). Heidelberg, Germany: Springer-Verlag. https://doi.org/10.1007/978-3-642-21551-3

Jennings, M., & Poppe, R. (2012, July). Education Management Information System Dropout Study 1999-2003 E.C. (2006/07-2010/11 G.C.). http://www.moe.gov.et/

Kahsay, M. N. (2013). Quality and Quality Assurance in Ethiopian Higher Education: Critical Issues and Practical Implications [PhD Dissertation]. University of Twente, Netherlands.

KDNuggets. (2014). Data Mining, Analytics, Big Data, and Data Science. http://www.kdnuggets.com/

Kurgan, L. A., & Musilek, P. (2006). A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review, 21(1), 1–24. https://doi.org/10.1017/s0269888906000737

Lasonen, J., Kemppainen, R., & Raheem, K. (2005). Education and Training in Ethiopia: An Evaluation of Approaching EFA Goals. In University of Jyväskylä, Finland (Working Paper 23). Institute for Educational Research. https://ktl.jyu.fi

Melese, W., & Fanta, G. (2009). Trends and Causes of Female Students Dropout from Teacher Education Institutions of Ethiopia: The Case of Jimma University. Ethiopian Journal of Education and Science, 5(1), 1–19. https://www.ajol.info/index.php/ejesc/article/view/56309/44749

Mersha, Y., Bishaw, A., & Tegegne, F. (2012). Factors Affecting Female Students’ Academic Achievement at Bahir Dar University. Journal of International Cooperation in Education, 15(3), 135–148. https://core.ac.uk/download/pdf/222951681.pdf

Saint, W. (2003). Higher Education in Ethiopia: The Vision and Its Challenges. Journal of Higher Education in Africa, 2(3), 83–113. https://www.jstor.org/stable/24486295

Singh, H. P., & Alhulail, H. N. (2022). Predicting Student-Teachers Dropout Risk and Early Identification: A Four-Step Logistic Regression Approach. IEEE Access, 10, 6470–6482. https://doi.org/10.1109/access.2022.3141992

Tiruneh, W. A., & Petros, P. (2013). Factors affecting female students’ academic performance at higher education: The case of Bahir Dar University, Ethiopia. African Educational Research Journal, 2(4), 161–166. https://eric.ed.gov/?id=EJ1216887

UNESCO. (1998). Higher Education in the Twenty-first Century: Vision and Action [Research Paper]. World Conference on Higher Education, Paris, 5-9 October. http://unesdoc.unesco.org

UNESCO. (2015). Ethiopia for all 2015 National Review. http://unesdoc.unesco.org

Weldegiorgis, T., & Awel, Y. M. (2013). Determinants of Student Attrition at College of Business and Economics, Mekelle University: Econometric Investigation [Symposium Proceeding]. National Symposium on Establishing, Enhancing and Sustaining Quality Practices in Education, Nekemte, Oromia Region, Ethiopia. http://demo153.webbazaar.com/uploads/starjournalnew/17.pdf

Worku, B. N. (2013). The Quality of Evening Education Program at Jimma Teachers’ Training College (JTTC), In Oromia, Ethiopia. European Scientific Journal, 10(28), 312–326. https://eujournal.org/index.php/esj/article/view/4402

World Bank. (2003). Higher Education Development for Ethiopia: Pursuing the Vision. http://siteresources.worldbank.org

Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3rd ed.). Waltham, MA: Morgan Kaufmann. https://doi.org/10.1016/C2009-0-61819-5

Liu, H., Hussain, F., Tan, C. L., & Dash, M. (2002). Discretization: An Enabling Technique. Data Mining and Knowledge Discovery, 6(4), 393-423. https://doi.org/10.1023/a:1016304305535

Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining (2nd ed., Methods and Applications in Data Mining). Hoboken, NJ: John Wiley & Sons.

Refaeilzadeh, P., Tang, L., & Liu, H. (2009). Cross-Validation (K. Shim, Ed.). In L. Liu & M. T. Özsu (Eds.), Encyclopedia of Database Systems (1st ed., pp. 532-538). New York, NY: Springer US. https://doi.org/10.1007/978-0-387-39940-9_565

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Published

2022-12-11

How to Cite

Singh, H. P., & Alhamad, I. A. (2022). A Data Mining Approach to Predict Key Factors Impacting University Students Dropout in a Least Developed Economy. Archives of Business Research, 10(12), 48–59. https://doi.org/10.14738/abr.1012.13556