The International Arab Journal of Information Technology (IAJIT)


Predicting Student Enrolments and Attrition Patterns in Higher Educational Institutions using

In higher educational institutions, student enrollment management and increasing student retention are fundamental performance metrics to academic and financial sustainability. In many educational institutions, high student attrition rates are due to a variety of circumstances, including demographic and personal factors such as age, gender, academic background, financial abilities, and academic degree of choice. In this study, we will make use of machine learning approaches to develop prediction models that can predict student enrollment behavior and the students who have a high risk of dropping out. This can help higher education institutions develop proper intervention plans to reduce attrition rates and increase the probability of student academic success. In this study, real data is taken from Abu Dhabi School of Management (ADSM) in the UAE. This data is used in developing the student enrollment model and identifying the student’s characteristics who are willing to enroll in a specific program, in addition to that, this research managed to find out the characteristics of the students who are under the risk of dropout.

[1] Agrawal R., Mannila H., Srikant R., Toivonen H., and Verkamo A., “Fast Discovery of Association Rules,” Advances in Knowledge Discovery and Data Mining, vol. 12, no. 1, pp. 307-328, 1996.

[2] Al-Shabandar R., Hussain A., Liatsis P., and Keight R., “Detecting At-Risk Students with Early Interventions Using Machine Learning Techniques,” IEEE Access, vol. 7, pp. 149464- 149478, 2019.

[3] Freund Y., Schapire R., and Abe N., “A Short Introduction to Boosting,” Journal-Japanese Society for Artificial Intelligence, vol. 14, pp. 1612, 1999.

[4] Kemper L., Vorhoff G., and Wigger B., “Predicting Student Dropout: A Machine Learning Approach,” European Journal of Higher Education, vol. 10, no. 1, pp. 28-47, 2020.

[5] Mulugeta M. and Borena B., “Higher Education Students’ Enrolment Forecasting System Using Data Mining Application in Ethiopia” HiLCoE Journal of Computer Science and Technology, vol. 2, no. 2, pp. 37-43, 2013.

[6] Nakhkob B. and Khademi M., “Predicted Increase Enrolment In Higher Education Using Neural Networks and Data Mining Techniques,” Journal of Advances in Computer Research, vol. 7, no. 4, pp. 125-140, 2016.

[7] Petkovski A., Stojkoska B., Trivodaliev K., and Kalajdziski S., “Analysis of Churn Prediction: A Case Study on Telecommunication services in Macedonia,” in Proceedings of 24th Telecommunications Forum, Belgrade, pp. 1-4, 2016.

[8] Rai S. and Jain A., “Students' Dropout Risk Assessment in Undergraduate Courses of ICT at Residential University-A Case Study,” International Journal of Computer Applications, vol. 84, no. 14, pp. 31-36, 2013.

[9] Sadatrasoul S., Gholamian M., and Shahanaghi K., “Combination of Feature Selection and Optimized Fuzzy Apriori Rules: The Case of Credit Scoring,” The International Arab Journal of Information Technology, vol. 12, no. 2, pp. 138- 145, 2015.

[10] Schapire R. and Freund Y., “Boosting: Foundations and Algorithms,” Kybernetes, 2013.

[11] Waters A. and Miikkulainen R., “Grade: Machine Learning Support for Graduate Admissions,” AI Magazine, vol. 35, no. 1, pp. 64-75, 2014.

[12] Yukselturk E., Ozekes S., and Türel Y., “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. Predicting Student Enrolments and Attrition Patterns in Higher Educational... 567 Samar Shilbayeh is Assistant Professor in Business Analytics program in Abu Dhabi School of Management, she worked as a senior data scientist and head of research Centre in Cognitro Analytics company experienced in extracting data, analyzing, findings and applying predictive modeling techniques, assisted in the development of an analytics framework for smart construction of a new type of predictive indictors “Cognitive Indicators”. Developed active learning algorithms. Developed a cost effective, expert intelligent system, which guides the data miner to optimize models selection. Her research interests include machine learning and AI approaches, cost sensitive machine learning algorithms, applying machine learning solutions in health, finance, telecom and insurance. Dr Samar has a PhD in Machine learning and AI from the university of Salford, Computer science and Engineering department, Manchester, UK. Abdullah Abonamah is the President and Provost of the Abu Dhabi School of Management. From August, 2000 to October, 2007, he was a Professor and Director of the Institute for Technological Innovation at Zayed University and the Assistant Dean of the College of Information Systems. Before coming to the UAE, he was a Professor of Computer Science and the Computer Science Division Head at the University of Akron. Dr. Abonamah has a PhD in Computer Science from the Illinois Institute of Technology, Chicago, Illinois and an Executive Management Graduate Degree from Yale University School of Management. His research and teaching interests include strategy, technology management, and entrepreneurship and innovation. With over fifty publications in international journals and conferences and a US patent in reliable systems, Dr. Abdullah remains a strong advocate of strategic management, innovation, entrepreneurship, and proper technology-business integration.