Enrolment Management in Higher Learning Institutions: Student Retention Prediction
Abstract
Student retention has become one of the most important priorities for decision makers in higher learning institutions (HLI). The increasing competition for students among tertiary institutions has resulted in a greater emphasis on student retention. Improving student retention starts with a thorough understanding of the reasons behind the attrition. In an effort to address this issue, the study used student demographic and institutional data along with several business intelligence (BI) techniques and analytical tools, to develop prototype to predict likelihood of student persistence or dropout with the goal to identify factors that can be used to identify students who are at risk of dropping out of tertiary institution program. This study used classification models generated using Waikato Environment for Knowledge Analysis (WEKA). The model was built using the 10-fold cross validation, and holdout method (60% of the data was used as training and the remaining as test and validation). Random sampling techniques were used in selecting the datasets. The attribute selection analysis of the models revealed that the student age on entry, parent occupation, health of student and financial variables are among the most important predictors of the phenomenon. Results of the classifiers were compared using accuracy level, confusion matrices and speed of model building benchmarks. The study shows that identifying the relevant student background factors can be incorporated to design a business intelligence system that can serve as valuable tool in predicting student withdrawal or persistence as well as recommend the necessary intervention strategies to adopt, leading to better education efficiency and graduation rate.
Article Views and Downloands Counter
References
Carey, K. (2004). A matter of degrees: Improving graduation rates in four-year colleges and universities. New York: The Education Trust.
Frank, E., Hall, M. A., (2011). Data Mining: Practical Machine Learning Tools and Techniquesǁ, 3rd Ed. Morgan Kaufmann.
Hämäläinen, W., & Vinni, M. (2010). Classifiers for educational technology. In C. Romero, S. Ventura, M., Pechenizkiy, R. S., Baker, J. D., (eds.), Handbook of Educational Data Mining, (pp. 54-74). CRC Press.
Watson, H. J., & Wixom, B. H.,(2007). The Current State of Business Intelligence, Computer, vol.40, no. 9, pp. 96-99, September 2007, doi:10.1109/MC.2007.331
Hall, M., Frank, E, Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H., (2009). The WEKA data mining software: An update. ACM SIGKDD explorations newsletter.
Olszak, C. M., & Ziemba, E. (2004). Business intelligence systems as a new generation of decision support systems. Proceedings PISTA 2004, International Conference on Politics and Information Systems: Technologies and Applications. Orlando: The International Institute of Informatics and Systemic.
Tinto, V. (1975). Dropout from Higher Education: A Theoretical Synthesis of Recent Research. Review of Educational Research vol.45, pp.89-125.
Copyright (c) 2023 Africa Journal of Technical and Vocational Education and Training
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright Notice Copyright of published articles is held by AfriTVET. No limitation will be placed on the personal freedom of authors to copy or to use in subsequent work, material contained in their papers. Please contact the Publisher for clarification if you are unsure of the use of copyright material. Apart from fair dealing for the purposes of research and private study, or criticism and or review, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the Publishers.
