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Research Article · Free to read · Published 30 September 2010

An Application of Multivariate Analysis in Modeling Students Placement in Nigerian Higher Institutions

Ali Idrisa Gambo & Mohammed Waziri Yusuf

Journal of Mathematics and Statistics, Volume 6, Issue 3, pp. 350–356 · doi.org/10.3844/jmssp.2010.350.356

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Abstract

Problem statement: Students’ placement into courses of study in most (if not all) tertiary institutions in Nigeria had long been a problematic exercise. Most students get placed into courses of study they are ill suited to and then for which they are ill prepared. Approach: This research focused on the polytechnic, taking the newest federal polytechnic as a case study, that is, Federal Polytechnic Damaturu. The research tries to design a predictive model for the purpose of placing incoming students from the Pre-National Diploma (Pre-ND) programme, into courses of study based on the students’ performance at the Pre-ND level. The classification model sought to assign a student into a course of study, through the analysis of subjects scores at the Pre-ND level. Results: Discriminate analysis technique was applied and results obtained indicated different subjects in the different courses as the strongest contributors for the placement of students into different courses of study. The science courses have similar subjects as strong contributors, while the management courses share the same subjects mix as contributors to a student’s placement into a course. Conclusion: The natures of the models constitute discriminate factors and the potential for its improvement in future are also discussed in the research.

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Gambo, A. I. & Yusuf, M. W. (2010). An Application of Multivariate Analysis in Modeling Students Placement in Nigerian Higher Institutions. Journal of Mathematics and Statistics, 6(3), 350–356. https://doi.org/10.3844/jmssp.2010.350.356

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An Application of Multivariate Analysis in Modeling Students Placement in Nigerian Higher InstitutionsJournal of Mathematics and Statistics · Volume 6, Issue 3 · pp. 350–356 · © Science PublicationsDownload