Journal of Information Technology Education Volume 10, 2011
Students Selection for University Course Admission at the Joint Admissions Board (Kenya) Using Trained Neural Networks Franklin Wabwoba Masinde Muliro University of Science and Technology, Kakamega, Kenya firstname.lastname@example.org
Fullgence M. Mwakondo, Mombasa Polytechnic University College, Mombasa, Kenya email@example.com
Every year, the Joint Admission Board (JAB) is tasked to determine those students who are ex-pected to join various Kenyan public universities under the government sponsorship scheme. This exercise is usually extensive because of the large number of qualified students compared to the very limited number of slots at various institutions and the shortage of funding from the govern-ment. Further, this is made complex by the fact that the selections are done against a predefined cluster subjects vis a vis the student’s preferred and applied for academic courses. Minimum re-quirements exist for each course and only students having the prescribed grades in specific sub-jects are eligible to join that course. Due to this, students are often admitted to courses they con-sider irrelevant to their career prospects and not their preferred choices. This process is tiresome, costly, and prone to bias, errors, or favour, leading to disadvantaging innocent students. This paper examines the potential use of artificial neural networks at the JAB for the process of selecting students for university courses. Based on the fact that Artificial Neural Networks (ANNs) have been tested and used in classification, the paper explains how a trained neural network can be used to perform the students’ placement effectively and efficiently. JAB will be able, therefore, to undertake the students’ placement thoroughly and be able to accomplish it with minimal wastage of time and resources respectively without having to utilise unnecessary effort. The paper outlines how the various metrics can be coded and used as input to the ANNs. Ultimately, the paper underscores the various merits that would accompany the adoption of this technique. By making use of neural networks in the university career choices, student placement at JAB will enhance the chances of students being placed into courses they prefer as part of their career choice. This is likely to motivate the students, making them work harder and leading to improved performance and improved completion rate. The ANN application may also reduce the cost spend on the application processing and the time the applicants have to wait for the outcome. The ANN application could further increase the chances of high quality applicants getting admis-sion to career courses for which they qualify. Editor: Elsje Scott
Students Selection for University Course Admission
Keywords: neural networks, university admission, cluster subjects, minimum requirements, uni-versity courses, selection. Introduction
Student selection for university courses in Kenya is an activity that is performed by the Joint Ad-missions Board (JAB) each year. In this process students are allocated courses of their choice ac-cording to their performance in specific subjects. Minimum requirements exist for each course and only students having the prescribed grades in specific subjects are eligible to join a particular course. This activity may be costly and prone to bias, errors, or favor, leading to disadvantaging some students. Admission requirements for these universities are dynamic and keep fluctuating each year de-pending on the overall performance of students. Course requirements have to be revised every year in order to scale the number of student admissions according to limited university slots. This activity can be tedious, time consuming and calls for systems that are also dynamically changing to manage the task. The approach is also subject to abuse by insiders within JAB and inappropri-ate use of resources. Information and Communication...
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