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Students selection for university course admission at the joint admissions board (Kenya) using trained neural networks

Wabwoba, Franklin; Mwakondo, Fullgence M.

Abstract:
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

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Document Title: Students selection for university course admission at the joint admissions board (Kenya) using trained neural networks
Journal: Journal of Information Technology education
Volume: 10


Document Type:Journal Article (Peer Reviewed)
Subject Area:Students
Country:Kenya
Keywords:Student Enrolment, Student Choice, Universities, Course Materials, Course Evaluation


File Size:471 KB
Date Added:02 December 2011


Students selection for university course admission at the joint admissions board (Kenya) using trained neural networks