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Computer Science Department
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PREDICTING STUDENT PERFORMANCE USING NEURAL NETWORK
CHAPTER ONE
INTRODUCTION
1.0 Background of the Study
Education is an essential factor for improving the socio-economic, cultural, and political development of a country; for this reason, its role cannot be overemphasized (Ajayi & Ekundayo, 2008). In our contemporary world, higher education is an important means for economic and social development and progress of a country. It should be noted that higher education is not just one of many means to a middle-class life; it has become most essentially the only means (Tierney, 2006).
The university admission system is charged with the responsibility of admitting prospective students into the university using guidelines that are set by the Joint Admission and Matriculation Board and the National University Commission. The Federal government of Nigeria established the Joint Admission and Matriculation Board (jamb) in 1978 to handle admission processes (Asein & Lawal, 2007). The board aims at establishing a unified standard for carrying out matriculation examination and giving admission to qualified candidates into the university academic system (Asein & Lawal, 2007).
1.1 Statement of the Problem
The poor quality of graduates of most Nigerian universities is overwhelming and the ability to predict or forecast the performance of students remains significant to the growth and development of an institution and the country at large.
The quality of candidates who are to be admitted into the universities of higher learning affects the level of training and research within the academic institution and generally affects the development and growth of the country.
The inability of the university admission system to give admission to candidate who will likely do well and other factors has being held responsible for the decline in the performance of undergraduate student, for this reason this research work tries to proffer solution by providing a means to evaluate prospective student who are to be considered for admission, so thereby admitting those who are likely to perform well in school.
1.2 Aim and Objectives
The aim of this project work is to use an artificial neural network model to predict prospective candidate's performance when admitted. This aim is achievable through the following objectives
1. To determine key factors that directly or indirectly affects the performance of students
2. To transform highlighted key factors into a form that can be represented in a neural network
3. To use transformed factors as background data to train, validate and test an artificial neural network that can predict a student performance seeking admission into the university or institution of higher learning.
1.3 Scope of the Study
This study attempts to identify various factors that affect students' performance or factors that have the potential of determining how a candidate will perform when admitted into the university and uses this factors as background data for system coding so as to use a suitable artificial neural network model to predict a prospective student performance.
This study spans the department of Computer Science, Federal University of Technology Minna.
1.4 Limitation of the Study
Data used to train, validate and test the network was obtained from the department of computer science Federal University of Technology Minna, therefore it may not be generalize to other department and schools of higher learning.
1.5 Significance of the Study
The ability to predict or forecast the performance of a prospective candidate seeking admission will eliminate the problem faced by the university admission system in determining which student will do well when admitted into the institution hence improves the University admission system
📄 Pages: 55 🧠 Words: 7327 📚 Chapters: 5 🗂️️ For: PROJECT
👁️🗨️️️ Views: 379
