DEVELOPMENT OF A PERFORMANCE BASED ENROLLMENT SYSTEM USING ARTIFICIAL NEURAL NETWORK

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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
Predicting student academic performance has long been an important research topic. Among the issues of higher education institutions, questions concerning admissions remain important. The main objective of the admission system is to determine the candidates who would likely perform well after being accepted into the university. The quality of admitted students has a great influence on the level of research and training within the institution. The failure to perform an accurate admission decision may result in an unsuitable student being admitted to the program. Hence, admission officers want to know more about the academic potential of each student. Accurate predictions help admission officers to distinguish between suitable and unsuitable candidates for an academic program, and identify candidates who would likely do well in the university. The results obtained from the prediction of academic performance may be used for classifying students, which enables educational managers to offer them additional support, such as customized assistance and tutoring resources. The results of this prediction can also be used by instructors to specify the most suitable teaching actions for each group of students, and provide them with further assistance tailored to their needs. In addition, the prediction results may help students develop a good understanding of how well or how poorly they would perform, and then develop a suitable learning strategy. Accurate prediction of student achievement is one way to enhance the quality of education and provide better educational services.
Forecasting is a decision-making tool used by many businesses to help in budgeting, planning, and estimating future growth. In the simplest terms, forecasting is the attempt to predict future outcomes based on past events and management insight. It is required in many situations: deciding whether to build another power generation plant in the next five years requires forecasts of future demand; scheduling staffing a call centre next week requires forecasts of call volume; stocking an inventory requires forecasts of stock requirements. Forecasts can be required several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning.
Most people view the world as consisting of a large number of alternatives. Futures research evolved as a way of examining the alternative futures and identifying the most probable. Forecasting is designed to help decision making and planning in the present. Forecasts empower people because their use implies that we can modify variables now to alter (or be prepared for) the future. A prediction is an invitation to introduce change into a system.
Recently, however, there has been a noticeable slide in the quality of graduates of some Nigerian universities. The inadequacies of the present university admission system, among other factors, have been blamed for this decline. Due to the increasing gap between the numbers students seeking admission and the total available admission slots, there has been a corresponding increased pressure on the process. This pressure has led to rampant cases of admission fraud and related problems.
The observed poor quality of students in external examinations in schools and weak graduates of tertiary institutions has poised the introduction of forecasting models to predict the performance of college and school enrolment. Various factors that may likely influence the performance of a student were identified. Such factors as ordinary level subjects’ scores and subjects’ combination, matriculation examination scores, age on admission, parental background, types and location of secondary school attended and gender, among others.
Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors.
Different approaches have been applied to predicting student academic performance, including traditional mathematical models and modern data mining techniques. In these approaches, a set of mathematical formulas was used to describe the quantitative relationships between outputs and inputs (i.e., predictor variables). The prediction is accurate if the error between the predicted and actual values is within a small range. The artificial neural network (ANN) is a biologically inspired simulation performed on the computer. It is also a soft computing technique, which has been successfully applied in different fields of science, such as pattern recognition, fault diagnosis, clustering, classification forecasting and prediction.
In this study, an approach to the problem based on the artificial neural network (ANN) was adopted.
1.2 Statement of the Problem.
Following the continuous decline in the general performance of students in recent WAEC and NECO results, there has been and the large turnout of weak graduates by tertiary institutions, there is an urgent need to review the current admission system and a close forecasting of student’s long time performance if admitted into colleges or high schools. The following were some of the problems leading to the rise poor performance of students at all level of education.
In current system, students’ educational history is not considered, as this a key factor in determining a students’ performance.
The current system does not take notice of the age limits in their enrolment. It is believed that students within the best age in a class, or level tend to perform better.
The health of students also determines their performance. Sickle cell anemia for instance is a very challenging health issue to students as it can cause them spending more time attending to health issues than study.
The admission examination is not always treated with standard. The process has been compromised as students whose points could not reach cut off were admitted with concession through bribery or relationship.
1.3 Aim and Objectives of the study
The main aim of this project is to develop an enrolment system for schools using Artificial Neural Network (ANN). The following objectives will be needed in other to achieve this aim.
To design an enrolment system that will predict student academic performance for colleges.
To implement the developed system.
To use Artificial Neural Network to predict candidate performance based on some given pre-admission data.

1.4 Scope of the project

The proposed system will be strictly a web based program so it can be accessed from any location. All what is required is an internet enabled device like phones or personal computers. The system will be developed with the sole aim of predicting the performance of their new enrolment. The system will be used by the school management and a site administrator. The school management will have a survey form where they are to provide or grade the new students based on certain criteria. One of the criteria will be their performance in the last screening exercise; others will be the student’s age, their educational history, their health condition, and some other variables. The system will be able to predict the performance of students based on the respective information provided by the school management. Information about each student performance provided by a school management will be made very discreet as it can only be accessed by the site administrator and a general report about the prediction of the system will be provided to the school management for further actions. Actions expected to be carried out by the school management includes more intensive and aggressive lessons in cases whereby the general performance of the school or college enrolment is very low.
1.5 Significance of the project
The significance of this project explains the benefits that this project will provide to institutions or schools that implements it. The following are some of the benefits that will be derived from the proposed system.
It will help in the provision of predictions on the future educational performance of students.
The system will help in the preparation of teachers or lecturers as the case may be on the task ahead. This is because in situations whereby student’s performance is pretty low, more efforts are expected from their teachers.
The system will also allow multi user access to the system. This will allow several users in the school management to provide inputs of individual students at a time.
The system will finally improve the general performance of students bearing in mind that all their efforts are monitored. Extra moral will be provided each time their individual efforts improve the forecast each academic year.

DEVELOPMENT OF A PERFORMANCE BASED ENROLLMENT SYSTEM USING ARTIFICIAL NEURAL NETWORK