PREDICTING STUDENT PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

ABSTRACT

I am a student of the above institution in computer science department who currently seek to carry out a research work on “design and implementation of a computer based seaport billing system”, I took this research upon me after my research survey and finding for problems that actually needs an attention and solution.

The observed poor quality of graduates of students of this institution in recent times has been partly traced to inadequacies of some or most of the lecturer in this University. In this study an Artificial Neural Network (ANN) model, for predicting the likely performance of student will be developed and tested.

I will also identify the various factors that may likely influence the performance of student. An implementation of a user-friendly software tool for predicting the students’ performance which is based on a neural network classifier. This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or weak students who are likely to have low achievements. The system will be developed and trained using data spanning five generations of graduates from one of the department in the school. The use of artificial intelligence has enabled the development of more sophisticated and more efficient student models which represent and detect a broader range of student behaviour than was previously possible.

Due to the platform which this system will run on, it will be developed using the Artificial Intelligence Markup Language, therefore, I will deploy this system using C# for its development.

INTRODUCTION

During the last few years, the application of artificial intelligence in education has grown exponentially, spurred by the fact that it allows us to discover new, interesting and useful knowledge about students. Educational data mining (EDM) is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational context. While traditional database queries can only answer questions such as ”find the students who failed the examinations”, data mining can provide answers to more abstract questions like ”find the students who will possibly succeed the examinations”. One of the key areas of the application of EDM is the development of student models that would predict student characteristics or performances in their educational institutions. Hence, researchers have begun to investigate various data mining methods to help educators to evaluate and improve the structure of their course context.The main objective of the admission system is to determine candidates who would likely do well in the university or can perform well within the academic year or to produce students of high grade and intelligence. The quality of candidates admitted into any higher institution affects the level of research and training within the institution, and by extension, has an overall effect on the development of the country itself, as these candidates eventually become key players in the affairs of the country in all sectors of the economy.