PREDICTING STUDENT PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

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PREDICTING STUDENT PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

CHAPTER ONE

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.

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