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PROJECT TOPIC:  DETERMINE STUDENT’S ACADEMICS PERFORMANCE USING ARTIFICIAL NEURAL NETWORK ALGORITHM
Department:  Computer Science
AMOUNT:  20000
FORMART:   MS WORD
PAGES:  90 pages, abstract, chapter 1-5 , APENDIX A source code and APENDIX B output, well reserached and supervised
  Algorithm

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Abstract

Predicting student academic performance plays an important role in academics. Classifying students using conventional techniques cannot give the desired level of accuracy, while doing it with the use of soft computing techniques may prove to be beneficial. A good prediction of student's success is one way to be in the competition in education system. The use of Soft Computing methodology is justified for its real-time applicability in education system. The paper presents Artificial Neural Network model to model academic profile of students. The model mimics teacher's ability to deal with imprecise information representing student's characteristics in linguistic form. The proposed system adopted the system structured analysis and design methodology (SSADM). While Html, JavaScript for the front end design, PHP and MysQl database was used for backend and database development.

 

CHAPTER ONE

INTRODUCTION

1.1      BACKGROUND TO THE STUDY

Predicting student academic performance has long been an important research work. Among the issues of education system, questions concerning admissions into academic institutions (secondary and tertiary level) remain important (Ting, 2008). The main objective of the admission system is to determine the candidates who would likely perform well after being accepted into the school. The quality of admitted students has a great influence on the level of academic performance, 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 school (Ayan and Garcia, 2013). 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.


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