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Predicting student performance using neural network

 

Table Of Contents


Chapter ONE

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Research
1.9 Definition of Terms

Chapter TWO

2.1 Overview of Neural Networks
2.2 History of Neural Networks
2.3 Types of Neural Networks
2.4 Applications of Neural Networks
2.5 Neural Networks in Education
2.6 Neural Networks in Predictive Analysis
2.7 Strengths of Neural Networks
2.8 Limitations of Neural Networks
2.9 Neural Networks vs Traditional Methods
2.10 Future Trends in Neural Networks

Chapter THREE

3.1 Research Design
3.2 Sampling Techniques
3.3 Data Collection Methods
3.4 Data Analysis Techniques
3.5 Variables and Measures
3.6 Ethical Considerations
3.7 Research Validity and Reliability
3.8 Research Limitations

Chapter FOUR

4.1 Overview of Findings
4.2 Demographic Analysis
4.3 Performance Prediction Results
4.4 Factors Influencing Predictions
4.5 Comparison with Other Models
4.6 Discussion on Accuracy and Precision
4.7 Implications of Findings
4.8 Recommendations for Future Research

Chapter FIVE

5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to Knowledge
5.4 Practical Implications
5.5 Limitations of the Study
5.6 Recommendations for Practice
5.7 Recommendations for Further Research
5.8 Conclusion Statement

Thesis Abstract

Abstract
Student performance prediction is a critical task in educational institutions to identify students who may be at risk of academic difficulties. Traditional methods of predicting student performance have limitations in terms of accuracy and efficiency. This research focuses on the application of neural networks for predicting student performance based on various input features such as demographic information, previous academic records, and behavioral data. The study utilizes a dataset containing information on a large number of students, including their attendance, grades, and socio-economic background. A neural network model is developed to analyze this data and predict the performance of students in terms of their grades or likelihood of dropping out. The neural network architecture includes multiple hidden layers to capture complex patterns and relationships within the input data. To train the neural network model, the dataset is divided into training and testing sets for evaluation. Various neural network parameters are optimized through techniques such as grid search and cross-validation to enhance the model's performance. The trained model is then used to predict student performance on the testing set and evaluate its accuracy using metrics such as accuracy, precision, recall, and F1 score. The results demonstrate that the neural network model outperforms traditional methods of student performance prediction by achieving higher accuracy and predictive power. The model successfully identifies students at risk of academic difficulties with a high degree of precision, enabling educational institutions to intervene early and provide necessary support. Additionally, the model can be used to identify factors that significantly influence student performance, helping educators tailor interventions to address specific needs. The research contributes to the field of educational data mining by showcasing the effectiveness of neural networks in predicting student performance. By leveraging the power of neural networks, educational institutions can enhance their student support systems and improve overall academic outcomes. Future research directions include exploring the use of more advanced neural network architectures, incorporating additional data sources, and developing personalized intervention strategies based on predicted student performance. In conclusion, this research demonstrates the potential of neural networks in predicting student performance and providing valuable insights for educational institutions to support student success.

Thesis Overview

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).

A prospective candidate must acquire at least a five credit pass in relevant senior secondary school subjects include Mathematics and English and also to score the required score in the Unified Tertiary Matriculation Examination (UTME) for the desired choice of institution and course (Salim, 2006). The guidelines set by the federal government for admission into institution of higher learning (State, Federal and Private Universities) are based on quota systems in which 45% of candidates are admitted on merit, 35% on catchment and 20% on educationally less developed states mostly the northern states.(Bakwaph, 2013). Considering the statistics above only 45% of students are admitted based on high academic performance, the admission system has failed due to cheating, bribery for admission, exam scores manipulation and most of the competent candidates are denied admission (Moja, 2000), although the admission policy provides equitable admission into the universities.

This situation has affected the standard of students being admitted into tertiary institutions, students admitted find it difficult to pass their first year courses, and those who passes do have poor grades, greater number of university graduates in Nigeria graduates with grades below the second class upper division and the number of student spilling over is increasing regularly and a worse scenario is the case in which graduates from tertiary institutions are not productive in the labor market due to the fact that they perform below expectation of the employers (Ajaja 2010).

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. Hence this study takes a different approach to solve the problem of admission by exploring how to use an artificial neural network model to predict the performance of a candidate before offering the candidate admission. An artificial neural network has the ability to learn and extrapolate patterns therefore predicting student performance will be based on extrapolating patterns from historical data of previous student and their respective performance

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


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