Predicting student performance using neural network
Table Of Contents
Chapter ONE
1.1 Introduction1.2 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 Networks2.2 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 Modeling
2.7 Challenges in Neural Network Implementation
2.8 Neural Networks vs Traditional Methods
2.9 Neural Network Algorithms
2.10 Future Trends in Neural Networks
Chapter THREE
3.1 Research Methodology Overview3.2 Research Design
3.3 Data Collection Methods
3.4 Sampling Techniques
3.5 Data Analysis Procedures
3.6 Validation Methods
3.7 Ethical Considerations
3.8 Limitations of the Methodology
Chapter FOUR
4.1 Introduction to Findings4.2 Demographic Analysis
4.3 Performance Prediction Results
4.4 Factors Influencing Performance
4.5 Comparison with Traditional Methods
4.6 Discussion on Accuracy and Reliability
4.7 Implications of Findings
4.8 Recommendations for Future Research
Chapter FIVE
5.1 Summary of Findings5.2 Conclusions Drawn
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 Final Thoughts and Closing Remarks
Project Abstract
AbstractWith the increasing availability of educational data, there is a growing interest in predicting student performance to help educators intervene early and improve outcomes. In this study, we explore the use of neural networks for predicting student performance based on various academic and demographic features. The dataset used consists of information on student demographics, previous academic performance, study habits, and various socio-economic factors. We preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features. We then build a neural network model using TensorFlow with multiple hidden layers to capture complex relationships within the data. The model is trained on a portion of the dataset and evaluated using metrics such as accuracy, precision, recall, and F1 score. Our results show that the neural network model outperforms traditional machine learning algorithms such as logistic regression and decision trees in predicting student performance. The model achieves high accuracy and F1 score, indicating its efficacy in identifying students at risk of underperforming. By analyzing the model's predictions, we can gain insights into factors that impact student performance and tailor interventions to support struggling students. Furthermore, we conduct feature importance analysis to identify the most influential variables in predicting student performance. This analysis highlights the importance of factors such as previous academic achievements, study time, and parental education level in determining student success. By understanding these key predictors, educators can focus their efforts on addressing specific areas to enhance student outcomes. Overall, our study demonstrates the potential of neural networks in predicting student performance and providing valuable insights for educational stakeholders. By leveraging advanced machine learning techniques, educators can proactively identify students who may require additional support and implement targeted interventions to improve overall academic success. This research contributes to the growing body of literature on using data-driven approaches to enhance educational practices and support student achievement.
Project 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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