Predicting student performance using neural network

Project Abstract

Predicting student performance is a critical task in the field of education to ensure effective learning outcomes. In this research project, a neural network-based predictive model is proposed to forecast student performance based on various input features. The study aims to leverage the power of neural networks in capturing complex patterns and relationships within educational data to enhance the accuracy of performance predictions. The dataset used for training and evaluation contains information on student demographics, past academic achievements, attendance records, and other relevant attributes. By feeding this data into the neural network model, the system can learn the underlying patterns that influence student performance and make predictions for future outcomes. The neural network architecture includes multiple layers with interconnected nodes that allow for nonlinear transformations of the input data, enabling the model to extract intricate relationships that may not be apparent through traditional statistical analysis. To enhance the generalization ability of the model and prevent overfitting, techniques such as dropout regularization and cross-validation are employed during the training process. Dropout regularization helps to prevent the neural network from relying too heavily on specific features, thus improving its robustness to noise and variations in the data. Cross-validation is used to assess the model's performance on unseen data and fine-tune hyperparameters to optimize predictive accuracy. The experimental results demonstrate the effectiveness of the neural network model in predicting student performance with a high degree of accuracy. By comparing the predicted outcomes with actual student grades, the model shows promising performance metrics such as precision, recall, and F1 score. Furthermore, visualizations of the model's decision boundaries reveal the complex interactions between different input features and their impact on performance predictions. Overall, this research highlights the potential of neural networks as powerful tools for predicting student performance in educational settings. The ability to leverage complex data patterns and nonlinear relationships through neural network architectures offers a valuable approach to improving educational outcomes and providing personalized support to students. Future work may focus on refining the model architecture, exploring additional input features, and deploying the predictive system in real-world educational environments to further validate its effectiveness in enhancing student success.

Project Overview

<p> </p><div><b><b><b><p><b>INTRODUCTION</b></p><p><b>1.0 &nbsp; &nbsp; &nbsp; Background of the Study</b></p><p>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 &amp; Ekundayo, 2008). &nbsp; &nbsp; &nbsp; &nbsp; 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).</p><p>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 &amp; 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 &amp; Lawal, 2007).</p><p></p><p>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.</p><p>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).</p><p>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</p><p></p></b></b></b></div><b><b><b><div><p><b>1.1 &nbsp; &nbsp; &nbsp; Statement of the Problem</b></p><p>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.</p><p>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.</p><p>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.</p><p><b>1.2 &nbsp; &nbsp; &nbsp; Aim and Objectives</b></p><p>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</p><p>1. &nbsp; &nbsp; To determine key factors that directly or indirectly affects the performance of students</p><p>2. &nbsp; &nbsp; To transform highlighted key factors into a form that can be represented in a neural network</p><p>3. &nbsp; &nbsp; 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.</p><p><b>1.3 &nbsp; &nbsp; &nbsp; Scope of the Study</b></p><p>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.</p><p>This study spans the department of Computer Science, Federal University of Technology Minna.</p><p><b>1.4 &nbsp; &nbsp; &nbsp; Limitation of the Study</b></p><p><b></b></p><b><p>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.</p><p><b>1.5 &nbsp; &nbsp; &nbsp; Significance of the Study</b></p><p>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</p></b></div></b></b></b> <br><p></p>