ITDS | SPMS

PREDICTING STUDENT’S PERFORMANCE USING ARTIFICIAL NEURAL NETWORKS

Project Description
Supervisor

This study investigates the development and application of an Artificial Neural Network (ANN) model to predict student academic performance in higher education. By incorporating a diverse set of factors, including demographics, study habits, extracurricular activities, and parental support, the model offers a comprehensive approach to understanding the elements that contribute to academic success. Unlike traditional models that primarily rely on past academic records, this ANN model leverages both academic and non-academic variables to enhance predictive accuracy. The research highlights the value of early identification of at-risk students, enabling timely interventions and personalized learning strategies. The ANN model demonstrated moderate predictive accuracy, suggesting its potential for practical use in educational settings. Key recommendations for universities include the integration of predictive tools into learning management systems, improving data collection, and ensuring ethical use of student data. The findings underscore the potential of using predictive analytics to support better decision-making, optimize resource allocation, and improve student outcomes in higher education.

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

To develop and evaluate an artificial neural network model that predicts university students' academic performance based on a diverse set of non-academic factors. This model will serve as a tool to support students in making informed decisions about their academic paths and assist university administrators in implementing targeted support strategies.

2023/2024

Feb 12, 2026

OPOKU OFORIWAAH PATRICIA (UEB3213922), OSEI ROBERT (UEB3215620), OFORI ALFRED JOSHUA (UEB3208520), KOTEI NIKOI CHRISTIANA, (UEB3203820), AWUDU MUFTAWU BAKARI (UEB3211120)

BSc Information Technology