Enhancing Student Academic Performance Prediction Models Using Deep Learning in Federal University Gashua, Yobe State
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Background of the Study
Predicting student academic performance accurately is crucial for implementing timely interventions and enhancing educational outcomes. At Federal University Gashua, Yobe State, traditional predictive models based on conventional statistical methods have proven insufficient due to their inability to capture complex, non-linear relationships inherent in educational data. Deep learning, a subset of machine learning, offers advanced computational techniques capable of processing vast amounts of data and learning intricate patterns that can improve performance predictions (Ibrahim, 2023). By employing neural networks with multiple layers, deep learning models can analyze diverse datasets—such as historical grades, attendance records, and engagement metrics—to forecast student outcomes with high precision. These models continuously improve as they are exposed to new data, enabling dynamic and adaptive prediction capabilities (Chinwe, 2024). The integration of deep learning in academic performance prediction supports early identification of at-risk students, thereby facilitating proactive support measures and personalized learning interventions. Furthermore, deep learning models can be integrated with real-time data systems, ensuring that predictions remain current and actionable. Despite these advantages, challenges such as the need for extensive, high-quality datasets, high computational costs, and the complexity of model interpretability remain. This study aims to develop and evaluate deep learning-based models for predicting student academic performance at Federal University Gashua, comparing their accuracy with traditional methods and proposing strategies for effective implementation (Olufemi, 2025).
Statement of the Problem
The current student performance prediction methods at Federal University Gashua rely on traditional statistical techniques that do not capture the non-linear and multifaceted nature of academic data. This limitation results in imprecise predictions, delaying the identification of at-risk students and hindering the timely implementation of remedial measures (Adebola, 2023). The inadequacy of conventional models is compounded by the increasing volume and complexity of educational data, which these methods cannot efficiently process. As a result, administrators face challenges in allocating resources and designing interventions that are tailored to individual student needs. The absence of advanced predictive models leads to a reactive approach, where issues are addressed only after significant academic decline has occurred. Deep learning presents a promising solution by utilizing neural networks to extract complex patterns from extensive datasets, offering a more accurate and dynamic prediction of academic performance. However, the implementation of deep learning models faces challenges such as the requirement for large, high-quality datasets, significant computational resources, and difficulties in interpreting the outputs for practical use. This study aims to bridge this gap by developing a deep learning-based model to forecast student performance, providing a reliable tool for early intervention and strategic academic planning, and comparing its performance against traditional methods to highlight its potential benefits.
Objectives of the Study:
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To develop a deep learning-based model for predicting student academic performance.
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To evaluate and compare the predictive accuracy of deep learning and traditional models.
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To recommend strategies for integrating deep learning into student performance monitoring systems.
Research Questions:
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How does a deep learning-based model compare to traditional methods in predicting student performance?
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What factors most significantly influence academic outcomes according to the deep learning model?
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How can the model be effectively integrated into early intervention strategies?
Significance of the Study
This study is significant as it leverages deep learning to enhance the accuracy of student performance predictions at Federal University Gashua, thereby enabling timely interventions and improved academic outcomes. The findings will provide critical insights for educators and policymakers, supporting the adoption of advanced AI techniques in educational analytics and fostering data-driven decision-making to boost student success (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to developing and evaluating a deep learning-based student performance prediction model at Federal University Gashua, Yobe State, and does not extend to other predictive applications or institutions.
Definitions of Terms:
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Deep Learning: A machine learning technique using neural networks with multiple layers to model complex patterns.
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Predictive Model: A system that forecasts future outcomes based on historical data.
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Academic Performance: Measurable student achievements, typically assessed through grades and test scores.
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