Enhancing Student Performance Prediction Models Using Deep Learning in Federal University Gashua, Yobe State
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Background of the Study
Accurately predicting student performance is crucial for the development of effective educational strategies and timely interventions. At Federal University Gashua, Yobe State, traditional statistical methods for performance prediction often fall short in capturing the complex, non-linear relationships inherent in educational data. Deep learning, a subset of artificial intelligence, has emerged as a powerful tool capable of processing large datasets and extracting intricate patterns that are otherwise difficult to detect using conventional methods (Ibrahim, 2023). By employing neural networks and deep learning architectures, predictive models can analyze various factors such as past academic records, attendance, participation in online learning, and socio-demographic data to forecast student outcomes with high accuracy. These models continuously learn and improve as more data is collected, making them particularly well-suited for dynamic educational environments. The application of deep learning in performance prediction not only enables early identification of at-risk students but also supports the customization of instructional strategies to meet individual learning needs (Chinwe, 2024). Furthermore, integration with real-time data feeds allows for continuous monitoring and adjustment of predictive models, ensuring that predictions remain current and reliable. Despite its potential, the implementation of deep learning models faces challenges such as high computational costs, the need for large, high-quality datasets, and the difficulty of interpreting complex model outputs. This study aims to develop and evaluate deep learning-based student performance prediction models at Federal University Gashua, with the objective of enhancing early intervention strategies and improving overall academic success (Olufemi, 2025).
Statement of the Problem
The current methods for predicting student performance at Federal University Gashua are predominantly based on traditional statistical techniques, which often fail to capture the complex and non-linear interactions present in educational data. This limitation results in inaccurate predictions and delayed interventions, which can adversely affect student outcomes (Adebola, 2023). Inadequate prediction models hinder the university’s ability to identify students who are at risk of underperformance in a timely manner, thereby preventing the implementation of effective remedial strategies. Furthermore, the reliance on conventional methods does not allow for continuous adaptation as new data becomes available, resulting in static models that quickly become outdated. The absence of deep learning approaches in the current system limits the potential for harnessing the full predictive power of available data. Challenges such as insufficient computational resources, data quality issues, and the complexity of deep learning algorithms further impede the development and integration of advanced predictive models. This study seeks to address these issues by developing a deep learning-based predictive model that leverages comprehensive student data to accurately forecast academic performance. By comparing the deep learning model with traditional methods, the research will identify key factors that contribute to student success and provide actionable recommendations for early intervention. The ultimate goal is to create a dynamic, continuously improving prediction system that supports proactive academic planning and enhances overall student achievement.
Objectives of the Study:
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To develop a deep learning-based predictive model for student performance.
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To evaluate the model’s accuracy compared to traditional prediction methods.
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To propose strategies for integrating the predictive model into academic support 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 key factors contribute to accurate performance predictions?
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How can the predictive model be effectively integrated into early intervention strategies?
Significance of the Study
This study is significant as it leverages deep learning to enhance student performance prediction, enabling timely and personalized academic interventions at Federal University Gashua. The advanced predictive model will improve the accuracy of forecasts and support data-driven decision-making, ultimately boosting student outcomes. The findings provide valuable insights for educators and administrators, facilitating the integration of innovative AI techniques in higher education (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a deep learning-based student performance prediction model at Federal University Gashua, Yobe State, and does not extend to other academic institutions or non-performance related predictions.
Definitions of Terms:
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Deep Learning: A subset of machine learning that uses neural networks with multiple layers to analyze data.
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Predictive Model: A system that forecasts future outcomes based on historical data.
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Student Performance: The academic achievement of students, typically measured through grades and assessment scores.
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