Development of a Machine Learning-Based Course Performance Prediction System in University of Maiduguri, Borno State
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Background of the Study
Machine learning has increasingly become a vital component in educational analytics, particularly in predicting student performance and enhancing academic outcomes. At the University of Maiduguri, Borno State, developing a machine learning-based course performance prediction system represents a strategic initiative to improve student success. By leveraging historical academic data, attendance records, and demographic information, machine learning models can forecast student performance with considerable accuracy, enabling proactive interventions (Garcia, 2023). Algorithms such as regression analysis, decision trees, and neural networks uncover complex patterns that traditional methods often overlook (Wright, 2024). This predictive capability not only assists in identifying at-risk students early but also supports targeted academic support and curriculum adjustments (Thomas, 2025).
The integration of machine learning into course performance evaluation is aligned with global trends toward data-driven decision-making in education. Such systems facilitate personalized academic advising and enhance curriculum planning by providing timely insights into student performance trends. In a region where educational challenges are acute, this technological advancement can play a pivotal role in reducing failure rates and improving overall academic standards. Moreover, the system promotes collaborative efforts among faculty, administrators, and data scientists, thereby fostering an environment of continuous improvement and innovation.
Furthermore, the iterative nature of machine learning ensures that predictive models can be continuously refined as new data become available. This dynamic process allows the system to adapt to changing educational environments and student behaviors, thereby increasing its long-term efficacy (Martinez, 2024). The adoption of this technology is not without challenges, however, as it requires substantial infrastructural investment and technical expertise to ensure reliable data collection and analysis. Nonetheless, the potential benefits in terms of enhanced academic support and improved student outcomes make the development of such a system a critical area of study.
Statement of the Problem
Despite the promising potential of machine learning-based prediction systems, the University of Maiduguri faces challenges in their effective implementation. A primary concern is the lack of high-quality, comprehensive datasets necessary for training accurate predictive models (Garcia, 2023). Fragmented and incomplete academic records lead to inaccuracies that can undermine the model’s reliability. Moreover, limited technical expertise among faculty and administrative staff further impedes the integration of advanced machine learning algorithms into the existing academic framework (Wright, 2024).
Resistance to technological change remains another barrier, as some educators are skeptical about relying on algorithmic predictions for critical academic decisions. Concerns over transparency, interpretability, and potential biases in machine learning models also contribute to this resistance (Thomas, 2025). Additionally, infrastructural limitations—such as inadequate computing resources and data storage solutions—restrict the system’s ability to process large volumes of data efficiently. These combined challenges create a significant gap between the theoretical benefits of a predictive system and its practical application. Addressing these issues is essential to develop a robust, data-driven framework that can support proactive academic interventions and enhance overall student performance.
Objectives of the Study:
- To develop a machine learning model that accurately predicts course performance at the University of Maiduguri.
- To identify key factors influencing student academic outcomes using historical data.
- To propose actionable strategies for integrating the prediction system into existing academic support frameworks.
Research Questions:
- What are the most significant predictors of course performance at the University of Maiduguri?
- How can machine learning algorithms be optimized to improve prediction accuracy?
- What challenges and opportunities arise when integrating a machine learning-based prediction system into academic support structures?
Significance of the Study
This study is significant as it explores the potential of machine learning to enhance academic performance prediction at the University of Maiduguri. The research provides valuable insights into key factors affecting course outcomes and offers innovative strategies for early intervention and support. By leveraging data-driven techniques, the study aims to contribute to improved student retention and academic success, benefitting educators, administrators, and policymakers alike (Lee, 2023).
Scope and Limitations of the Study:
This study is limited to the development and evaluation of a machine learning-based course performance prediction system at the University of Maiduguri, Borno State, and does not extend to other predictive analytics applications.
Definitions of Terms:
- Machine Learning: A subset of artificial intelligence that uses algorithms to learn from and make predictions based on data (Garcia, 2023).
- Course Performance: Measurable academic outcomes, including grades and assessment scores, in a given course (Wright, 2024).
- Predictive Model: A statistical model that uses historical data to forecast future outcomes (Thomas, 2025).
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