Implementation of a Machine Learning‑Based Student Dropout Prediction System: A Case Study of Borno State University, Maiduguri, Maiduguri LGA
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Background of the Study
Student retention is a critical challenge in higher education, and early identification of at-risk students is vital for implementing timely interventions. At Borno State University in Maiduguri, Maiduguri LGA, the implementation of a machine learning‑based student dropout prediction system is being explored to address this issue. Traditional methods of tracking student performance are often reactive, providing insights only after dropout rates have increased. The proposed system utilizes advanced machine learning algorithms to analyze various data sources, including academic performance, attendance records, socio‑demographic factors, and behavioral indicators. By processing large datasets, the system can identify patterns and risk factors that predict student dropout with a high degree of accuracy (Chinwe, 2023; Musa, 2024). The system’s predictive analytics enable academic advisors to implement targeted interventions, allocate resources more effectively, and ultimately improve student retention rates. Integration with existing student information systems ensures that the data used is comprehensive and up‑to‑date. Furthermore, the system provides real‑time dashboards and alerts that allow for continuous monitoring and rapid response. Although pilot projects have shown promising results in similar institutions, challenges such as data quality, algorithmic bias, and integration with legacy systems remain. The system’s design also takes into account the need for user-friendly interfaces to facilitate ease of use by academic staff and administrators. This study aims to evaluate the technical performance, predictive accuracy, and user acceptance of the dropout prediction system, and to propose strategies for addressing the challenges inherent in its implementation, thereby fostering a proactive approach to student retention (Okafor, 2025).
Statement of the Problem
Borno State University currently faces high student dropout rates that adversely impact institutional performance and student success. Traditional methods of monitoring student performance are reactive and fail to identify at‑risk students early enough for timely intervention. Although a machine learning‑based dropout prediction system offers the potential to forecast dropout risks by analyzing multifaceted data, its implementation is beset by challenges. Inaccurate or incomplete data, algorithmic bias, and the difficulty of integrating new predictive tools with existing student information systems significantly hinder the system’s effectiveness. Faculty members express concerns over the reliability of automated predictions and the potential for misinterpretation of data. Additionally, ensuring data privacy and maintaining the system’s scalability in a dynamic academic environment present further obstacles. This study aims to evaluate the performance of the dropout prediction system, comparing its outputs with historical data and manual assessments. By identifying technical and operational challenges, the research will propose strategies to improve data quality, mitigate biases, and ensure user acceptance. The ultimate goal is to establish a robust, scalable system that supports proactive academic interventions and reduces dropout rates at Borno State University (Musa, 2024).
Objectives of the Study
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To design and implement a machine learning‑based student dropout prediction system.
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To assess the system’s predictive accuracy and operational efficiency.
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To propose strategies for enhancing data quality and mitigating algorithmic bias.
Research Questions
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How accurately does the system predict student dropout compared to traditional methods?
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What are the major challenges in data integration and algorithmic bias?
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Which measures can improve user trust and system scalability?
Significance of the Study
This study is significant as it aims to reduce student dropout rates at Borno State University through the implementation of an AI‑driven predictive system. By providing early warnings and actionable insights, the system supports timely interventions and improved student retention. The findings will contribute to enhanced academic planning and resource allocation, ultimately fostering a more supportive educational environment (Chinwe, 2023).
Scope and Limitations of the Study
This study is limited to the development and evaluation of a student dropout prediction system at Borno State University, Maiduguri, Maiduguri LGA.
Definitions of Terms
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Dropout Prediction System: A digital tool that forecasts the likelihood of students discontinuing their studies.
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Machine Learning: A branch of AI that allows systems to learn from data and improve over time.
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Predictive Analytics: Techniques that use historical data to forecast future outcomes.
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