Learning-Based Student Performance Prediction System in Secondary Schools in Zaria Local Government, Kaduna State
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- NGN 5000
Background of the Study
The increasing availability of educational data has opened new opportunities for leveraging machine learning to predict and enhance student performance. In secondary schools within Zaria Local Government, Kaduna State, a machine learning-based prediction system could provide critical insights into academic performance, helping educators identify students at risk of underachievement and tailor interventions accordingly (Abdulrahman, 2023). By analyzing historical academic records, attendance data, and other relevant factors, the system can forecast future performance and suggest targeted strategies to improve learning outcomes. Such predictive analytics offer a proactive approach to educational management, shifting from reactive measures to data-driven decision making. The integration of machine learning algorithms into educational frameworks allows for continuous improvement, as the system refines its predictions based on new data inputs over time. Moreover, the system can assist teachers by highlighting patterns that may not be immediately apparent through conventional analysis, thereby facilitating more personalized learning interventions (Olu, 2024). The development of this prediction system will involve collecting comprehensive datasets, selecting appropriate machine learning models, and validating the system’s accuracy through rigorous testing. The anticipated benefits include improved student retention rates, more effective allocation of resources, and overall enhancement of the academic environment in secondary schools. This study aims to design, implement, and evaluate a machine learning-based student performance prediction system that can serve as a decision support tool for educators in Zaria Local Government, ultimately contributing to improved educational outcomes and a more efficient educational system (Chinwe, 2025).
Statement of the Problem
Secondary schools in Zaria Local Government face challenges in monitoring and improving student performance due to the lack of effective predictive tools. Traditional methods of performance evaluation often rely on periodic assessments that do not provide timely insights into student progress, making it difficult to identify and support struggling students (Ibrahim, 2023). This reactive approach results in missed opportunities for early intervention, contributing to higher dropout rates and lower overall academic achievement. Although data-driven approaches using machine learning offer a promising alternative, their adoption in local secondary schools is limited by technical constraints, insufficient training, and a lack of comprehensive data integration. The absence of a reliable performance prediction system hinders the ability of educators to implement targeted interventions and allocate resources efficiently. This study seeks to address these issues by developing a machine learning-based system that analyzes multiple data sources to predict student performance accurately. By providing early warnings and actionable insights, the system can help educators implement remedial measures before students fall significantly behind. The research will evaluate the system’s accuracy, identify key predictors of academic success, and explore challenges related to data collection and model deployment. Ultimately, the study aims to provide a framework for integrating predictive analytics into educational management, ensuring that interventions are timely, effective, and tailored to individual student needs (Udo, 2024).
Objectives of the Study:
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To design and implement a machine learning-based system for predicting student performance.
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To evaluate the system’s accuracy and effectiveness in identifying at-risk students.
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To propose strategies for integrating predictive analytics into secondary school management.
Research Questions:
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How accurately can the machine learning-based system predict student performance?
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What key factors contribute to academic success as identified by the system?
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What challenges are associated with implementing such a system in secondary schools, and how can they be addressed?
Significance of the Study
This study is significant as it explores the potential of machine learning to transform educational management by predicting student performance in secondary schools. The findings will provide valuable insights for educators and policymakers, enabling early intervention and personalized support for students. The implementation of this system could lead to improved academic outcomes and a more efficient allocation of educational resources.
Scope and Limitations of the Study:
The study is limited to the design, development, and evaluation of a machine learning-based performance prediction system for secondary schools in Zaria Local Government, Kaduna State, and does not extend to higher education or other regions.
Definitions of Terms:
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Machine Learning: A branch of artificial intelligence that uses statistical techniques to enable systems to learn from data.
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Student Performance Prediction: The process of forecasting academic outcomes based on historical and current data.
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Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes.
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