Comparative Study of Machine Learning Algorithms for Exam Score Prediction in Secondary Schools in Yola South Local Government, Adamawa State
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- NGN 5000
Background of the Study
The ability to accurately predict exam scores is crucial for identifying students who may require additional support and for enhancing overall educational strategies. In secondary schools within Yola South Local Government, Adamawa State, machine learning algorithms offer a promising approach to forecast exam performance by analyzing historical academic data and various influencing factors. Machine learning techniques such as linear regression, support vector machines, and ensemble methods have demonstrated potential in educational data mining, providing insights into student performance trends (Abdullahi, 2023). By processing data related to attendance, previous exam results, socio-economic background, and behavioral indicators, these algorithms can generate predictive models that help educators identify students at risk of underperformance. The comparative study of different machine learning algorithms is essential to determine which models yield the highest accuracy and reliability in the context of exam score prediction. Recent research has highlighted the advantages of using ensemble techniques that combine multiple models to improve prediction accuracy and reduce overfitting (Olu, 2024). Moreover, the integration of these predictive models into school management systems can facilitate timely interventions and tailored educational support. Despite the promising potential of machine learning in predicting academic outcomes, several challenges remain, including data sparsity, quality issues, and the need for domain-specific model tuning. This study aims to compare various machine learning algorithms for exam score prediction in secondary schools, assessing their performance in terms of accuracy, precision, and robustness. The research will also explore the factors that most significantly impact exam outcomes, providing a comprehensive analysis that can inform policy and practice in Adamawa State’s educational sector (Chinwe, 2025).
Statement of the Problem
Secondary schools in Yola South Local Government face persistent challenges in accurately predicting student exam scores, leading to delayed interventions and insufficient support for struggling students. Traditional methods of performance evaluation are often inadequate, relying on periodic assessments that do not account for the multifaceted factors affecting student performance (Ibrahim, 2023). Although machine learning algorithms offer a data-driven approach to exam score prediction, their application in this context is hindered by challenges such as limited data availability, inconsistent data quality, and the complexity of model selection and tuning. As a result, schools may not be able to effectively identify at-risk students early enough to provide the necessary remedial support. Moreover, the absence of a standardized framework for comparing the performance of different algorithms further complicates the decision-making process. This study seeks to address these issues by conducting a comparative analysis of various machine learning algorithms to determine the most effective approach for exam score prediction in secondary schools. The research will evaluate the performance of each algorithm based on accuracy, precision, and overall reliability, while also identifying key predictors of exam success. By addressing the methodological challenges and providing empirical evidence on the best practices, the study aims to contribute to improved academic planning and targeted interventions in Yola South Local Government (Udo, 2024).
Objectives of the Study:
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To compare the performance of various machine learning algorithms for exam score prediction.
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To identify key factors that significantly influence exam outcomes.
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To propose a standardized framework for predictive analytics in secondary schools.
Research Questions:
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Which machine learning algorithm provides the highest accuracy in predicting exam scores?
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What are the most critical factors influencing student performance in exams?
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How can the predictive framework be standardized and implemented effectively?
Significance of the Study
This study is significant as it evaluates the effectiveness of machine learning algorithms in predicting exam scores in secondary schools, providing a data-driven approach to early intervention. The findings will offer valuable insights for educators and policymakers, enabling the development of tailored support strategies that enhance academic outcomes and reduce failure rates in Yola South Local Government.
Scope and Limitations of the Study:
The study is limited to comparing machine learning algorithms for exam score prediction in secondary schools in Yola South Local Government, Adamawa State, and does not extend to higher education or other regions.
Definitions of Terms:
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Machine Learning Algorithms: Computational methods used to learn patterns from data and make predictions.
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Exam Score Prediction: The process of forecasting student performance based on historical and current data.
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Ensemble Methods: Techniques that combine multiple machine learning models to improve predictive accuracy.
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