Exploring the Application of Machine Learning in Student Performance Prediction in Secondary Schools in Gusau Local Government Area, Zamfara State
- Project Research
- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the Study
Machine Learning (ML), a subset of Artificial Intelligence (AI), is an advanced technique that enables systems to learn from data and make predictions or decisions without explicit programming. In education, machine learning is increasingly used to predict student performance based on various factors such as attendance, participation, past grades, and socio-economic background. By using historical data, ML models can provide insights into which students may need additional support, helping educators make timely interventions to improve learning outcomes.
In Gusau Local Government Area, Zamfara State, the application of machine learning to predict student performance remains largely unexplored. Despite the potential of ML to enhance the educational experience, challenges such as data availability, lack of technological infrastructure, and limited awareness among educators and school administrators may hinder its implementation. This study aims to explore the potential of machine learning in predicting student performance in secondary schools in Gusau and assess its effectiveness.
Statement of the Problem
The use of machine learning in predicting student performance is still in its early stages in Gusau Local Government Area, with schools lacking the necessary infrastructure and expertise to fully adopt this technology. As a result, the benefits of early intervention in student learning remain largely untapped. This study seeks to explore how machine learning models can be applied to predict student performance and identify at-risk students, thus enabling timely interventions and improved educational outcomes.
Objectives of the Study
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To explore the application of machine learning in predicting student performance in secondary schools in Gusau Local Government Area.
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To assess the accuracy and reliability of machine learning models in predicting student performance.
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To identify the challenges and benefits of using machine learning for student performance prediction.
Research Questions
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How can machine learning be applied to predict student performance in secondary schools in Gusau Local Government Area?
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How accurate and reliable are machine learning models in predicting student performance?
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What are the challenges and benefits associated with the use of machine learning for student performance prediction?
Research Hypotheses
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Machine learning can be effectively applied to predict student performance in secondary schools in Gusau Local Government Area.
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Machine learning models provide accurate and reliable predictions of student performance.
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There are significant challenges and benefits in using machine learning for predicting student performance in secondary schools.
Significance of the Study
This study will provide valuable insights into the application of machine learning in education, particularly in predicting student performance. The findings will help school administrators, educators, and policymakers make informed decisions about the adoption of technology to improve student learning outcomes.
Scope and Limitations of the Study
The study will focus on the use of machine learning for student performance prediction in secondary schools in Gusau Local Government Area, Zamfara State. Limitations include access to quality student data, the level of technological infrastructure in schools, and the availability of trained personnel to implement machine learning models.
Definitions of Terms
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Machine Learning (ML): A subset of AI that enables systems to learn and make predictions based on data without explicit programming.
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Student Performance Prediction: The use of data-driven models to forecast students’ academic achievements or outcomes.
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Educational Data: Information collected from students and educational systems, such as grades, attendance, and participation, used for analysis and predictions.
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