Analysis of Machine Learning Algorithms for Detecting Examination Malpractices in Senior Secondary Schools in Jalingo LGA, Taraba State
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- NGN 5000
Background of the Study
Examination malpractice is a major challenge in the education sector, especially in secondary schools. The rise of digital technology has made it easier to detect and prevent various forms of malpractice using machine learning algorithms. In Jalingo Local Government Area, Taraba State, senior secondary schools face significant issues with examination malpractice, such as cheating, impersonation, and use of unauthorized materials. Machine learning models can analyze patterns in student behavior, exam conditions, and performance to detect potential malpractice (Ibrahim & Bello, 2024). By applying these models, schools can implement preventive measures, ensuring fair and credible examinations.
Statement of the Problem
Examination malpractice in senior secondary schools in Jalingo is widespread and undermines the integrity of the education system. Traditional methods of detecting malpractice, such as manual monitoring and surveillance, are not sufficient to address the growing complexity of cheating methods. This study aims to explore the use of machine learning algorithms to identify patterns indicative of malpractice, thereby improving the fairness and credibility of examinations.
Objectives of the Study
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To analyze the types of examination malpractice prevalent in senior secondary schools in Jalingo.
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To evaluate the performance of various machine learning algorithms in detecting examination malpractice.
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To propose an optimized machine learning model for detecting and preventing examination malpractice in the schools.
Research Questions
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What are the common forms of examination malpractice in senior secondary schools in Jalingo?
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How effective are machine learning algorithms in detecting different types of examination malpractice?
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What are the best machine learning models for identifying and preventing examination malpractice in the schools?
Research Hypotheses
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Machine learning algorithms can effectively detect examination malpractice with higher accuracy than traditional methods.
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There is a significant relationship between the type of machine learning algorithm used and its effectiveness in detecting malpractice.
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The implementation of machine learning models will significantly reduce the occurrence of examination malpractice in senior secondary schools.
Significance of the Study
This study will provide secondary schools in Jalingo with a reliable and efficient system for detecting and preventing examination malpractice, enhancing the integrity of the examination process. The findings will contribute to the use of machine learning in educational integrity and provide a foundation for further research in the application of AI in education.
Scope and Limitations of the Study
The study will focus on senior secondary schools in Jalingo Local Government Area, Taraba State. It will analyze various machine learning algorithms and their performance in detecting examination malpractice. Limitations include data accessibility and the need for comprehensive datasets to train the algorithms effectively.
Definitions of Terms
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Examination Malpractice: Any form of dishonest or unfair conduct during examinations, such as cheating, impersonation, or the use of unauthorized materials.
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Machine Learning Algorithms: Algorithms that use statistical models to learn from data and make predictions or decisions without being explicitly programmed.
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Detection: The process of identifying abnormal patterns or behaviors that suggest malpractice.
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