Exploring the Use of Big Data for Detecting Academic Dishonesty in Nasarawa State University, Keffi, Nasarawa State
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Background of the Study
Academic dishonesty poses a significant threat to the integrity of educational institutions. At Nasarawa State University, Keffi, Nasarawa State, traditional methods for detecting academic misconduct, such as manual review of examination papers and plagiarism checks, are often labor-intensive, time-consuming, and subject to human error. With the advent of big data analytics, there is an opportunity to develop automated systems that can effectively monitor and detect instances of academic dishonesty by analyzing large volumes of student data (Ibrahim, 2023). Big data approaches can integrate information from various sources, including digital submission logs, online exam monitoring, and plagiarism detection software, to identify patterns and anomalies that suggest academic fraud. Machine learning algorithms and anomaly detection techniques are particularly useful in discerning subtle indicators of misconduct that might be missed through conventional methods (Chinwe, 2024). The use of real-time data processing and predictive modeling allows for continuous monitoring and immediate response, thereby reducing the occurrence of dishonest practices and upholding academic standards. Furthermore, data visualization tools can assist administrators in interpreting complex data sets and making informed decisions about policy changes. Despite its potential, challenges such as data privacy concerns, the integration of disparate data systems, and the technical expertise required for effective implementation remain significant. This study aims to explore the feasibility and effectiveness of a big data-based system for detecting academic dishonesty at Nasarawa State University, providing a robust framework that ensures academic integrity and enhances the credibility of assessment processes (Olufemi, 2025).
Statement of the Problem
Academic dishonesty is a pervasive issue at Nasarawa State University that undermines the credibility of academic evaluations and compromises the quality of education. Traditional detection methods are limited by their manual nature, which not only increases the likelihood of human error but also delays the identification of misconduct. The fragmented data sources and lack of integration between existing systems hinder the ability to conduct comprehensive analyses of student activities, making it difficult to detect subtle or sophisticated forms of cheating (Adebola, 2023). Without a robust, automated system, instances of plagiarism and exam fraud may go unnoticed until they have significantly impacted academic integrity. The absence of real-time monitoring further exacerbates this issue, as proactive interventions cannot be implemented promptly. Additionally, concerns about data privacy and the ethical use of student information complicate the development of such systems. This study seeks to address these challenges by investigating the use of big data analytics for detecting academic dishonesty. By developing and testing advanced machine learning algorithms on integrated data sets, the research aims to create a system that provides timely alerts and actionable insights for administrators. The ultimate goal is to enhance the integrity of the academic process and maintain high educational standards at Nasarawa State University by reducing the incidence of academic fraud.
Objectives of the Study:
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To develop a big data-based system for detecting academic dishonesty.
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To evaluate the accuracy and efficiency of machine learning algorithms in identifying misconduct.
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To recommend strategies for integrating the system into the university’s academic integrity framework.
Research Questions:
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How effective is big data analytics in detecting academic dishonesty?
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What are the key indicators of academic misconduct in the data?
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How can the system be integrated into existing university processes to improve detection?
Significance of the Study
This study is significant as it harnesses big data analytics to enhance the detection of academic dishonesty at Nasarawa State University. By automating the monitoring process and providing real-time insights, the system aims to uphold academic integrity and protect the quality of education. The findings will offer valuable guidance for university administrators and contribute to the development of robust, data-driven mechanisms to combat academic fraud (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the use of big data analytics for detecting academic dishonesty at Nasarawa State University, Keffi, Nasarawa State, and does not extend to other types of academic misconduct or institutions.
Definitions of Terms:
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Big Data Analytics: Techniques for analyzing large volumes of diverse data to uncover patterns.
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Academic Dishonesty: Unethical behaviors such as cheating and plagiarism in academic settings.
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Anomaly Detection: The identification of unusual patterns that deviate from expected behavior.
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