Exploring the Use of Big Data for Detecting Academic Dishonesty in Nasarawa State University, Keffi, Nasarawa State
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- NGN 5000
Background of the Study
Academic dishonesty undermines the integrity of educational institutions and devalues the quality of academic credentials. At Nasarawa State University, Keffi, Nasarawa State, traditional methods of detecting cheating and plagiarism are often manual, time-consuming, and prone to error. Big data analytics presents a powerful alternative by processing large volumes of data from various digital sources, such as online submission records, examination logs, and plagiarism detection software (Ibrahim, 2023). By employing machine learning algorithms and anomaly detection techniques, a big data approach can identify unusual patterns and flag potential cases of academic misconduct in real time (Chinwe, 2024). This proactive monitoring system not only improves the accuracy of detection but also enables timely interventions that preserve academic integrity. Data visualization dashboards can provide administrators with clear insights into trends and anomalies, facilitating evidence-based policy decisions. The integration of big data for detecting academic dishonesty aligns with global best practices in higher education, where maintaining high ethical standards is paramount. However, challenges such as ensuring data privacy, integrating heterogeneous data sources, and addressing the computational demands of processing large datasets must be overcome. This study aims to develop and evaluate a big data-based system for detecting academic dishonesty at Nasarawa State University, providing a robust framework to ensure the integrity of the academic process and improve overall educational quality (Olufemi, 2025).
Statement of the Problem
The current methods for detecting academic dishonesty at Nasarawa State University are largely reliant on manual oversight and retrospective analysis, leading to delayed detection and insufficient intervention. These traditional approaches are not only labor-intensive but also susceptible to human error, which undermines the effectiveness of monitoring systems (Adebola, 2023). As a result, instances of plagiarism and cheating often go undetected until they have significantly impacted academic performance and institutional reputation. Moreover, the fragmented nature of digital data across various platforms makes it challenging to achieve a comprehensive view of student activities, thus hindering the identification of subtle or sophisticated forms of academic fraud. The lack of a unified, automated system to analyze and flag suspicious patterns in real time exacerbates the problem, leaving the institution vulnerable to ethical breaches. This study seeks to address these challenges by leveraging big data analytics to develop an integrated system that monitors academic submissions and exam behaviors continuously. By incorporating advanced algorithms for anomaly detection and pattern recognition, the proposed system aims to provide early warning signals and actionable insights, ensuring prompt intervention and the maintenance of high academic standards.
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 effectiveness of machine learning algorithms in identifying suspicious patterns.
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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 the big data system in detecting academic dishonesty?
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What indicators are most predictive of fraudulent behavior in academic settings?
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How can the system be integrated into existing monitoring processes to enhance integrity?
Significance of the Study
This study is significant as it demonstrates the application of big data analytics to detect academic dishonesty at Nasarawa State University. By providing an automated, real-time monitoring system, the research aims to enhance the integrity of the academic process, reduce instances of fraud, and support evidence-based interventions. The findings will offer valuable insights for administrators and policymakers, fostering a culture of transparency and ethical conduct in higher education (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 datasets to uncover patterns and anomalies.
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Academic Dishonesty: Behaviors such as cheating and plagiarism that violate academic integrity.
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Anomaly Detection: The process of identifying data points that deviate from established norms.
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