Design of a Big Data-Based System for Monitoring Academic Integrity in Federal University Birnin Kebbi, Kebbi State
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Background of the Study
Academic integrity is fundamental to the credibility of educational institutions, yet traditional monitoring methods often fall short in detecting fraudulent practices. At Federal University Birnin Kebbi, Kebbi State, the use of a big data-based system for monitoring academic integrity offers a robust solution by automating the detection of unethical behaviors. Such a system can integrate data from multiple sources, including digital submissions, online examinations, and plagiarism detection tools, to provide a comprehensive overview of academic activities (Ibrahim, 2023). By employing machine learning algorithms and anomaly detection techniques, the system can identify irregular patterns that may indicate instances of cheating or plagiarism. Real-time data processing enables immediate intervention, thereby reducing the window for academic fraud to occur (Chinwe, 2024). The use of data visualization dashboards further empowers administrators to monitor trends and make informed decisions, fostering a culture of accountability and transparency. However, the development and implementation of such systems are challenged by issues related to data privacy, the integration of heterogeneous datasets, and the computational resources required to process large volumes of data. This study aims to design and evaluate a big data-based system that enhances academic integrity monitoring by providing timely, accurate, and actionable insights. The goal is to ensure that Federal University Birnin Kebbi maintains high standards of academic honesty and supports continuous improvement in assessment processes (Olufemi, 2025).
Statement of the Problem
Traditional methods of monitoring academic integrity at Federal University Birnin Kebbi are predominantly manual and retrospective, leading to delayed detection of academic misconduct such as plagiarism and cheating. These conventional approaches are labor-intensive, inconsistent, and prone to human error, thereby undermining the integrity of academic evaluations (Adebola, 2023). The fragmented nature of data across various platforms further complicates the monitoring process, resulting in incomplete oversight and missed opportunities for early intervention. Without an integrated system, unethical practices may persist, compromising the quality of education and damaging the institution’s reputation. The absence of a real-time, big data-based monitoring system prevents administrators from promptly identifying and addressing instances of academic dishonesty. This study seeks to address these challenges by developing an automated system that leverages big data analytics to continuously monitor academic activities and detect anomalies indicative of misconduct. The system will integrate diverse data sources and utilize advanced analytical techniques to provide a comprehensive, real-time overview of academic integrity. By doing so, the study aims to enhance the transparency and reliability of the monitoring process, thereby supporting proactive intervention and upholding academic standards.
Objectives of the Study:
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To design a big data-based system for monitoring academic integrity.
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To evaluate the system’s effectiveness in detecting academic misconduct.
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To propose strategies for integrating the system into existing academic processes.
Research Questions:
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How can big data analytics improve the monitoring of academic integrity?
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What are the key indicators of academic dishonesty as detected by the system?
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How can the system be effectively integrated into current academic monitoring practices?
Significance of the Study
This study is significant as it presents a big data-based system designed to enhance the monitoring of academic integrity at Federal University Birnin Kebbi. The system’s ability to provide real-time, accurate detection of academic misconduct will improve the quality of education and maintain institutional credibility. The findings will offer actionable insights for administrators and policymakers to strengthen academic oversight and promote a culture of ethical behavior in higher education (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the design and evaluation of a big data-based academic integrity monitoring system at Federal University Birnin Kebbi, Kebbi State, and does not extend to other types of academic monitoring or institutions.
Definitions of Terms:
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Big Data-Based System: A system that utilizes large-scale data analytics for decision-making.
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Academic Integrity: The adherence to ethical standards in academic work.
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Plagiarism Detection: Techniques used to identify unoriginal content in academic submissions.
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