Design of a Big Data-Driven Early Warning System for Academic Probation in University of Abuja, FCT
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- NGN 5000
Background of the Study
Academic probation is a critical intervention aimed at supporting students who are at risk of failing to meet academic standards. At the University of Abuja in FCT, early identification of students who may require additional support is essential for preventing academic failure and ensuring student retention. The integration of big data analytics into an early warning system offers a transformative approach by harnessing vast amounts of academic and behavioral data to predict which students are likely to face academic difficulties (Adebola, 2023). By analyzing indicators such as course grades, attendance records, assignment submissions, and participation in extracurricular activities, advanced algorithms can detect early signs of academic struggle and trigger timely interventions. Big data-driven systems provide real-time monitoring and dynamic risk assessment, enabling administrators to tailor support strategies to individual student needs. Furthermore, visualization tools and dashboards can facilitate communication among academic advisors, enabling a coordinated approach to student support (Ibrahim, 2024). The use of such systems aligns with global trends in educational analytics, where data-driven decision-making is becoming increasingly vital for improving academic outcomes. However, the implementation of an early warning system based on big data faces challenges including data integration from disparate sources, ensuring data accuracy and privacy, and the need for technical expertise in advanced analytics. This study aims to design and evaluate a big data-driven early warning system for academic probation at the University of Abuja. The research will assess the system’s predictive accuracy, its impact on student retention, and provide recommendations for its integration into the university’s academic support framework (Chinwe, 2025).
Statement of the Problem
The current methods for identifying at-risk students at the University of Abuja rely predominantly on manual review of academic records and periodic assessments, which are often reactive and insufficient for timely intervention. This delay in identifying students who are at risk of falling below academic standards leads to missed opportunities for early support, resulting in higher dropout rates and academic probation cases (Olufemi, 2023). Traditional approaches do not leverage the vast amounts of data generated by modern educational systems, leading to a lack of precision in early warning signals. Additionally, fragmented data sources and inconsistent record-keeping practices further impede the ability to accurately monitor student performance trends in real time. The absence of a comprehensive, data-driven early warning system hinders proactive measures that could prevent academic failure and improve student outcomes. This study seeks to address these challenges by developing a big data-driven model that integrates academic, behavioral, and demographic data to predict students at risk of academic probation. The research will evaluate the system’s performance, identify critical predictors of academic difficulties, and propose strategies for its effective implementation. Through this approach, the study aims to facilitate timely interventions, thereby reducing the incidence of academic probation and improving overall student success.
Objectives of the Study:
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To design a big data-driven early warning system for academic probation.
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To evaluate the system’s predictive accuracy and impact on student retention.
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To recommend strategies for integrating the system into the university’s academic support framework.
Research Questions:
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How effectively does the early warning system predict at-risk students?
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What are the key indicators of academic probation as identified by the system?
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How can the system be integrated into existing academic support processes?
Significance of the Study
This study is significant as it explores the application of big data analytics to develop an early warning system for academic probation at the University of Abuja. By enabling timely identification and intervention for at-risk students, the system promises to enhance student retention and academic performance. The research offers actionable insights for educators and administrators, contributing to data-driven educational support and improved overall institutional effectiveness (Adebola, 2023).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a big data-driven early warning system for academic probation at the University of Abuja, FCT, and does not extend to other intervention strategies or institutions.
Definitions of Terms:
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Early Warning System: A framework designed to detect potential issues before they escalate.
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Big Data Analytics: The process of examining large datasets to uncover patterns and trends.
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Academic Probation: A status assigned to students whose academic performance falls below established standards.
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