Exploring the Use of Big Data in University Student Behavior Prediction in Federal University Lokoja, Kogi State
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- NGN 5000
Background of the Study
Understanding student behavior is essential for improving academic support and enhancing institutional performance. At Federal University Lokoja, Kogi State, traditional methods of predicting student behavior have relied on limited historical data and manual interpretation, which often fail to capture the complexity and dynamism of modern student populations. The emergence of big data analytics has created new opportunities for behavior prediction by enabling the analysis of vast and diverse datasets, including academic records, attendance logs, social media interactions, and demographic information (Chinwe, 2023). Big data techniques employ sophisticated algorithms to uncover hidden patterns and correlations that can predict student outcomes such as academic performance, retention, and engagement levels. These insights can be used to develop targeted interventions that address the specific needs of at-risk students and optimize resource allocation across the institution (Ibrahim, 2024). Moreover, predictive models developed using big data can continuously learn and adapt to new trends, thereby providing real-time insights into student behavior. However, despite these promising advancements, the integration of big data analytics in behavior prediction is challenged by issues of data quality, privacy, and the complexity of unstructured data. Federal University Lokoja must overcome these obstacles to harness the full potential of big data for proactive student support. This study aims to investigate the effectiveness of big data in predicting student behavior by comparing traditional prediction methods with data-driven approaches, ultimately providing a framework for enhanced behavioral analysis that can inform institutional strategies and support services (Musa, 2025).
Statement of the Problem
Federal University Lokoja faces a critical challenge in accurately predicting student behavior using traditional methods, which are often based on incomplete data and subjective analysis. The lack of comprehensive, real-time insights into student engagement and performance has led to ineffective interventions and resource misallocation. Traditional approaches do not account for the diverse and dynamic nature of modern student populations, leading to predictions that are often inaccurate and outdated (Chinwe, 2023). In contrast, big data analytics offers the potential for precise behavior prediction by integrating diverse data sources; however, its implementation is hindered by issues such as data fragmentation, inconsistencies in data quality, and concerns over student privacy (Ibrahim, 2024). Additionally, the complexity of analyzing unstructured data from social media and other digital platforms presents technical challenges that the current system cannot address. Without a robust, data-driven framework, the university is unable to proactively identify at-risk students or tailor interventions to individual needs. This gap results in missed opportunities for improving student retention and academic performance. This study intends to address these issues by developing a comprehensive predictive model that leverages big data analytics to forecast student behavior, thereby enabling timely and targeted support measures that enhance overall educational outcomes (Musa, 2025).
Objectives of the Study:
- To develop a predictive model for student behavior using big data analytics.
- To compare the accuracy of the big data-driven model with traditional prediction methods.
- To propose strategies for ensuring data quality and protecting student privacy in behavior prediction.
Research Questions:
- How does big data analytics improve the prediction of student behavior?
- What are the limitations of traditional behavior prediction methods?
- How can data quality and privacy concerns be effectively managed in a big data framework?
Significance of the Study
This study is significant as it explores the application of big data analytics in predicting student behavior, offering actionable insights to improve proactive support and resource allocation at Federal University Lokoja. The findings will help refine predictive models, enhance intervention strategies, and ultimately contribute to better academic outcomes and student retention (Chinwe, 2023).
Scope and Limitations of the Study:
This study is limited to the evaluation of student behavior prediction methods at Federal University Lokoja, Kogi State.
Definitions of Terms:
• Big Data Analytics: Techniques used to analyze large, complex datasets for insights (Ibrahim, 2024).
• Student Behavior Prediction: The process of forecasting student actions and performance trends (Chinwe, 2023).
• Predictive Model: A statistical tool that forecasts future outcomes based on historical data (Musa, 2025).
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