An Investigation of the Use of Big Data in Monitoring Student Social Behavior in Federal University Gashua, Yobe State
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- NGN 5000
Background of the Study:
Understanding student social behavior has become increasingly important for universities striving to foster supportive and inclusive academic environments. At Federal University Gashua, Yobe State, the application of big data techniques to monitor student social behavior offers a novel approach to uncovering patterns in social interactions, engagement levels, and overall campus climate. By analyzing data from social media, campus forums, and institutional surveys, big data analytics can provide insights into how students interact, collaborate, and express their sentiments (Hassan, 2023). These insights are critical for developing interventions that promote positive social behavior and address issues such as bullying, isolation, and disengagement. The use of big data in this context enables continuous monitoring of social trends, allowing university administrators to respond promptly to emerging issues (Ibrahim, 2024). Moreover, integrating social behavior analytics with academic performance data offers a holistic view of the student experience, thus enhancing student support services and campus safety initiatives (Musa, 2025). Despite the potential benefits, challenges related to data privacy, ethical considerations, and the accuracy of sentiment analysis tools must be addressed to fully harness the capabilities of big data. This study aims to investigate how big data can be effectively used to monitor and analyze student social behavior at Federal University Gashua, providing recommendations for policy and practice improvements (Hassan, 2023; Ibrahim, 2024; Musa, 2025).
Statement of the Problem:
Federal University Gashua faces challenges in understanding and managing student social behavior due to the complexity and volume of data generated by various digital platforms. The current methods of monitoring social interactions are fragmented and lack the analytical depth necessary to identify subtle trends in student behavior (Hassan, 2023). Inadequate integration of data sources, inconsistent data quality, and limitations in current sentiment analysis techniques further impede efforts to obtain a comprehensive picture of campus social dynamics. Moreover, ethical concerns regarding data privacy and the potential misuse of personal information have created resistance among students and staff. These challenges limit the effectiveness of interventions aimed at fostering a supportive campus environment. The study seeks to address these issues by developing a framework for leveraging big data to monitor student social behavior, ensuring that data is collected, analyzed, and used ethically and effectively to improve campus policies and support services (Ibrahim, 2024; Musa, 2025).
Objectives of the Study:
- To explore the effectiveness of big data analytics in monitoring student social behavior at Federal University Gashua.
- To identify key patterns and trends in student interactions and sentiments.
- To propose a framework for ethical and effective monitoring of social behavior using big data.
Research Questions:
- How can big data be used to monitor student social behavior effectively?
- What are the key social behavior trends observable through data analytics?
- What ethical considerations must be addressed when monitoring student social behavior?
Significance of the Study:
This study is significant as it examines the application of big data to monitor student social behavior at Federal University Gashua, offering insights into campus dynamics that can inform targeted interventions and policy reforms. The findings will help create a more supportive and responsive academic environment (Hassan, 2023).
Scope and Limitations of the Study:
This study is limited to the investigation of big data applications for monitoring student social behavior at Federal University Gashua, Yobe State, and does not extend to other behavioral or academic analytics.
Definitions of Terms:
- Big Data Analytics: The process of analyzing large volumes of data to uncover patterns and insights (Hassan, 2023).
- Social Behavior: The interactions and communication patterns among individuals within a community (Ibrahim, 2024).
- Sentiment Analysis: The technique used to determine the emotional tone behind a body of text (Musa, 2025).
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