Investigation into the Use of Big Data Analytics for Enhancing Secondary School Academic Performance in Gombe Local Government, Gombe State
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- NGN 5000
Background of the Study
The rapid growth of digital data in education has opened new avenues for improving academic performance through big data analytics. In secondary schools within Gombe Local Government, Gombe State, educators are increasingly recognizing the potential of leveraging large datasets to gain insights into student learning patterns, attendance, and overall academic performance. Big data analytics enables the collection, integration, and analysis of diverse data sources—ranging from examination scores to behavioral metrics—allowing for the identification of key factors that influence academic success (Abdulrahman, 2023). By applying sophisticated data mining techniques, such as clustering, association rule mining, and predictive modeling, schools can develop targeted intervention strategies that address the specific needs of underperforming students (Olu, 2024). Furthermore, big data analytics facilitates real-time monitoring of academic trends, thereby enabling educators to make timely decisions that can enhance teaching effectiveness and student engagement. The integration of data dashboards and visualization tools further aids administrators in tracking performance metrics and allocating resources efficiently. In recent years, several studies have demonstrated that the strategic use of big data in educational settings can lead to significant improvements in student outcomes, reduced dropout rates, and enhanced overall school performance (Chinwe, 2025). However, the adoption of these technologies in many secondary schools remains limited due to challenges such as inadequate infrastructure, data quality issues, and a lack of technical expertise among educators. This study aims to explore how big data analytics can be effectively implemented to enhance academic performance in secondary schools within Gombe Local Government. The research will assess the current state of data usage in these schools, identify best practices from similar contexts, and develop a framework for integrating big data analytics into school management systems.
Statement of the Problem
Despite the availability of extensive student data, secondary schools in Gombe Local Government often struggle to translate raw data into actionable insights that can improve academic performance. Traditional methods of data analysis are typically manual and time-consuming, resulting in delayed interventions and missed opportunities for timely support (Ibrahim, 2023). In addition, many schools lack the necessary infrastructure and technical expertise to implement advanced big data analytics tools, leading to underutilization of available data. This deficiency contributes to persistent issues such as low student engagement, high dropout rates, and suboptimal academic outcomes. The current system does not effectively monitor or predict student performance trends, making it difficult for educators to identify and support at-risk students. Moreover, the fragmentation of data sources and poor data quality further exacerbate these challenges, preventing the implementation of comprehensive data-driven strategies. This study seeks to address these limitations by investigating the use of big data analytics in secondary schools, aiming to develop a systematic approach to data collection, integration, and analysis. The objective is to provide educators with real-time insights that can inform targeted interventions and ultimately enhance academic performance. By adopting a data-driven approach, the study intends to create a model for sustainable improvement in educational outcomes through proactive monitoring and resource allocation (Udo, 2024).
Objectives of the Study:
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To assess the current utilization of student data in secondary schools within Gombe Local Government.
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To develop a big data analytics framework that identifies key performance indicators affecting academic success.
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To propose actionable strategies for integrating big data analytics into school management.
Research Questions:
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How can big data analytics improve the monitoring of student performance in secondary schools?
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What are the key factors influencing academic outcomes as identified by data analysis?
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What challenges exist in implementing big data solutions in these schools, and how can they be overcome?
Significance of the Study
This study is significant as it investigates the application of big data analytics to enhance academic performance in secondary schools in Gombe Local Government. The findings will provide educators and policymakers with actionable insights, enabling timely interventions and informed decision-making that can lead to improved educational outcomes and reduced dropout rates.
Scope and Limitations of the Study:
The study is limited to the use of big data analytics for improving academic performance in secondary schools in Gombe Local Government, Gombe State, and does not extend to higher education or other regions.
Definitions of Terms:
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Big Data Analytics: The process of examining large and varied data sets to uncover hidden patterns and insights.
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Academic Performance: A measure of student achievement typically reflected in grades and test scores.
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Intervention Strategies: Actions taken to improve student learning and academic outcomes based on data insights.
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