Implementation of a Predictive Analytics Model for Early Detection of Student Dropout in Secondary Schools in Maiduguri Metropolitan Council, Borno State
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- NGN 5000
Background of the Study
Student dropout remains a critical issue in secondary education, adversely affecting both individual futures and overall educational quality. In Maiduguri Metropolitan Council, Borno State, the implementation of a predictive analytics model offers a proactive approach to identify students at risk of dropping out. Predictive analytics involves the use of statistical models, machine learning algorithms, and historical data to forecast future outcomes (Abubakar, 2023). By analyzing variables such as attendance records, academic performance, socioeconomic status, and behavioral indicators, the model can detect early warning signs and facilitate timely interventions. Such a system not only supports educators in making informed decisions but also enables targeted support for students, thereby reducing dropout rates and improving overall academic success (Oluwaseun, 2024). The integration of predictive analytics into school management systems can transform how educators approach student retention, moving from reactive measures to proactive, data-driven strategies. This study aims to design and implement a predictive analytics model tailored for secondary schools in Maiduguri, evaluating its accuracy, reliability, and impact on reducing dropout rates. The research will also explore the challenges associated with data collection, model training, and ethical considerations related to student data. Ultimately, the study seeks to provide actionable recommendations for policymakers and school administrators to enhance student retention through predictive analytics.
Statement of the Problem
Secondary schools in Maiduguri face high dropout rates that negatively impact educational outcomes and future opportunities for students. Traditional methods of identifying at-risk students are often reactive and lack the precision necessary for timely intervention (Ibrahim, 2023). The absence of a data-driven approach results in missed opportunities to support vulnerable students before they disengage completely. Although predictive analytics models have been successfully implemented in various educational contexts, their adoption in Maiduguri is limited due to challenges such as inadequate data infrastructure, insufficient training for staff, and concerns regarding data privacy. These issues hinder the effective early detection of potential dropouts and the implementation of appropriate support mechanisms. This study aims to address these challenges by developing a predictive analytics model that leverages available student data to forecast dropout risks accurately. The model will analyze a range of indicators and provide early warnings, enabling educators to implement timely interventions. Additionally, the research will examine the barriers to implementing such a model in Maiduguri’s secondary schools and propose strategies to overcome them. By providing a robust, data-driven tool for early detection, the study seeks to enhance student retention rates and improve overall academic performance, ultimately contributing to a more resilient educational system (Chinwe, 2024).
Objectives of the Study:
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To develop a predictive analytics model for early detection of student dropout.
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To evaluate the model’s accuracy and effectiveness in identifying at-risk students.
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To propose strategies for integrating predictive analytics into secondary school management.
Research Questions:
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How accurately can the predictive analytics model identify students at risk of dropping out?
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What impact does early detection have on student retention rates?
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What challenges affect the implementation of predictive analytics in secondary schools, and how can they be addressed?
Significance of the Study
This study is significant as it explores the use of predictive analytics to tackle the critical issue of student dropout in Maiduguri. By providing early warning indicators, the model aims to enable timely interventions and support, thereby improving student retention and academic outcomes. The findings will offer valuable insights for educators and policymakers, promoting the adoption of data-driven strategies to enhance the overall quality of secondary education.
Scope and Limitations of the Study:
The study is limited to the implementation and evaluation of a predictive analytics model for early detection of student dropout in secondary schools within Maiduguri Metropolitan Council, Borno State, and does not extend to other educational levels or regions.
Definitions of Terms:
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Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to predict future outcomes.
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Student Dropout: The discontinuation of schooling by students before completion of the educational program.
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Early Detection: The identification of potential issues before they result in adverse outcomes.
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