Development of a Data-Driven Approach for Detecting Student Absenteeism Patterns in Yola North Local Government, Adamawa State
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Background of the Study
Student absenteeism is a significant challenge in the educational sector, directly affecting academic performance and student engagement. In Yola North Local Government, Adamawa State, traditional methods of monitoring attendance are often inadequate in identifying complex absenteeism patterns. A data-driven approach that leverages big data analytics and machine learning can transform how institutions understand and address absenteeism. By integrating data from attendance registers, biometric systems, and digital classroom interactions, advanced analytical models can identify trends and predict potential issues related to absenteeism (Ibrahim, 2023). These techniques enable the detection of both chronic and sporadic absenteeism, allowing for the early identification of students at risk. Moreover, the application of predictive analytics can provide insights into the underlying causes of absenteeism, such as socio-economic factors, health issues, or disengagement with the curriculum (Chinwe, 2024). The development of interactive dashboards and visualization tools further aids administrators in monitoring attendance trends and implementing timely interventions. This data-driven method not only enhances the efficiency of attendance monitoring but also supports the development of targeted strategies to improve student retention and academic performance. However, challenges such as data integration, ensuring data accuracy, and maintaining student privacy must be addressed. This study aims to develop a comprehensive data-driven framework for detecting student absenteeism patterns in Yola North Local Government, providing actionable insights to enhance institutional response and support systems (Olufemi, 2025).
Statement of the Problem
The existing attendance monitoring systems in Yola North Local Government rely heavily on manual methods that are often inefficient and prone to errors, resulting in an incomplete picture of student absenteeism. Traditional methods fail to capture the complexity of absentee patterns, making it difficult for administrators to identify at-risk students early and implement effective interventions (Adebola, 2023). The fragmentation of attendance data across multiple sources further complicates efforts to analyze trends and pinpoint the underlying causes of absenteeism. As a result, the institution struggles to address issues of chronic absenteeism, which adversely affects academic performance and overall student retention. Without a robust, data-driven approach, the university remains reactive rather than proactive in managing absenteeism, leading to missed opportunities for timely intervention. This study seeks to address these challenges by developing a data-driven approach that consolidates attendance data and applies machine learning techniques to detect and predict absenteeism patterns. The objective is to provide educators with actionable insights that facilitate early identification and intervention, ultimately improving student engagement and academic outcomes.
Objectives of the Study:
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To develop a data-driven framework for analyzing student absenteeism patterns.
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To evaluate the predictive accuracy of machine learning models in forecasting absenteeism.
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To recommend targeted intervention strategies based on data insights.
Research Questions:
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How can data science techniques improve the detection of absenteeism patterns?
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Which machine learning models best predict future absenteeism?
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How can the insights from the data-driven approach be used to reduce absenteeism rates?
Significance of the Study
This study is significant as it employs a data-driven approach to detect and predict student absenteeism patterns, providing valuable insights for early intervention strategies in Yola North Local Government. The findings will support educators in implementing targeted measures to improve student attendance and academic performance, ultimately enhancing overall institutional effectiveness (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a data-driven absenteeism detection system in secondary schools within Yola North Local Government, Adamawa State, and does not extend to other regions or educational metrics.
Definitions of Terms:
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Data-Driven Approach: The use of analytical techniques to extract insights from data.
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Absenteeism Patterns: Trends and behaviors related to student absence.
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Predictive Analytics: Techniques for forecasting future events based on historical data.
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