Investigation into the Role of Data Analytics in School Dropout Prediction in Secondary Schools in Sokoto South Local Government Area, Sokoto State
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Background of the Study
The issue of student dropout has been a persistent challenge in secondary education across Nigeria, with significant implications for individual futures and societal development. In Sokoto South Local Government Area, Sokoto State, the rate of school dropouts has been rising, which calls for timely and effective intervention strategies. Traditional methods of dropout prediction often rely on anecdotal evidence or simple indicators like attendance rates, but with the advancement of technology, data analytics provides an opportunity to predict and mitigate this issue more accurately.
Data analytics, through its capacity to process and analyze large datasets, can help identify patterns and trends that may indicate students at risk of dropping out (Oluwaseun & Bello, 2023). By using data from various sources, such as academic performance, attendance records, socioeconomic factors, and behavioral data, predictive models can be built to foresee potential dropouts and implement interventions before it is too late. This study will explore how data analytics can be employed to predict school dropouts and enhance retention strategies in secondary schools in Sokoto South.
Statement of the Problem
In Sokoto South Local Government Area, the increasing rate of school dropouts is a growing concern, yet there are limited strategies in place to predict and prevent this issue effectively. While traditional methods have shown some effectiveness, there is a need for a more data-driven approach to predict which students are at the greatest risk of dropping out. Data analytics offers the potential to create more reliable predictive models, but its role in dropout prediction in Sokoto South remains underexplored. This study will investigate how data analytics can be utilized to address the problem of school dropouts in secondary schools in the region.
Objectives of the Study
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To explore the role of data analytics in predicting school dropouts in secondary schools in Sokoto South.
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To identify the key factors that influence school dropout rates based on data analytics.
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To evaluate the effectiveness of data-driven predictions in improving dropout prevention efforts in Sokoto South’s secondary schools.
Research Questions
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How can data analytics be used to predict school dropouts in secondary schools in Sokoto South?
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What are the key factors influencing school dropout rates as identified through data analytics?
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How effective are data-driven interventions in preventing school dropouts in Sokoto South’s secondary schools?
Research Hypotheses
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Data analytics can significantly predict school dropouts in secondary schools in Sokoto South.
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Academic performance, attendance rates, and socioeconomic factors are significant predictors of school dropout in Sokoto South.
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Data-driven interventions based on predictive models can reduce dropout rates in secondary schools in Sokoto South.
Significance of the Study
This study will contribute to the ongoing efforts to reduce school dropouts by providing valuable insights into the application of data analytics in education. The findings will help educational policymakers and administrators in Sokoto South create targeted interventions that are grounded in data, improving retention rates and student success.
Scope and Limitations of the Study
The study will focus on secondary schools within Sokoto South Local Government Area, examining the use of data analytics to predict school dropouts. It will not cover other regions of Sokoto State or primary schools. Limitations may include data availability, privacy concerns, and the reliance on existing data sources, which may not fully capture all the factors contributing to student dropout.
Definitions of Terms
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Data Analytics: The process of examining and interpreting large datasets to uncover patterns, correlations, and trends.
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School Dropout: The cessation of enrollment in school before completing the required grade or level of education.
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Predictive Models: Algorithms and statistical techniques used to forecast future events or trends, such as student dropout.
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