Development of an AI-Based Student Performance Prediction Model for Secondary Schools in Sokoto South Local Government Area, Sokoto State
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- NGN 5000
Background of the Study
Student performance prediction has become a critical area of research in education, as it enables early intervention strategies to improve academic outcomes. In many secondary schools, performance monitoring is based on periodic assessments, which often fail to identify struggling students until it is too late for effective intervention (Adebayo & Yusuf, 2023). Traditional methods of academic evaluation rely heavily on test scores, attendance records, and teacher observations, which are often subjective and may not accurately reflect a student's learning potential. There is a growing need for data-driven approaches that can provide real-time insights into student performance and predict future outcomes.
Artificial Intelligence (AI) has revolutionized various fields, including education, by enabling predictive analytics to assess student performance based on multiple factors such as attendance, participation, socioeconomic background, and previous academic records (Okonkwo & Ibrahim, 2024). AI-based models leverage machine learning algorithms to analyze patterns in student data and forecast academic success or failure with high accuracy. Such models have been implemented in developed countries, helping educators identify at-risk students early and implement targeted interventions (Olawale & Musa, 2023).
In Sokoto South Local Government Area, secondary school students face multiple challenges that impact their academic performance. Factors such as language barriers, socioeconomic conditions, teacher-student ratios, and limited access to digital learning resources contribute to variations in student success (Adebayo & Salisu, 2023). Currently, there is no AI-driven system in place to predict student performance in real time, leaving teachers and administrators to rely solely on end-of-term assessments to make academic decisions. This delay in identifying struggling students results in missed opportunities for timely intervention.
Given the increasing availability of digital student data in Nigerian schools, there is a need to develop an AI-based student performance prediction model tailored to the educational context of Sokoto South. This system will analyze various academic and behavioral indicators to generate early warnings for students at risk of poor performance. By providing educators with real-time insights, the model will support data-driven decision-making, personalized learning strategies, and targeted student support programs.
Statement of the Problem
The traditional approach to student performance evaluation in Sokoto South secondary schools is reactive rather than proactive, often identifying struggling students only after they have failed assessments. This delay in performance evaluation prevents timely intervention, contributing to increased dropout rates and declining academic achievements (Adebayo & Salisu, 2024). The absence of a data-driven system that can predict student outcomes in advance has made it difficult for teachers to provide personalized support to at-risk students.
Moreover, performance assessment methods currently used in secondary schools are limited in scope, focusing primarily on test scores without considering other critical factors such as attendance patterns, classroom participation, family background, and previous academic trends (Okonkwo & Musa, 2024). These traditional methods do not capture the full picture of student learning progress, leading to inaccurate assessments and ineffective intervention strategies.
Although AI-based predictive models have been successfully deployed in other sectors, their application in secondary education in Sokoto South remains largely unexplored. Schools lack the necessary digital infrastructure to analyze student data efficiently, and educators are not equipped with AI-driven tools that can assist in performance monitoring. Therefore, this study aims to develop an AI-based student performance prediction model that will analyze key academic and behavioral indicators to provide early intervention opportunities for struggling students.
Objectives of the Study
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To develop an AI-based student performance prediction model for secondary schools in Sokoto South Local Government Area, Sokoto State.
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To assess the impact of the AI model on early identification of at-risk students and academic intervention strategies.
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To evaluate the accuracy and effectiveness of the model in predicting student performance based on academic and behavioral indicators.
Research Questions
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What are the key academic and behavioral factors that influence student performance in secondary schools in Sokoto South?
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How can an AI-based prediction model improve early identification of struggling students and academic intervention strategies?
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What is the accuracy and effectiveness of the AI-based model in forecasting student performance outcomes?
Research Hypotheses
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The implementation of an AI-based student performance prediction model will significantly improve early identification of at-risk students.
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There is a positive relationship between AI-driven student performance prediction and improved academic intervention outcomes.
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The AI-based prediction model will provide accurate and reliable forecasts of student academic performance based on multiple data points.
Significance of the Study
This study is significant as it introduces an AI-driven approach to student performance monitoring in Sokoto South secondary schools. By leveraging machine learning techniques, the model will provide educators with real-time insights into student learning progress, enabling timely interventions to improve academic outcomes. The research will also contribute to the growing field of AI applications in education, providing valuable data on how predictive analytics can be used to enhance student success. Additionally, policymakers and school administrators can use the findings to implement data-driven strategies for improving student learning experiences.
Scope and Limitations of the Study
This study is limited to the development and implementation of an AI-based student performance prediction model for secondary schools in Sokoto South Local Government Area, Sokoto State. The research will focus on evaluating the impact of the model on student performance monitoring and academic intervention strategies. The study will collect and analyze student data from selected secondary schools within Sokoto South. It will not extend to primary schools, tertiary institutions, or other local government areas outside Sokoto South.
Definitions of Terms
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Artificial Intelligence (AI): A field of computer science that enables machines to simulate human intelligence and decision-making processes, particularly in data analysis and predictive modeling.
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Predictive Analytics: The use of statistical algorithms and machine learning techniques to analyze current and historical data to predict future outcomes.
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Academic Performance Prediction: The process of using AI-based models to assess student data and forecast their academic success or failure.
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