Development of an AI-Based Early Detection System for Academic Struggles in Secondary Schools in Makera LGA, Kaduna State
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Background of the study
Academic struggles among secondary school students are often indicative of underlying issues, such as learning disabilities, personal challenges, or insufficient academic support. Early detection of students at risk can help educators intervene before problems escalate, leading to improved student performance and retention rates. Traditional methods of identifying struggling students rely heavily on teacher observation, periodic tests, and general academic performance, which may not always capture early signs of academic difficulties. AI-based systems, on the other hand, can analyze a wide range of data, including grades, attendance, behavioral patterns, and even social factors, to predict which students are likely to face academic challenges. This study aims to develop an AI-based early detection system for academic struggles in secondary schools in Makera LGA, Kaduna State, to provide timely interventions for students at risk.
Statement of the problem
In Makera LGA, Kaduna State, secondary schools face challenges in identifying students who may be struggling academically. Early intervention is often hindered by the lack of a comprehensive and data-driven system that can flag students at risk of academic failure. The use of AI-based early detection systems could address this problem by analyzing a wide range of factors and predicting which students are likely to experience difficulties before their academic performance declines significantly. However, the potential for AI to enhance early detection systems in the region remains largely unexplored. This study will investigate the development and implementation of an AI-powered early detection system tailored to the needs of students in Makera LGA.
Objectives of the study
1. To develop an AI-based early detection system for identifying students at risk of academic struggles in secondary schools in Makera LGA.
2. To evaluate the effectiveness of the AI system in predicting academic difficulties and improving student outcomes.
3. To assess the potential of the AI-based system in supporting teachers and administrators in providing targeted interventions.
Research questions
1. How effective is the AI-based early detection system in predicting academic struggles among secondary school students in Makera LGA?
2. What factors contribute most significantly to the prediction of academic difficulties using AI-based models?
3. How can AI-based predictions inform targeted interventions to improve student performance?
Research hypotheses
1. The AI-based early detection system will accurately predict academic struggles among secondary school students in Makera LGA.
2. Academic performance, attendance, and student engagement will be the most significant predictors of academic difficulties.
3. The use of AI-based early detection will lead to improved student outcomes and timely interventions.
Significance of the study
This study will demonstrate how AI can be applied to detect academic struggles early, providing insights into effective intervention strategies. The findings could have significant implications for improving student success and retention in secondary schools in Makera LGA, Kaduna State.
Scope and limitations of the study
The study will focus on the development and evaluation of an AI-based early detection system in secondary schools within Makera LGA, Kaduna State. Limitations may include data availability, the accuracy of the AI model, and the readiness of educators to adopt new technologies.
Definitions of terms
• Early Detection System: A system designed to identify students at risk of academic difficulties before their performance significantly declines.
• AI-Based System: A system that uses artificial intelligence to analyze data and make predictions based on patterns.
• Academic Struggles: Difficulties faced by students in meeting educational requirements or mastering course content.
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