Analysis of AI-Based Early Warning Systems for Identifying At-Risk University Students: A Case Study of Ahmadu Bello University, Zaria (Zaria LGA, Kaduna State)
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- 1-5 Chapters
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- Table of Content: Available
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- NGN 5000
Background of the Study
Early identification of at-risk students is crucial for universities aiming to improve student retention and academic performance. Factors such as low academic performance, social isolation, and mental health issues can contribute to a student being at risk of dropping out (Olayinka & Afolabi, 2023). Traditionally, identifying at-risk students has relied on manual assessments and intervention from faculty or counselors, which can be subjective and often reactive. AI-based early warning systems (EWS) offer a more proactive solution by analyzing large datasets, including academic performance, attendance, and engagement metrics, to identify patterns that indicate a student may be at risk (Akinyemi & Olatunji, 2024). These systems can provide real-time alerts, allowing university administrators to intervene early and offer the necessary support to at-risk students.
At Ahmadu Bello University, Zaria, located in Zaria LGA, Kaduna State, student retention and academic success are ongoing concerns, especially given the large and diverse student population. This study aims to analyze the implementation of an AI-based early warning system to identify at-risk students and provide data-driven insights for intervention.
Statement of the Problem
Ahmadu Bello University, Zaria, faces challenges in identifying and supporting at-risk students due to the sheer volume of students and the complexity of factors contributing to academic challenges. Traditional methods of identifying at-risk students are often slow and rely on subjective judgments from faculty and counselors. AI-based early warning systems offer the potential to automate and improve this process, but their effectiveness and feasibility in the university context have not been fully explored.
Objectives of the Study
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To develop and implement an AI-based early warning system for identifying at-risk students at Ahmadu Bello University, Zaria.
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To assess the effectiveness of the AI-based early warning system in identifying at-risk students.
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To evaluate the impact of early intervention on the academic success and retention of at-risk students.
Research Questions
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How effective is the AI-based early warning system in identifying at-risk students at Ahmadu Bello University, Zaria?
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What impact does early intervention, based on AI-driven alerts, have on student retention and academic performance?
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How can the AI-based system be further optimized to improve the identification of at-risk students?
Significance of the Study
This study will provide valuable insights into the use of AI-based early warning systems in universities. The findings could assist Ahmadu Bello University, Zaria, and other institutions in proactively identifying and supporting at-risk students, ultimately improving retention rates and academic outcomes.
Scope and Limitations of the Study
The study will focus on the development and evaluation of an AI-based early warning system for identifying at-risk students at Ahmadu Bello University, Zaria, located in Zaria LGA, Kaduna State. The research will not address broader institutional policies or factors outside of academic performance, attendance, and student engagement.
Definitions of Terms
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Early Warning System (EWS): A system that uses data and analytics to predict students who may be at risk of academic failure or dropout.
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At-Risk Students: Students who are identified as being at a high risk of academic failure or dropout due to various factors, including low grades, poor attendance, or social challenges.
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Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
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