Design and Implementation of an ML-Based Early Warning System for Students at Risk of Dropping Out in Bauchi State University, Gadau, Bauchi State
- Project Research
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- Table of Content: Available
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- NGN 5000
Background of the Study
Student retention and success are critical factors in the overall performance of universities. Bauchi State University, Gadau, faces challenges related to student dropout rates, which can be attributed to a variety of factors such as academic difficulties, personal issues, and financial constraints. Early identification of students at risk of dropping out is essential for providing timely interventions to improve retention rates. Machine learning (ML) models can be used to analyze student data, such as academic performance, attendance, and engagement, to predict the likelihood of a student dropping out (Zubair & Idris, 2024). The design and implementation of an ML-based early warning system can assist in identifying at-risk students early, enabling university staff to provide the necessary support and resources to retain them.
Statement of the Problem
Bauchi State University, Gadau, lacks an effective system for identifying students at risk of dropping out. While some students may receive support, many at-risk students go unnoticed until it is too late. This study seeks to design and implement a machine learning-based early warning system to predict dropout risks and help the university take proactive measures to retain students.
Objectives of the Study
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To analyze the factors contributing to student dropout at Bauchi State University, Gadau.
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To design and implement an ML-based early warning system for identifying students at risk of dropping out.
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To evaluate the effectiveness of the ML-based system in predicting dropout risks and facilitating timely interventions.
Research Questions
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What are the key factors contributing to student dropout at Bauchi State University, Gadau?
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How can machine learning algorithms be used to accurately predict students at risk of dropping out?
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How effective is the ML-based early warning system in identifying and retaining at-risk students?
Research Hypotheses
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The implementation of an ML-based early warning system will significantly improve the accuracy of predicting students at risk of dropping out.
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Students identified by the early warning system will receive timely interventions that will reduce the dropout rate.
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The use of the ML-based system will lead to an overall improvement in student retention at Bauchi State University, Gadau.
Significance of the Study
This study will provide Bauchi State University, Gadau, with a tool for early identification of students at risk of dropping out, enabling timely and targeted interventions to improve retention rates. It will also contribute to the understanding of how machine learning can be applied to student success and retention in higher education.
Scope and Limitations of the Study
The study will focus on Bauchi State University, Gadau, and will examine the use of machine learning for dropout prediction. The scope is limited to the implementation of the early warning system and its impact on retention rates. Limitations include the availability of accurate and comprehensive data for training the machine learning model.
Definitions of Terms
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Machine Learning (ML): A type of artificial intelligence that uses statistical techniques to enable computers to learn from data and make predictions.
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Early Warning System: A system designed to detect signs of potential problems or risks (e.g., dropout) early, allowing for timely intervention.
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Student Retention: The ability of a university to retain students until they complete their degree programs.
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