Comparative Study of Machine Learning Techniques for Predicting Student Dropout in Bayero University, Kano, Kano State
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- NGN 5000
Background of the Study
Student dropout is a significant issue in higher education institutions, affecting both students’ academic success and the overall performance of universities. Bayero University, Kano, faces challenges related to student retention, with many students dropping out due to factors such as academic difficulties, financial constraints, and personal issues (Ahmed & Salim, 2023). Machine learning (ML) techniques have been increasingly applied in predicting student dropout by analyzing various factors such as grades, attendance, participation, and demographic information. These techniques can identify patterns that may indicate a higher likelihood of dropout, enabling early intervention. This study aims to compare the effectiveness of different machine learning algorithms, such as decision trees, support vector machines, and neural networks, in predicting student dropout at Bayero University, Kano.
Statement of the Problem
Bayero University, Kano, lacks an effective system for predicting and addressing student dropout. Although there are various factors contributing to dropout, the university has not fully utilized machine learning techniques to predict students at risk. This study seeks to compare and evaluate the performance of different machine learning algorithms in predicting student dropout, thereby offering insights into the best approach for intervention.
Objectives of the Study
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To identify the key factors contributing to student dropout at Bayero University, Kano.
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To compare the performance of different machine learning techniques in predicting student dropout.
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To propose an optimal machine learning model for predicting student dropout at Bayero University, Kano.
Research Questions
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What are the key factors that contribute to student dropout at Bayero University, Kano?
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Which machine learning algorithm is most effective in predicting student dropout?
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How can machine learning techniques be applied to reduce student dropout rates at Bayero University, Kano?
Research Hypotheses
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Machine learning techniques can accurately predict student dropout at Bayero University, Kano, based on various factors.
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Decision trees will outperform other machine learning algorithms in predicting student dropout at Bayero University, Kano.
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Implementing a machine learning-based dropout prediction system will significantly reduce the dropout rate at Bayero University, Kano.
Significance of the Study
This study will provide Bayero University, Kano, with an efficient tool for predicting student dropout, enabling the institution to take proactive measures to retain students. The findings will also contribute to the growing body of knowledge on the use of machine learning in higher education, particularly in student retention.
Scope and Limitations of the Study
The study will focus on Bayero University, Kano, and will evaluate the use of machine learning techniques to predict student dropout. The scope is limited to the comparison of different algorithms and their effectiveness in dropout prediction. Limitations include the availability and accuracy of student data for training the models.
Definitions of Terms
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Machine Learning (ML): A type of artificial intelligence that allows systems to learn from data and make predictions without being explicitly programmed.
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Student Dropout: The act of a student discontinuing their studies before completing their degree program.
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Predictive Modeling: The process of using statistical algorithms and machine learning techniques to predict future outcomes based on historical data.
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