Comparative Study of Machine Learning Algorithms for Student Dropout Prediction: A Case Study of Federal University, Birnin Kebbi (Birnin Kebbi LGA, Kebbi State)
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- NGN 5000
Background of the Study
Student dropout remains a significant challenge in higher education institutions worldwide, including Nigeria. Federal University, Birnin Kebbi, located in Birnin Kebbi LGA, Kebbi State, experiences a considerable number of student dropouts, which negatively impacts the institution’s academic performance and resource utilization. Predicting students at risk of dropping out early can enable intervention strategies to support these students. Machine learning algorithms offer a promising approach for predicting student dropout based on historical data, such as academic performance, attendance, and socio-economic factors. This study will explore and compare various machine learning algorithms, such as Decision Trees, Support Vector Machines, and Neural Networks, for their effectiveness in predicting student dropout at Federal University, Birnin Kebbi.
Statement of the Problem
Federal University, Birnin Kebbi faces challenges with student retention, and the lack of a predictive system for identifying at-risk students makes it difficult to implement timely interventions. The current methods of identifying potential dropouts are manual and inefficient. Machine learning algorithms, if applied correctly, have the potential to provide early warnings, allowing the university to offer support and resources to at-risk students before they drop out. However, the effectiveness of different machine learning algorithms in predicting student dropout within this context remains unclear.
Objectives of the Study
- To explore the application of machine learning algorithms in predicting student dropout at Federal University, Birnin Kebbi.
- To compare the predictive accuracy of different machine learning algorithms for student dropout prediction.
- To recommend the most effective machine learning algorithm for predicting student dropout at Federal University, Birnin Kebbi.
Research Questions
- How effective are machine learning algorithms in predicting student dropout at Federal University, Birnin Kebbi?
- Which machine learning algorithm provides the most accurate predictions of student dropout?
- What factors contribute most significantly to student dropout prediction?
Research Hypotheses
- Machine learning algorithms can accurately predict student dropout based on historical data.
- There will be a significant difference in the accuracy of student dropout predictions between different machine learning algorithms.
- Academic performance will be the most significant factor influencing student dropout prediction.
Significance of the Study
This study will provide Federal University, Birnin Kebbi with insights into the use of machine learning algorithms for predicting student dropout. The findings will help the institution implement targeted intervention programs to improve student retention rates. This research may also contribute to the broader field of higher education retention strategies.
Scope and Limitations of the Study
The study will focus on the use of machine learning algorithms for predicting student dropout at Federal University, Birnin Kebbi, within Birnin Kebbi LGA, Kebbi State. It will not extend to other forms of student retention strategies outside machine learning-based prediction.
Definitions of Terms
Machine Learning Algorithms: Algorithms that enable computers to identify patterns in data and make predictions without being explicitly programmed.
Student Dropout: The phenomenon where students discontinue their studies before completing their academic programs.
Predictive Accuracy: The degree to which a model correctly predicts outcomes based on historical data.
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