Comparative Study of Neural Networks and Decision Trees in Student Dropout Prediction: A Case Study of Kaduna State University (Kaduna North LGA, Kaduna State)
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Background of the Study
Student dropout is a major issue facing many universities, including Kaduna State University in Kaduna North LGA, Kaduna State. The persistence of high dropout rates often reflects deeper issues, such as academic underperformance, financial difficulties, and lack of engagement with university activities. Predicting which students are most likely to drop out before it occurs allows for timely intervention to address the root causes of dropout and help retain students through graduation.
Neural networks and decision trees are two popular machine learning algorithms that have shown promise in predicting student dropout. Neural networks, with their ability to model complex, non-linear relationships in large datasets, can potentially provide more accurate predictions. On the other hand, decision trees offer an interpretable model that can easily identify specific factors contributing to dropout. By comparing these two approaches, this study aims to determine which algorithm is better suited for predicting student dropout at Kaduna State University.
Statement of the Problem
Kaduna State University has faced a persistent problem with student dropouts, which negatively impacts both the institution's reputation and its financial sustainability. Despite efforts to address this issue, the university lacks an effective predictive system that can identify at-risk students early on. The use of machine learning algorithms such as neural networks and decision trees for dropout prediction remains largely unexplored at the university, leaving a gap in the ability to implement proactive interventions.
Objectives of the Study
1. To compare the effectiveness of neural networks and decision trees in predicting student dropouts at Kaduna State University.
2. To evaluate the factors that contribute most to student dropout at Kaduna State University based on machine learning models.
3. To propose a machine learning-based system for predicting and preventing student dropouts at Kaduna State University.
Research Questions
1. How do neural networks and decision trees compare in predicting student dropouts at Kaduna State University?
2. What are the key factors contributing to student dropout, as identified by machine learning models at Kaduna State University?
3. How can the findings from these models be used to reduce dropout rates at Kaduna State University?
Research Hypotheses
1. Neural networks will outperform decision trees in terms of prediction accuracy for student dropouts at Kaduna State University.
2. The machine learning models will identify key factors that significantly contribute to student dropout at Kaduna State University.
3. The application of predictive models will reduce the dropout rate by enabling early interventions at Kaduna State University.
Significance of the Study
The study will provide a comparative analysis of neural networks and decision trees for predicting student dropouts, offering insights into which method is more suitable for higher education institutions like Kaduna State University. The findings will aid in the development of an effective dropout prevention strategy, ultimately improving student retention and success rates.
Scope and Limitations of the Study
The study will focus on the prediction of student dropouts at Kaduna State University, located in Kaduna North LGA, Kaduna State. The study will not address other issues related to student retention, such as mental health or financial aid policies.
Definitions of Terms
• Neural Networks: A machine learning technique modeled after the human brain that is capable of learning from large datasets and detecting patterns.
• Decision Trees: A machine learning algorithm that splits data into subsets based on the most important features, resulting in a tree-like structure for decision-making.
• Student Dropout: The act of a student discontinuing their education before completing their program or degree.
• Predictive Modeling: The use of statistical techniques and machine learning algorithms to predict future outcomes based on historical data.
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