An Investigation of Deep Learning Approaches for Early Detection of Student Dropout: A Case Study of Federal University, Gusau (Gusau LGA, Zamfara State)
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Background of the Study
Student dropout rates are a significant concern for higher education institutions, especially in developing countries like Nigeria. Dropping out of university not only affects the students' future career prospects but also contributes to a loss of institutional revenue and disrupts the educational system. Several factors contribute to student dropout, including academic struggles, financial constraints, mental health issues, and social challenges. Traditionally, predicting student dropout has been difficult because it involves a complex interaction of personal, academic, and socio-economic factors. However, recent advancements in deep learning and artificial intelligence (AI) offer promising solutions for early identification of students at risk of dropping out.
Deep learning approaches, a subset of machine learning, utilize artificial neural networks to analyze large datasets and make predictions with high accuracy. These techniques have been successfully applied to many prediction tasks, including medical diagnostics, financial forecasting, and student performance analysis. In the context of student dropout, deep learning can be used to identify patterns in academic performance, attendance, socio-economic background, and engagement with university resources. By predicting the likelihood of dropout early, universities can take proactive measures to intervene and support at-risk students. This study aims to investigate the use of deep learning techniques for early detection of student dropout at Federal University, Gusau, in Gusau LGA, Zamfara State.
Statement of the Problem
Student dropout is a pervasive problem at Federal University, Gusau, where a significant proportion of students leave the institution before completing their degree programs. The traditional methods of identifying at-risk students often rely on manual observation or simple statistical techniques, which are not always effective in capturing the complex factors contributing to dropout. There is a lack of an automated, data-driven system that can predict student dropout early enough to allow for targeted interventions. This research seeks to address this gap by developing and implementing a deep learning-based system capable of predicting student dropout at Federal University, Gusau.
Objectives of the Study
1. To explore the use of deep learning algorithms in predicting student dropout at Federal University, Gusau.
2. To develop a predictive model using deep learning techniques to identify at-risk students.
3. To evaluate the effectiveness of the deep learning model in improving early dropout detection compared to traditional methods.
Research Questions
1. How effective are deep learning models in predicting student dropout at Federal University, Gusau?
2. What student data features (e.g., academic performance, attendance, socio-economic background) are most predictive of dropout?
3. How does the deep learning-based approach compare to traditional methods in terms of accuracy and timeliness of dropout predictions?
Research Hypotheses
1. Deep learning models will outperform traditional methods in predicting student dropout at Federal University, Gusau.
2. The model will identify key features that are strongly correlated with student dropout.
3. The deep learning-based system will enable earlier detection of at-risk students compared to conventional methods.
Significance of the Study
This study aims to contribute to the growing body of research on using AI and deep learning in higher education. By developing a predictive model for early dropout detection, the study could significantly improve retention rates at Federal University, Gusau. Early intervention based on the model’s predictions would allow university administrators to provide timely support and resources to at-risk students, reducing dropout rates and improving overall student success.
Scope and Limitations of the Study
The study will focus on using deep learning techniques to predict student dropout at Federal University, Gusau, in Gusau LGA, Zamfara State. Data collected for this study will include students’ academic records, attendance, socio-economic status, and engagement with university services. Limitations include potential challenges in acquiring sufficient data and the accuracy of predictions in cases involving complex, unforeseen factors.
Definitions of Terms
• Deep Learning: A subset of machine learning that uses artificial neural networks with multiple layers to model complex patterns in data.
• Student Dropout: The phenomenon where a student discontinues their studies before completing their degree program.
• At-Risk Students: Students who are likely to drop out due to various academic, social, or personal factors.
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