Analysis of AI-Based Course Dropout Prediction Systems in Taraba State University, Jalingo, Taraba State
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- NGN 5000
Background of the Study
Course dropout is a persistent issue in higher education institutions worldwide, and Taraba State University, Jalingo, is no exception. Students drop out of courses for various reasons, such as academic difficulties, financial constraints, personal issues, or lack of motivation. Traditional methods for addressing this issue are often reactive, based on manual tracking and student self-reporting. AI-based systems, however, can predict the likelihood of course dropout by analyzing patterns from historical student data, such as attendance, performance, and engagement. By leveraging machine learning algorithms, such systems can identify students at risk of dropping out and provide early interventions. This study investigates the development and implementation of an AI-based dropout prediction system at Taraba State University, Jalingo, to help improve student retention and academic success.
Statement of the Problem
At Taraba State University, Jalingo, many students drop out of courses or fail to complete their studies due to a range of personal, academic, and financial challenges. While some of these factors can be addressed through student support services, there is a lack of proactive systems to identify at-risk students early on. The absence of predictive models hinders the ability to provide timely interventions to prevent dropouts. An AI-based dropout prediction system can fill this gap by using data-driven methods to predict and address dropout risks, offering a more targeted approach to student retention.
Objectives of the Study
1. To design and develop an AI-based course dropout prediction system for Taraba State University, Jalingo.
2. To evaluate the effectiveness of the AI-based system in predicting course dropout and identifying at-risk students.
3. To assess the potential of the AI dropout prediction system in improving student retention and reducing dropout rates.
Research Questions
1. How accurately can the AI-based system predict student course dropout based on historical data?
2. What factors are most significant in predicting student dropouts at Taraba State University?
3. How effective is the AI-based dropout prediction system in helping to reduce dropout rates and improve student retention?
Research Hypotheses
1. The AI-based dropout prediction system can accurately identify students at risk of dropping out with a higher degree of precision than traditional methods.
2. Students identified by the AI system as at risk of dropping out will benefit from early intervention programs, resulting in higher retention rates.
3. The implementation of an AI-based dropout prediction system will lead to a significant reduction in course dropout rates at Taraba State University.
Significance of the Study
This study will provide an AI-based solution to the ongoing issue of course dropout at Taraba State University, Jalingo. By predicting at-risk students early, the system will enable timely interventions that could reduce dropout rates and improve overall student retention, ultimately contributing to better academic outcomes.
Scope and Limitations of the Study
This study will focus on the development and evaluation of the AI-based dropout prediction system at Taraba State University, Jalingo. The research will be limited to the analysis of historical student data and will not explore the implementation of interventions based on the predictions. Limitations include the availability and quality of historical data and challenges in integrating the prediction system with existing university infrastructure.
Definitions of Terms
• AI-Based Dropout Prediction: A machine learning system designed to predict the likelihood of student course dropout based on historical and behavioral data.
• Student Retention: The ability of a university to retain students throughout their academic programs until graduation.
• At-Risk Students: Students who are identified as being more likely to drop out of their courses or programs based on certain risk factors.
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