Investigation into AI-Based Predictive Models for Student Dropout in Gombe State University, Gombe State
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- NGN 5000
Background of the Study
Student dropout is a critical challenge faced by universities globally, and Gombe State University is no exception. The inability to retain students not only affects the institution’s academic standing but also has broader societal implications, including a decrease in the skilled workforce and a loss of potential academic contributions. While the factors leading to student dropout are multifaceted, recent advancements in Artificial Intelligence (AI) offer promising solutions for early detection and intervention. AI-based predictive models, specifically those utilizing machine learning algorithms, have the potential to identify students at risk of dropping out based on academic performance, financial challenges, social issues, and behavioral patterns.
AI-powered predictive systems analyze large datasets and utilize various variables such as grades, attendance, financial status, and personal circumstances to forecast a student’s likelihood of dropout. These models have been used in other universities to minimize dropout rates by providing timely interventions. However, Gombe State University lacks a tailored AI-based system, leaving it vulnerable to higher dropout rates. This study aims to investigate the effectiveness of AI-based predictive models in identifying students at risk of dropout, thereby contributing to strategies that can reduce this issue.
Statement of the Problem
Gombe State University has been grappling with an increasing dropout rate over the past few years. Various factors, such as academic struggles, financial constraints, and personal issues, contribute to the challenges that students face. However, the university currently lacks an effective system to monitor students and identify early warning signs of potential dropout. As a result, interventions are often too late to prevent student attrition, leading to unnecessary academic failures and increased institutional costs. This study aims to fill this gap by exploring how AI-based predictive models can help mitigate the issue of student dropout by providing early warnings and supporting timely interventions.
Objectives of the Study
1. To investigate the potential of AI-based predictive models in identifying students at risk of dropping out at Gombe State University.
2. To evaluate the accuracy of machine learning algorithms in predicting student dropout based on academic and non-academic factors.
3. To recommend strategies for utilizing AI-powered predictive systems to reduce dropout rates at Gombe State University.
Research Questions
1. What are the key factors contributing to student dropout at Gombe State University?
2. How effective are AI-based predictive models in identifying students at risk of dropout?
3. What interventions can be implemented to mitigate dropout rates based on AI predictions?
Research Hypotheses
1. AI-based predictive models can accurately predict student dropout based on academic performance and socio-economic factors.
2. There is a significant relationship between AI-generated dropout predictions and actual student attrition rates.
3. Interventions based on AI predictions significantly reduce the likelihood of student dropout.
Significance of the Study
The study will offer Gombe State University a tailored AI-based solution for reducing student dropout rates. By identifying at-risk students early, the institution can implement preventive measures such as counseling, financial aid, or academic support. The findings will also contribute to the broader academic discourse on the effectiveness of AI in educational retention, benefiting universities that face similar challenges.
Scope and Limitations of the Study
The study will focus on Gombe State University and will collect data from students enrolled in various programs. The research will specifically explore how AI-based predictive models can be developed and implemented within the university's existing infrastructure. Limitations include the availability of accurate and comprehensive student data, potential resistance to adopting AI technologies, and financial constraints in implementing AI systems.
Definitions of Terms
• AI-Based Predictive Models: Systems that use machine learning algorithms to analyze data and predict future outcomes, such as student dropout.
• Student Dropout: The phenomenon where a student fails to complete their course of study before graduation.
• Machine Learning: A subset of AI that enables systems to learn from data and improve predictions over time.
• Early Intervention: Actions taken to support students at risk of failure or dropout before it becomes irreversible.
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