Implementation of AI-Based Predictive Models for Student Graduation Success in Kano State Polytechnic, Kano State
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- NGN 5000
Background of the Study
In recent years, educational institutions have increasingly adopted artificial intelligence (AI) to improve student outcomes and institutional performance. One promising application of AI is the development of predictive models that can forecast student success, including graduation rates. These models leverage historical data, including academic performance, attendance, engagement, and socio-demographic factors, to predict whether students are likely to graduate or face challenges (Jha & Ray, 2024). The ability to predict graduation success enables educational institutions to intervene early, providing targeted support to at-risk students, and thereby improving overall graduation rates (Sharma et al., 2023).
At Kano State Polytechnic, Kano State, an AI-based predictive model can be developed to help identify students who may need additional academic support or interventions before it is too late. These models can provide early warning signals about potential dropouts and offer personalized recommendations to both students and faculty, aiming to reduce attrition rates and enhance the institution’s graduation success rate (Ojo & Adeyemi, 2023). By exploring the integration of AI in this area, this study seeks to design and implement a predictive model tailored to the needs of students at Kano State Polytechnic.
Statement of the Problem
Kano State Polytechnic faces challenges in identifying at-risk students early enough to intervene effectively, leading to high dropout rates. Students who struggle academically, socially, or personally often go unnoticed until it is too late for support measures to be applied. This study aims to investigate whether AI-based predictive models can be used to assess student graduation success and offer timely intervention strategies to improve graduation rates.
Objectives of the Study
- To design and implement an AI-based predictive model for student graduation success at Kano State Polytechnic.
- To evaluate the accuracy of the AI model in predicting student graduation success or failure.
- To assess the impact of predictive modeling on improving student retention and graduation rates at Kano State Polytechnic.
Research Questions
- How accurately can an AI-based predictive model forecast student graduation success at Kano State Polytechnic?
- What factors are most predictive of student graduation success at Kano State Polytechnic?
- What impact does the implementation of predictive models have on student retention and graduation rates?
Research Hypotheses
- The AI-based predictive model will significantly improve the accuracy of identifying at-risk students compared to traditional methods.
- Students identified as at-risk by the AI-based predictive model will show improved graduation rates following targeted interventions.
- Academic performance, attendance, and engagement levels will be the most significant factors influencing graduation success in the AI model.
Significance of the Study
The findings of this study will provide Kano State Polytechnic with an effective tool to predict student graduation success and intervene early to support at-risk students. This will not only improve student retention but also help enhance the overall academic performance and institutional reputation of the polytechnic. The research will also contribute to the growing body of knowledge on the use of AI in higher education.
Scope and Limitations of the Study
This study will focus on the design, implementation, and evaluation of an AI-based predictive model for student graduation success at Kano State Polytechnic. The study will be limited to undergraduate students in selected academic programs and will not include postgraduate or non-degree courses.
Definitions of Terms
AI-Based Predictive Model: A machine learning-based model that uses historical data to predict future outcomes, such as student success or failure.
Graduation Success: The successful completion of a program of study within the stipulated time frame and academic requirements.
Retention Rate: The percentage of students who continue their studies at the institution without dropping out.
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