Development of an AI-Based Academic Performance Prediction System for Postgraduate Students in Federal University, Wukari, Taraba State
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Background of the Study
In higher education, predicting academic performance is a critical aspect of academic support systems. As institutions of higher learning strive to improve educational outcomes, predictive analytics powered by Artificial Intelligence (AI) has gained prominence. AI-based systems that predict students’ academic performance can provide early interventions, helping universities support at-risk students and optimize learning strategies. Predicting the performance of postgraduate students is especially important due to the complexity and demands of postgraduate programs.
Federal University, Wukari, located in Taraba State, is home to a diverse postgraduate community. Postgraduate students often face unique challenges such as balancing academic work with professional responsibilities, adapting to more rigorous academic requirements, and dealing with personal issues that could affect their performance. Traditional methods of monitoring student progress, such as exams and assignments, may not provide timely insights into the challenges faced by students, leaving little room for proactive support. An AI-based academic performance prediction system could offer real-time insights into students’ progress, allowing for targeted interventions and personalized academic assistance.
However, despite the potential of AI in predicting academic outcomes, its application in postgraduate education, particularly at Federal University, Wukari, has not been thoroughly explored. This study aims to develop an AI-based system that predicts the academic performance of postgraduate students by analyzing data such as past academic records, attendance, participation in academic activities, and other relevant factors.
Statement of the Problem
Postgraduate students at Federal University, Wukari, face numerous academic challenges, yet the institution lacks a robust system for predicting student performance or identifying at-risk individuals early in their academic journey. Without such a system, it is difficult for academic advisors and faculty members to intervene promptly to assist students who may be struggling. Although AI offers significant potential to predict academic performance based on historical and real-time data, the application of such a system in the context of postgraduate education at Federal University, Wukari remains underexplored. This study seeks to address the gap by developing an AI-based academic performance prediction system that can forecast the performance of postgraduate students, enabling the university to offer targeted academic support.
Objectives of the Study
- To develop an AI-based academic performance prediction system for postgraduate students at Federal University, Wukari.
- To analyze the factors that contribute to academic performance prediction in postgraduate students.
- To evaluate the effectiveness of the developed AI-based system in improving academic support and intervention strategies for postgraduate students.
Research Questions
- What factors influence the academic performance of postgraduate students at Federal University, Wukari?
- How effective is an AI-based academic performance prediction system in forecasting the academic success of postgraduate students?
- How can the use of an AI-based prediction system improve academic support strategies for postgraduate students at Federal University, Wukari?
Research Hypotheses
- An AI-based academic performance prediction system significantly improves the accuracy of forecasting postgraduate students’ academic success at Federal University, Wukari.
- Academic performance of postgraduate students at Federal University, Wukari, is influenced by a combination of factors, including previous academic records, attendance, and engagement in academic activities.
- The implementation of an AI-based performance prediction system enhances academic support and intervention strategies for postgraduate students at Federal University, Wukari.
Significance of the Study
The research will provide the Federal University, Wukari, with an AI-based system that can forecast postgraduate students’ academic performance, enabling early identification of students who may require additional academic support. This approach is expected to improve academic outcomes, increase student retention, and optimize the academic advisory process. The findings of this study could also be adapted to other universities looking to implement AI-based prediction systems to enhance postgraduate education.
Scope and Limitations of the Study
The study will focus on postgraduate students at Federal University, Wukari, Taraba State. The development of the AI-based academic performance prediction system will be based on data collected from the university’s postgraduate programs. The scope is limited to the identification of performance predictors and the creation of a predictive model within this specific institution. The research is limited to the data available from the university’s records and will not consider external factors beyond the scope of the university environment.
Definitions of Terms
AI-Based Academic Performance Prediction System: A system that uses AI algorithms to forecast the academic performance of students based on various data points, including past academic records, participation, and other relevant factors.
Postgraduate Students: Students enrolled in advanced degree programs, including Master’s and PhD programs, typically involving specialized academic work.
Federal University, Wukari: A higher education institution located in Wukari, Taraba State, Nigeria.
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