Development of an AI-Based Academic Performance Prediction System for Postgraduate Students in Federal University, Wukari, Taraba State
- Project Research
- 1-5 Chapters
- Abstract : Available
- Table of Content: Available
- Reference Style:
- Recommended for :
- NGN 5000
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.
Related Project Materials
OFFICE TECHNOLOGY AND MANAGEMENT GRADUATES AND HUMAN RELATIONS SKILLS REQUIRED OF THEM IN BUSINESS ORGANIZATION
EXECERPT FROM THE STUDY
There are 23 Personal Soft Skills indicator, namely: Personal Effectiveness, Flexibility, Manage...
The Effect of Oncology Nurses’ Workload on the Accuracy of Chemotherapy Administration at University of Ilorin Teaching Hospital, Kwara State
Background of the Study
Chemotherapy is a cornerstone of cancer treatment, offering patients the potential for remission or prolonged sur...
An investigation of the role of indigenous women in economic development in Ikeduru Local Government, Imo State
Background of the Study:
Indigenous women in Ikeduru Local Government have traditionally been pivotal in sustaining family...
An Investigation into the Use of Chemical Preservatives in Local Markets in Borno State
Background of the Study
Chemical preservatives are commonly used in food processing to extend shelf life...
An appraisal of youth participation in political elections in Gwarzo Local Government Area, Kano State.
Background of the Study
Youth participation in political processes is crucial for ensuring the long-term sustainability of...
The impact of rural-urban disparities on early childhood education access in Kiyawa Local Government Area, Jigawa State
Background of the Study
Rural-urban disparities continue to present significant challenges in ensuring eq...
An assessment of problem-based learning in STEM classrooms in Shanga LGA, Kebbi State
Background of the Study
Problem-based learning (PBL) is an instructional approach that emphasizes the active explorat...
An Examination of Electoral Monitoring by Civil Society Organizations: A Study of Potiskum Local Government, Yobe State
Chapter One: Introduction
1.1 Background of the Study
Electoral monitoring by civil society orga...
Implementation of a Data Science-Based University Hostel Management System in Abubakar Tafawa Balewa University, Bauchi State
Background of the Study
Efficient management of student accommodation is vital for enhancing the overall university experi...
THE ROLE OF VOCATIONAL TRAINING IN BRIDGING SKILL GAP IN THE BUILDING INDUSTRY OF NIGERIA
ABSTRACT
Several studies on building craft skills have identified a number of problems including ineffective training as...
