Development of a Machine Learning Model for Automated Student Profiling in University of Abuja, FCT
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Background of the Study
Student profiling is a vital aspect of personalized education, as it enables educational institutions to understand the unique characteristics and learning behaviors of each student. This knowledge can be used to tailor academic support, career counseling, and other student services. Machine learning (ML) offers a powerful approach to automating the profiling process by analyzing data such as academic performance, attendance, engagement, and socio-economic factors to create dynamic student profiles (Chukwu & Ahmed, 2023). The University of Abuja, located in the Federal Capital Territory (FCT), faces challenges in managing large volumes of student data manually. An automated ML-based student profiling system would help the university more effectively monitor student progress and identify those in need of academic support, thereby improving overall student success. This study will focus on developing a machine learning model for automating the student profiling process at the University of Abuja.
Statement of the Problem
The University of Abuja lacks an automated system to generate comprehensive student profiles, which hinders the institution’s ability to provide personalized academic support and services. Current methods for profiling students rely on manual data collection and analysis, which are time-consuming and prone to errors. The study aims to address these issues by developing a machine learning model that automates the profiling process and offers real-time insights into student progress and needs.
Objectives of the Study
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To identify the key factors that contribute to student profiles at the University of Abuja.
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To design and develop a machine learning model for automating student profiling.
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To evaluate the effectiveness of the ML-based system in improving student support and monitoring at the University of Abuja.
Research Questions
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What are the key factors that should be included in student profiling at the University of Abuja?
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How can a machine learning model be designed to automate student profiling?
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What impact will an ML-based student profiling system have on student support and academic performance?
Research Hypotheses
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A machine learning model will significantly improve the accuracy of student profiles compared to manual profiling methods.
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Implementing an ML-based student profiling system will enhance the university’s ability to provide timely academic support to students.
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Students who are profiled using an ML system will show improved academic outcomes due to more personalized support.
Significance of the Study
This study will help the University of Abuja automate and optimize the student profiling process, leading to more efficient monitoring and support systems. The findings could also serve as a model for other universities looking to integrate machine learning into their student management systems.
Scope and Limitations of the Study
The study will focus on the University of Abuja and will involve the development of a machine learning model for student profiling. Limitations include the availability and quality of student data for training the model, as well as the challenges of implementing ML systems in a university environment.
Definitions of Terms
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Machine Learning (ML): A branch of artificial intelligence that involves using algorithms to analyze data and make predictions or decisions without explicit programming.
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Student Profiling: The process of collecting and analyzing student data to create a comprehensive profile that can be used for academic monitoring and support.
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Automated System: A system that uses technology to perform tasks without human intervention, in this case, automating the student profiling process.
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