Optimization of AI-Based Course Recommendation Systems for University Students: A Case Study of Taraba State University (Jalingo LGA, Taraba State)
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Background of the Study
As universities offer a wide array of courses across various disciplines, students often face challenges in selecting courses that align with their academic interests, career goals, and strengths. Traditional methods of course selection, such as advising sessions with faculty, can be time-consuming and often fail to provide personalized recommendations tailored to the individual student (Olorunsola et al., 2024). AI-based course recommendation systems leverage machine learning algorithms and data analytics to provide students with personalized course suggestions based on their academic history, performance, interests, and career objectives (Adamu et al., 2025). By optimizing these recommendation systems, universities can enhance student satisfaction, improve academic performance, and streamline the course selection process.
Taraba State University, located in Jalingo LGA, Taraba State, provides an ideal case study for exploring how AI can optimize course recommendation systems. The university has a diverse student body with varying academic goals, making personalized course selection increasingly important. This study aims to develop and assess the effectiveness of an AI-based course recommendation system at the university, focusing on how the system can help students make informed decisions about their course choices.
Statement of the Problem
At Taraba State University, students often struggle with course selection due to the large number of available options and the lack of personalized guidance. While faculty advisors provide some assistance, their availability and capacity to offer personalized advice to all students are limited. As a result, students may make course choices that are not aligned with their strengths or academic goals, which can negatively impact their performance and overall academic experience. An AI-based course recommendation system has the potential to address this issue by offering tailored recommendations, but its implementation and effectiveness in a Nigerian university context remain underexplored.
Objectives of the Study
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To develop an AI-based course recommendation system for students at Taraba State University.
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To evaluate the effectiveness of the AI-based recommendation system in improving students' course selection process.
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To assess the impact of the recommendation system on student academic performance and satisfaction with their course choices.
Research Questions
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How can an AI-based course recommendation system be developed to serve the needs of students at Taraba State University?
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How effective is the AI-based course recommendation system in assisting students with their course selection?
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What is the impact of the AI-based course recommendation system on student academic performance and satisfaction?
Significance of the Study
This study will contribute to the development of AI-driven solutions to support personalized academic planning in universities. The findings could help optimize the course selection process, leading to better academic outcomes and increased student satisfaction at Taraba State University.
Scope and Limitations of the Study
The study will focus on the development and assessment of an AI-based course recommendation system for students at Taraba State University, located in Jalingo LGA, Taraba State. The study will examine the system's effectiveness in enhancing course selection and its impact on academic performance, excluding other administrative and academic processes.
Definitions of Terms
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AI-Based Course Recommendation System: A system powered by AI that provides personalized course recommendations to students based on their academic history and preferences.
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Machine Learning Algorithms: A subset of AI techniques that enable systems to learn from data and make predictions or recommendations based on that learning.
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Academic Performance: A measure of a student's success in their coursework, typically reflected in grades or other academic assessments.
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