Design and Implementation of an Online Course Recommendation System: A Case Study of Sokoto State University, Sokoto South LGA
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Background of the Study
As higher education becomes increasingly personalized, the need for tailored course recommendations has gained prominence. Sokoto State University, Sokoto South LGA, is addressing this need by designing and implementing an online course recommendation system. Traditional course selection methods often rely on manual counseling and static course catalogs, which may not reflect the evolving interests and academic strengths of students. The proposed system leverages machine learning algorithms and data analytics to analyze students’ academic records, preferences, and performance trends. By using collaborative filtering and content-based filtering techniques, the system generates personalized course recommendations that align with individual learning paths (Musa, 2023; Chinwe, 2024). This dynamic digital platform not only enhances the academic advising process but also empowers students to make informed decisions about their educational trajectories. Integration with existing university databases ensures that the recommendations are based on up‑to‑date information, while the system’s user-friendly interface makes it accessible to a wide range of users. Moreover, the system provides real‑time feedback and adjustments, enabling continuous improvement based on user interactions. Despite these benefits, challenges such as data integration, algorithmic bias, and user privacy must be addressed to ensure the system’s reliability and effectiveness. Pilot studies in similar institutions have demonstrated the potential for significant improvements in student satisfaction and academic outcomes through personalized recommendations. This study aims to evaluate the design, implementation, and performance of the online course recommendation system at Sokoto State University, offering insights into how digital transformation can support individualized academic planning (Okafor, 2025).
Statement of the Problem
The current course selection process at Sokoto State University is largely dependent on conventional methods that do not adequately address the diverse academic interests and needs of students. Manual counseling and static course catalogs lead to suboptimal course choices and hinder the personalization of academic pathways. Although an online course recommendation system has the potential to transform the advising process by providing personalized and data‑driven suggestions, its implementation is confronted with several challenges. Technical issues such as integrating disparate data sources, ensuring algorithmic accuracy, and addressing potential biases in recommendation outputs hinder system performance. Additionally, concerns over data privacy and resistance from users who are accustomed to traditional advising methods further complicate the transition. This study seeks to assess the efficacy of an online recommendation system by comparing its outputs with traditional course selection methods. It will evaluate user satisfaction, the accuracy of recommendations, and overall system integration with existing academic infrastructures. By identifying these barriers, the study aims to propose strategies to optimize the recommendation process, improve data accuracy, and foster greater acceptance among students and faculty. The ultimate goal is to develop a robust digital advising tool that enhances academic planning and contributes to improved student outcomes (Chinwe, 2024).
Objectives of the Study
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To design and implement an online course recommendation system that utilizes machine learning.
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To evaluate the system’s accuracy, user satisfaction, and integration with existing data sources.
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To propose strategies for mitigating algorithmic bias and enhancing data privacy.
Research Questions
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How does the online recommendation system improve course selection compared to traditional methods?
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What technical challenges affect data integration and algorithmic accuracy?
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Which strategies can increase user trust and system performance?
Significance of the Study
This study is significant as it explores the application of an online course recommendation system to enhance personalized academic advising at Sokoto State University. By providing tailored course suggestions, the system aims to improve student satisfaction and academic outcomes while reducing the burden on traditional counseling services. The findings will offer critical insights for educators and IT developers in implementing data‑driven advising tools (Musa, 2023).
Scope and Limitations of the Study
This study is limited to the design and implementation of an online course recommendation system at Sokoto State University, Sokoto South LGA.
Definitions of Terms
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Course Recommendation System: A digital tool that provides personalized course suggestions.
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Machine Learning: A branch of AI that enables systems to learn from data.
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Collaborative Filtering: A technique for predicting user preferences based on collective behavior.
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