Design of a Machine Learning‑Based Research Topic Recommender System: A Case Study of Federal Polytechnic, Kaura Namoda, Kaura Namoda LGA
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Background of the Study
Selecting a research topic is a pivotal step for students embarking on academic research, yet many struggle to identify topics that align with current trends and their interests. At Federal Polytechnic, Kaura Namoda, Kaura Namoda LGA, a machine learning‑based research topic recommender system is proposed to assist students in making informed decisions. Traditional methods, such as manual consultation with faculty or reviewing past research, are often time‑consuming and subjective. The proposed system leverages machine learning algorithms, including collaborative filtering and natural language processing, to analyze vast datasets of academic publications, faculty expertise, and student performance records (Adeyemi, 2023; Chinwe, 2024). By processing this data, the system generates personalized research topic suggestions tailored to individual student profiles and emerging academic trends. The digital platform is designed to integrate with the institution’s academic databases, ensuring that recommendations are up-to-date and relevant. Features such as interactive dashboards, user feedback mechanisms, and continuous learning models enable the system to refine its recommendations over time. This approach not only reduces the burden on academic advisors but also empowers students to explore diverse research areas and enhances overall research productivity. However, challenges such as ensuring data quality, mitigating algorithmic bias, and securing user privacy must be addressed. Pilot studies in comparable institutions have demonstrated that machine learning‑based recommender systems can significantly improve research topic selection and student satisfaction. This study aims to evaluate the design, implementation, and operational performance of the research topic recommender system at Federal Polytechnic, Kaura Namoda, contributing to more efficient and effective academic research processes (Okafor, 2024).
Statement of the Problem
Federal Polytechnic, Kaura Namoda currently faces challenges in guiding students toward relevant and innovative research topics due to reliance on conventional methods that are subjective and time‑consuming. The absence of a systematic, data‑driven approach results in limited exposure to emerging trends and often leads to suboptimal research choices. Although a machine learning‑based research topic recommender system offers a promising solution by providing personalized recommendations, its implementation is challenged by issues such as data quality, algorithmic bias, and integration with existing academic databases. Furthermore, concerns over data privacy and the transparency of the recommendation process may hinder user trust and adoption. These challenges undermine the system’s potential to enhance research productivity and academic outcomes. This study seeks to evaluate the effectiveness of the recommender system by comparing its recommendations with traditional advisory methods, identifying technical and operational barriers, and proposing strategies to optimize performance. Addressing these issues is crucial for developing a robust digital tool that supports students in selecting research topics that are both innovative and aligned with their academic interests (Chinwe, 2024).
Objectives of the Study
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To design and implement a machine learning‑based research topic recommender system.
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To evaluate the system’s accuracy, relevance, and user satisfaction.
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To propose strategies for mitigating algorithmic bias and ensuring data integrity.
Research Questions
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How does the recommender system enhance the process of research topic selection compared to traditional methods?
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What technical challenges affect the accuracy and relevance of recommendations?
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Which measures can improve data quality and user trust in the system?
Significance of the Study
This study is significant as it seeks to empower students at Federal Polytechnic, Kaura Namoda by providing a personalized research topic recommender system that leverages machine learning to enhance academic research. By streamlining the topic selection process and providing data-driven insights, the system is expected to improve research quality and student outcomes (Adeyemi, 2023).
Scope and Limitations of the Study
This study is limited to the design and implementation of a machine learning‑based research topic recommender system at Federal Polytechnic, Kaura Namoda, Kaura Namoda LGA.
Definitions of Terms
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Recommender System: A digital tool that suggests options based on user data and preferences.
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Collaborative Filtering: A technique for making recommendations based on the preferences of similar users.
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Natural Language Processing: AI technology that enables the understanding and analysis of human language.
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