Implementation of AI-Based Smart Course Recommender Systems in Taraba State Polytechnic, Suntai, Taraba State
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- NGN 5000
Background of the Study
In modern educational settings, the increasing diversity of student interests and learning styles requires more personalized approaches to academic advising and course selection. Traditional methods of course selection in institutions like Taraba State Polytechnic, Suntai, often fail to provide tailored recommendations based on students’ past performance, learning preferences, or career goals. This can result in students struggling with unsuitable courses or underperforming due to mismatched subject choices. AI-based smart course recommender systems leverage machine learning algorithms to analyze historical data, including academic performance, preferences, and learning patterns, to suggest optimal course selections for students. By offering personalized recommendations, such systems can guide students toward courses that match their strengths and academic goals. This study explores the implementation of AI-based course recommender systems at Taraba State Polytechnic to enhance the course selection process and improve overall student success.
Statement of the Problem
At Taraba State Polytechnic, Suntai, the course registration and selection process is predominantly manual and lacks personalized guidance for students. This results in students sometimes selecting courses that are not aligned with their abilities or career aspirations. The absence of a smart, data-driven recommendation system leaves students vulnerable to making poor course choices, which can negatively affect their academic performance. The implementation of an AI-based smart course recommender system could help resolve these issues by providing individualized course recommendations. This research seeks to investigate the potential of such a system to improve the course selection process for students at the Polytechnic.
Objectives of the Study
1. To design and implement an AI-based smart course recommender system at Taraba State Polytechnic, Suntai.
2. To evaluate the effectiveness of the AI system in providing personalized course recommendations to students.
3. To assess the impact of the AI-based system on students' academic performance and course satisfaction.
Research Questions
1. How effective is the AI-based smart course recommender system in suggesting courses that align with students' strengths and academic goals?
2. What are the impacts of the AI-based system on students’ course selection decisions and academic performance?
3. How do students perceive the use of AI-based course recommendations in improving their learning experiences?
Research Hypotheses
1. The AI-based course recommender system significantly improves students' academic performance by recommending suitable courses.
2. Students who use the AI-based recommender system will show higher satisfaction with their course selections compared to those who use traditional methods.
3. The implementation of the AI-based system leads to a more efficient and streamlined course registration process.
Significance of the Study
The study aims to enhance the academic experience at Taraba State Polytechnic by providing a personalized course selection system that improves students' chances of success. By optimizing course recommendations, the system could help reduce student dropout rates and increase overall academic satisfaction, serving as a model for other polytechnics in Nigeria.
Scope and Limitations of the Study
The study will focus on the design, implementation, and evaluation of the AI-based smart course recommender system within Taraba State Polytechnic. Limitations include challenges related to data availability, system integration with the existing infrastructure, and student acceptance of AI-powered tools.
Definitions of Terms
• AI-Based Smart Course Recommender System: An AI-powered system that suggests courses to students based on their academic history, learning preferences, and career aspirations.
• Personalized Learning: An educational approach that tailors learning experiences to individual students' needs, strengths, and interests.
• Machine Learning: A subset of AI that involves algorithms that allow systems to learn from data and make predictions or decisions based on that data.
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