Analysis of Reinforcement Learning Algorithms in University Course Recommendation Systems: A Case Study of Federal University, Kashere (Kashere LGA, Gombe State)
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- NGN 5000
Background of the Study
Course recommendation systems are becoming an essential feature of modern university systems, helping students select courses that align with their academic goals and interests. Federal University, Kashere, located in Kashere LGA, Gombe State, has a diverse student population with varying educational backgrounds, making the course selection process complex. Currently, students often face difficulties in selecting courses that match their strengths, interests, and future career aspirations.
Reinforcement learning (RL) is a type of machine learning that focuses on how agents should take actions in an environment in order to maximize a reward. In the context of course recommendation systems, RL algorithms can be used to analyze past student choices, academic performance, and preferences to suggest optimal courses. By using an RL-based approach, Federal University, Kashere can create a more personalized and adaptive course recommendation system that helps students make better-informed decisions about their course selections.
Statement of the Problem
The current course recommendation system at Federal University, Kashere is not adaptive to individual student needs and lacks personalization. Students often struggle to choose courses that will help them excel academically or align with their career goals. An RL-based course recommendation system could address these issues by offering personalized recommendations based on students' learning preferences, past academic performance, and future aspirations.
Objectives of the Study
1. To design a reinforcement learning-based course recommendation system for students at Federal University, Kashere.
2. To evaluate the effectiveness of the RL-based system in improving the course selection process.
3. To assess the impact of the RL-based recommendation system on student satisfaction and academic performance.
Research Questions
1. How can reinforcement learning algorithms be applied to develop an effective course recommendation system at Federal University, Kashere?
2. How effective is the RL-based course recommendation system in assisting students with course selection?
3. What is the impact of the RL-based recommendation system on student satisfaction and academic performance?
Research Hypotheses
1. The RL-based course recommendation system will improve the course selection process for students at Federal University, Kashere.
2. The RL-based recommendation system will increase student satisfaction with the course selection process.
3. The use of RL-based recommendations will positively impact student academic performance.
Significance of the Study
This study will provide valuable insights into how reinforcement learning can be utilized to enhance course recommendation systems in universities. By implementing such a system, Federal University, Kashere can offer more personalized and effective course suggestions, leading to improved student outcomes and greater satisfaction with the academic experience.
Scope and Limitations of the Study
The study will focus on the design and evaluation of an RL-based course recommendation system for students at Federal University, Kashere, located in Kashere LGA, Gombe State. It will be limited to undergraduate students and will not include postgraduate learners or other universities.
Definitions of Terms
• Reinforcement Learning (RL): A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards.
• Course Recommendation System: A system that suggests courses to students based on their preferences, performance, and career goals.
• Personalized Recommendations: Tailored suggestions provided to individuals based on their unique needs, preferences, and behaviors.
• Academic Performance: The measure of a student’s success in their studies, typically evaluated through grades and other academic achievements.
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