Evaluation of Reinforcement Learning Algorithms for Adaptive Learning Systems in Universities: A Case Study of Federal University, Gashua (Bade LGA, Yobe State)
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- NGN 5000
Background of the Study
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment and receiving feedback in the form of rewards or penalties. In the context of adaptive learning systems, RL algorithms can be used to create personalized learning paths that adapt to the needs and progress of individual students. Federal University, Gashua, located in Bade LGA, Yobe State, could benefit from implementing RL-powered adaptive learning systems to enhance the educational experience for students.
Statement of the Problem
While traditional learning systems follow a fixed curriculum, adaptive learning systems powered by reinforcement learning algorithms can tailor the learning process to each student's pace and performance. However, the practical application of RL algorithms in university-level adaptive learning systems is under-researched, particularly in Nigerian universities like Federal University, Gashua.
Objectives of the Study
1. To evaluate the effectiveness of reinforcement learning algorithms in adaptive learning systems at Federal University, Gashua.
2. To assess the impact of personalized learning paths on student engagement, performance, and satisfaction.
3. To identify the challenges in implementing RL-based adaptive learning systems in a university environment.
Research Questions
1. How can reinforcement learning algorithms be used to develop adaptive learning systems at Federal University, Gashua?
2. What impact do personalized learning paths have on student engagement and academic performance?
3. What are the challenges in implementing RL-based adaptive learning systems in the university context?
Research Hypotheses
1. Reinforcement learning algorithms will lead to the development of effective and personalized learning paths for students at Federal University, Gashua.
2. Students will demonstrate higher engagement and academic performance when using adaptive learning systems powered by RL algorithms.
3. Implementation challenges will include technical difficulties, resistance to change, and infrastructure limitations.
Significance of the Study
This study will provide valuable insights into the use of reinforcement learning in adaptive learning systems at Federal University, Gashua, and contribute to the broader field of AI in education. The findings will help the university improve student outcomes and tailor learning experiences to individual needs.
Scope and Limitations of the Study
The study will focus on the application of reinforcement learning algorithms in adaptive learning systems for select courses at Federal University, Gashua. Limitations include the availability of data for training RL models and potential resistance to new learning methods by students and faculty.
Definitions of Terms
• Reinforcement Learning (RL): A type of machine learning where an agent learns to take actions by receiving rewards or penalties based on its actions.
• Adaptive Learning Systems: Educational systems that adjust the learning path according to the learner's needs, pace, and preferences.
• Personalized Learning Paths: Customized learning experiences designed to suit the individual needs and abilities of students.
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