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Introduction:

Reinforcement Learning (RL) is a popular machine learning technique that has been used in a variety of fields, including game playing, robotics, and recommendation systems. In recent years, RL has also been applied to the field of Intelligent Tutoring Systems (ITS) to personalize and optimize the learning experience for students.

Objective of Study:

This thesis aims to explore how RL can be utilized in ITS to create more effective and adaptive tutoring systems. The study will investigate the potential benefits of using RL in ITS, compare different RL algorithms for tutoring systems, and propose practical implementations for real-world applications.

Limitation of Study:

Due to the wide scope of both RL and ITS, this study will focus on a specific subset of RL algorithms and their applications in tutoring systems. The study will not cover other machine learning techniques or ITS components that are not directly related to RL.

Scope of Study:

The scope of this study will include a review of relevant literature on RL and ITS, a comparison of different RL algorithms for tutoring systems, a detailed explanation of the research methodology used, a discussion of findings from practical implementations, and a conclusion summarizing the key insights and implications for future research.

Masters Thesis Table of Contents:

Chapter 1: Introduction
1.1 Background
1.2 Problem Statement
1.3 Objective of Study
1.4 Limitation of Study
1.5 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Reinforcement Learning
2.2 Application of Reinforcement Learning in Intelligent Tutoring Systems
2.3 Comparison of RL Algorithms for Tutoring Systems

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Experimental Design
3.3 Implementation of RL Algorithms in ITS

Chapter 4: Discussion of Findings
4.1 Analysis of Results
4.2 Practical Implications
4.3 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Recap of Key Findings
5.2 Contributions to the Field
5.3 Limitations and Recommendations for Future Research

Thesis Overview:

Reinforcement Learning (RL) has emerged as a promising approach for improving Intelligent Tutoring Systems (ITS) by personalizing the learning experience for students. This thesis aims to explore the potential benefits of utilizing RL in ITS, comparing different RL algorithms for tutoring systems, and proposing practical implementations for real-world applications. The study will include a literature review of RL and ITS, a comparison of RL algorithms, a detailed explanation of the research methodology, a discussion of findings from practical implementations, and a conclusion summarizing key insights and implications for future research. Overall, this thesis seeks to advance the understanding of how RL can enhance ITS and contribute to the development of more effective and adaptive tutoring systems.

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