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Introduction:

In recent years, online education has become increasingly popular, with more and more students opting to take courses remotely. With the vast array of online courses available, selecting the most suitable course can be a daunting task for students. Recommender systems have emerged as a popular solution to this problem, offering personalized recommendations based on student performance data and course ratings. By leveraging these data points, these systems can help students make informed decisions about their course selection, ultimately leading to improved learning outcomes.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Recommender Systems
2.2 Types of Recommender Systems
2.3 Online Course Selection
2.4 Student Performance Data
2.5 Course Ratings
2.6 Personalization in Education
2.7 Benefits of Recommender Systems in Education
2.8 Challenges in Implementing Recommender Systems
2.9 Case Studies of Recommender Systems in Education
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Selection of Participants
3.5 Ethical Considerations
3.6 Pilot Study
3.7 Data Validation
3.8 Data Interpretation

Chapter 4: Discussion of Findings
4.1 Analysis of Student Performance Data
4.2 Evaluation of Course Ratings
4.3 Comparison of Recommender System Algorithms
4.4 Impact of Personalization on Course Selection
4.5 Recommendations for Improving Recommender Systems
4.6 Implications for Educational Institutions
4.7 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Implications for Practice
5.3 Contributions to the Field
5.4 Limitations of the Study
5.5 Recommendations for Future Research
5.6 Conclusion

Thesis Overview:

The purpose of this thesis is to explore the use of recommender systems for online course selection using student performance data and course ratings. The introduction provides an overview of the research, including the background of the study, problem statement, objectives, limitations, and scope. The literature review delves into existing research on recommender systems in education, online course selection, student performance data, and course ratings. The research methodology chapter outlines the study design, data collection methods, data analysis techniques, and ethical considerations. The discussion of findings chapter presents the analysis of student performance data, evaluation of course ratings, comparison of recommender system algorithms, and recommendations for improving recommender systems. The conclusion and summary chapter summarizes the findings, discusses implications for practice, contributions to the field, limitations, recommendations for future research, and concludes the study.

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