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Introduction:

In recent years, online education has become increasingly popular, offering students flexibility and convenience in pursuing their academic goals. However, with the vast amount of online courses available, students often struggle to find the courses that best fit their needs and preferences. This is where recommender systems come into play, providing personalized recommendations based on student performance data and course content.

Recommender systems have been widely used in e-commerce and entertainment platforms to provide users with personalized recommendations based on their preferences and behaviors. By applying similar techniques to online education, we can help students find courses that match their interests, learning styles, and academic goals, ultimately improving their learning experience and outcomes.

This thesis aims to explore the use of recommender systems in online education, specifically focusing on utilizing student performance data and course content to generate personalized recommendations. By analyzing student performance data such as grades, completion rates, and engagement metrics, we can better understand students’ learning needs and preferences. Additionally, by considering course content such as topics covered, teaching styles, and difficulty levels, we can recommend courses that align with students’ academic interests and abilities.

Table of Contents:

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 Recommender Systems in Online Education
2.3 Student Performance Data in Recommender Systems
2.4 Course Content in Recommender Systems
2.5 Personalization in Education
2.6 Machine Learning Techniques for Recommender Systems
2.7 Evaluation Metrics for Recommender Systems
2.8 Challenges and Opportunities in Online Education
2.9 Ethical Considerations in Recommender Systems
2.10 Future Research Directions

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Selection
3.5 Model Selection
3.6 Evaluation Methods
3.7 Validation Techniques
3.8 Data Analysis
3.9 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Student Performance Data
4.2 Evaluation of Recommender System Models
4.3 Comparison of Different Recommendation Techniques
4.4 Implications for Online Education
4.5 Recommendations for Future Research
4.6 Practical Applications and Implementations

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

Overall, this thesis aims to contribute to the growing field of online education by exploring the potential of recommender systems to enhance the learning experience for students. By leveraging student performance data and course content, we can provide personalized recommendations that help students make informed decisions about their online course selections.

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