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Introduction:

Online education has become increasingly popular in recent years, offering students the flexibility and convenience to pursue their academic goals from anywhere in the world. However, one of the challenges faced by online institutions is the high dropout rates of students enrolled in online courses. Understanding the factors that contribute to student dropout rates in online courses is crucial for educators to design interventions that can support student success and retention.

This thesis aims to explore the predictors of student dropout rates in online courses and develop a predictive model to identify at-risk students early on. By doing so, online institutions can intervene and provide targeted support to help improve student retention rates and overall success in online courses.

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 online education
2.2 Factors influencing student dropout rates
2.3 Theoretical frameworks on student attrition
2.4 Predictive models in education
2.5 Technology in predicting student dropout rates
2.6 Previous studies on student dropout rates in online courses
2.7 Student engagement and retention strategies
2.8 Data mining techniques in predicting student attrition
2.9 Machine learning algorithms for predictive modeling
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Population and sample
3.3 Data collection methods
3.4 Variable selection and measurement
3.5 Data analysis techniques
3.6 Development of predictive model
3.7 Model evaluation and validation
3.8 Ethical considerations
3.9 Reliability and validity
3.10 Summary of research methodology

Chapter 4: Discussion of Findings
4.1 Descriptive statistics of student dropout rates
4.2 Factors influencing student attrition in online courses
4.3 Performance of predictive model
4.4 Comparison with existing models
4.5 Implications for online institutions
4.6 Recommendations for future research
4.7 Limitations of the study
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for practice
5.4 Recommendations for educators and policymakers
5.5 Future research directions
5.6 Conclusion

Thesis Overview:

Online education has revolutionized the way students access and engage with learning materials, providing unprecedented flexibility and convenience. However, the high dropout rates in online courses present a significant challenge for educators and institutions. This thesis aims to address this issue by exploring the predictors of student dropout rates in online courses and developing a predictive model to identify at-risk students early on.

Chapter 1 provides an introduction to the research topic, outlining the background, problem statement, objectives, scope, and significance of the study. Chapter 2 presents a comprehensive literature review on factors influencing student attrition, predictive models in education, technology in predicting dropout rates, and previous studies on student attrition in online courses. Chapter 3 details the research methodology, including research design, data collection methods, variable selection, data analysis techniques, and model development.

Chapter 4 discusses the findings of the study, including descriptive statistics of student dropout rates, factors influencing attrition, performance of the predictive model, implications for online institutions, and recommendations for future research. Chapter 5 presents the conclusion and summary of the thesis, summarizing the findings, discussing the contributions to the field, implications for practice, recommendations, and future research directions. Through this research, it is hoped that educators and institutions can better understand and address student dropout rates in online courses to improve student retention and success.

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