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Introduction:

Automated Essay Scoring (AES) is a technology that assesses and evaluates written essays using computer algorithms and machine learning techniques. With the advancements in artificial intelligence and natural language processing, AES has become a popular tool for educators and researchers in the field of education assessment. This thesis focuses on the application of machine learning algorithms in automated essay scoring and aims to develop a robust and accurate scoring system.

Chapter One: 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 Two: Literature Review
2.1 Overview of Automated Essay Scoring
2.2 History of Automated Essay Scoring
2.3 Machine Learning Algorithms in AES
2.4 Existing AES Systems
2.5 Benefits and Challenges of AES
2.6 Current Research Trends in AES
2.7 Applications of AES in Education
2.8 Ethical Considerations in AES
2.9 Future Directions in AES Research
2.10 Summary of Literature Review

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Extraction
3.5 Machine Learning Models
3.6 Model Training and Evaluation
3.7 Performance Metrics
3.8 Experimental Setup
3.9 Data Analysis Techniques

Chapter Four: Discussion of Findings
4.1 Overview of Findings
4.2 Analysis of Results
4.3 Comparison with Existing AES Systems
4.4 Interpretation of Results
4.5 Implications for Education
4.6 Limitations of the Study
4.7 Future Research Directions
4.8 Recommendations for Practice
4.9 Conclusion

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Research and Practice
5.4 Limitations and Future Directions
5.5 Conclusion

Thesis Overview:

Automated Essay Scoring (AES) has gained significant attention in recent years due to its potential to revolutionize the way essays are evaluated in educational settings. This thesis focuses on the application of machine learning algorithms in AES and aims to develop a robust and accurate scoring system.

In Chapter One, the introduction provides a background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter Two presents a comprehensive literature review on AES, including its history, machine learning algorithms, existing systems, benefits, challenges, research trends, applications, ethical considerations, and future directions.

Chapter Three describes the research methodology, including research design, data collection, preprocessing, feature extraction, machine learning models, model training and evaluation, performance metrics, experimental setup, and data analysis techniques. Chapter Four discusses the findings of the study, including an analysis of results, comparison with existing AES systems, interpretation of results, implications for education, limitations, future research directions, recommendations, and conclusions.

Finally, Chapter Five provides a summary of findings, contributions of the study, implications for research and practice, limitations, future directions, and concludes the thesis on Automated Essay Scoring Using Machine Learning.

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