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Introduction:

Essay scoring is a time-consuming task for educators and instructors, and the demand for automated essay scoring systems has been growing rapidly in recent years. Long Short-Term Memory (LSTM) networks have shown great potential in natural language processing tasks due to their ability to capture long-term dependencies in sequential data. In this thesis, we aim to design and implement an automated essay scoring system using LSTM to provide a more efficient and accurate alternative to human grading.

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 Introduction to automated essay scoring
2.2 Traditional methods of essay scoring
2.3 Neural networks in natural language processing
2.4 LSTM networks and their applications
2.5 Previous studies on automated essay scoring systems using LSTM
2.6 Evaluation metrics for automated essay scoring systems
2.7 Challenges and limitations in essay scoring systems
2.8 Comparison of different automated essay scoring approaches
2.9 Ethical considerations in automated essay scoring
2.10 Future directions in automated essay scoring research

Chapter 3: System Design and Methodology
3.1 System architecture
3.2 Data preprocessing techniques
3.3 Feature extraction methods
3.4 LSTM model design
3.5 Training and testing procedures
3.6 Evaluation metrics selection
3.7 Hyperparameter tuning
3.8 Model optimization techniques

Chapter 4: System Implementation
4.1 Data collection and annotation
4.2 Model development
4.3 System integration
4.4 User interface design
4.5 Performance evaluation
4.6 System testing and validation
4.7 Error analysis
4.8 Deployment and scalability considerations

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions of the study
5.3 Implications for educators and researchers
5.4 Future research directions
5.5 Conclusion

Thesis Overview on Building an automated essay scoring system using LSTM:

Automated essay scoring has become an essential tool for educators and instructors to efficiently evaluate large volumes of student writing. In this thesis, we propose a novel approach to automated essay scoring using Long Short-Term Memory (LSTM) networks. The use of LSTM networks in natural language processing tasks has shown promising results in capturing long-term dependencies in sequential data, making them ideal for assessing the coherence and quality of essays.

The thesis will begin with an introduction to the problem statement, objectives, limitations, scope, and significance of the study, followed by a comprehensive literature review on automated essay scoring, neural networks, LSTM networks, and previous studies on automated essay scoring using LSTM. The literature review will also cover evaluation metrics, challenges, limitations, and future directions in automated essay scoring research.

The system design and methodology chapter will detail the architecture of the automated essay scoring system, data preprocessing techniques, feature extraction methods, LSTM model design, training, and testing procedures, evaluation metrics selection, hyperparameter tuning, and model optimization techniques. The system implementation chapter will discuss data collection, annotation, model development, system integration, user interface design, performance evaluation, testing, validation, error analysis, deployment, and scalability considerations.

The conclusion and summary chapter will provide a summary of findings, contributions of the study, implications for educators and researchers, future research directions, and a conclusion. This thesis aims to provide a detailed overview of the design, implementation, and evaluation of an automated essay scoring system using LSTM, offering insights into the potential of LSTM networks in improving the efficiency and accuracy of essay scoring processes.

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