Python project implementing an LSTM-based automated essay scoring system.

Python project implementing an LSTM-based automated essay scoring system.

Introduction

In recent years, there has been an increasing interest in the development of automated essay scoring systems that can accurately assess the quality of essays written by students. One popular approach to this problem is the use of Long Short-Term Memory (LSTM) networks, which are a type of recurrent neural network that is well-suited for sequence prediction tasks.

Problem Statement

While LSTM-based automated essay scoring systems have shown promise in accurately evaluating essay quality, there are still some limitations that need to be addressed. One major issue is the lack of robustness and reliability in scoring essays with diverse topics and writing styles. Additionally, existing systems may not be able to provide detailed feedback to help students improve their writing skills.

Existing System

The existing LSTM-based automated essay scoring systems typically rely on pre-trained models that have been trained on a specific dataset. These models may struggle to generalize to essays that are outside of the training data distribution, leading to inaccurate scoring results. Furthermore, the lack of explainability in these systems makes it difficult for teachers and students to understand how scores are being assigned.

Disadvantages

Some of the main disadvantages of the existing LSTM-based automated essay scoring systems include:

  • Lack of robustness in scoring essays with diverse topics and writing styles
  • Difficulty in generalizing to essays outside of the training data distribution
  • Lack of explainability in scoring results
  • Inability to provide detailed feedback to help students improve their writing skills

Proposed System

In order to address the limitations of the existing LSTM-based automated essay scoring systems, we propose a novel approach that incorporates fine-tuning techniques to adapt the pre-trained models to new essay datasets. By fine-tuning the models on a diverse range of essays, we aim to improve their generalization capabilities and robustness in scoring essays with different topics and writing styles.

Additionally, our proposed system will include a feedback mechanism that provides students with detailed suggestions on how to improve their writing skills based on the scoring results. This feedback will be generated using text generation techniques that analyze the strengths and weaknesses of the essays and provide tailored recommendations for improvement.

Conclusion

In conclusion, the development of an LSTM-based automated essay scoring system that addresses the limitations of existing systems is essential for improving the accuracy and reliability of essay evaluations. By incorporating fine-tuning techniques and a feedback mechanism, we believe that our proposed system has the potential to provide more meaningful and useful feedback to students, ultimately helping them enhance their writing skills.