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<p>Automated grading of essays has a considerable contribution to improving the efficiency of English education by reducing human bias and scalability in assessment. The paper presents a new hybrid scoring model using Bi LSTM, Transformer encoder and Light GBM to overcome the weaknesses inherent in conventional rule-based and machine learning methods. The paper utilizes the Deep Essays Dataset provided by Kaggle with diverse student populations with human-annotated scores. The approach entailed deep text preprocessing, shallow and semantic feature extraction and context representation fusion assisted by Bi LSTM and Transformer layers. These features are used as input to a Light GBM regressor for end score prediction. Experiments with large-scale data show that the new model outperforms all the current methods substantially on RMSE, MAE and Pearson correlation metrics. Moreover, the evaluation metrics like accuracy, precision, recall and F1-score are always more than 99%, which is a witness to the stability of the model. The graphical analysis using ROC and PR plots also serves as a witness to the robustness of the system. The findings prove the effectiveness of blending deep neural networks and gradient boosting to detect linguistic and semantic patterns in essays. This system represents a promising innovation in automated scoring technology, to lead to fair, reliable and scalable assessment in English learning.</p>

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Research on English text scoring technology based on deep learning in English teaching

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月4日

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  • Ailing Li

摘要

Automated grading of essays has a considerable contribution to improving the efficiency of English education by reducing human bias and scalability in assessment. The paper presents a new hybrid scoring model using Bi LSTM, Transformer encoder and Light GBM to overcome the weaknesses inherent in conventional rule-based and machine learning methods. The paper utilizes the Deep Essays Dataset provided by Kaggle with diverse student populations with human-annotated scores. The approach entailed deep text preprocessing, shallow and semantic feature extraction and context representation fusion assisted by Bi LSTM and Transformer layers. These features are used as input to a Light GBM regressor for end score prediction. Experiments with large-scale data show that the new model outperforms all the current methods substantially on RMSE, MAE and Pearson correlation metrics. Moreover, the evaluation metrics like accuracy, precision, recall and F1-score are always more than 99%, which is a witness to the stability of the model. The graphical analysis using ROC and PR plots also serves as a witness to the robustness of the system. The findings prove the effectiveness of blending deep neural networks and gradient boosting to detect linguistic and semantic patterns in essays. This system represents a promising innovation in automated scoring technology, to lead to fair, reliable and scalable assessment in English learning.