Revolutionising English language education:empowering teachers with BERT-LSTM-driven pedagogical tools

In the field of English language education, state-of-the-art NLP techniques based on deep learning models such as BERT and LSTM can be regarded as breakthroughs in terms of education. This research focuses on elucidating the analytical capabilities of BERT-LSTM in designing educator-driven tools to improve grammar checking and essay grading using Python software. The proposed approach leverages big data and advanced machine learning algorithms by incorporating millions of student exercise attempts from a dataset. The performance metrics demonstrated the effectiveness of the models: BERT achieved 94% precision, 92% recall, and 94% F1 score for detecting grammatical mistakes. Similarly, LSTM scores a 95% accuracy rate with an MSE of 0.09 when assessing essay quality. These outcomes far surpass those of the conventional rule-based and manual grading techniques frequently employed in educational environments. The outcomes demonstrate that the proposed BERT-LSTM models are highly accurate in predicting errors and possess standard error metrics for scoring. The comparative analysis provides a clear indication of the advantages of the proposed algorithm over conventional approaches in terms of speed and accuracy. From these models, technology-enhanced and effective pedagogical tools can help educators with feedback and analysis of learning and can provide a better learning interface for students. This research can largely be considered to contribute to the existing discussion regarding educational technology, the possible application of which, as described in this paper, is envisioned as revolutionising language education. The integrated BERT-LSTM framework presented in this paper has the potential to improve education by offering efficient, scalable, and insightful learning analytics to teachers and learners.