Leveraging machine learning for personalised learning, automated feedback, and predictive analytics in college English education
Year: 2025
Type: article
Abstract: Machine learning is changing the face of college English education with personalisation, immediate feedback, and student performance prediction. In this study, we propose an ML-driven framework. The first stage is an RNN attention model-based content recommendation, followed by a fine-tuned GPT-4 model for writing evaluation and an optimised random forest model for early risk detection. On real-world data, we achieve a 31.2% increase in recommendation accuracy, a 49% reduction in grading time with high BLEU and ROUGE scores, and 88.3% accuracy in identifying at-risk students. If data privacy, ethics, and the like are appropriately managed, ML increases student performance by 27% and grading efficiency by 40%.
Author: Heng Wang
Institution: Zhengzhou Academy of Fine Arts
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W4410826678