A deep learning and visualization framework for personalized english language teaching

This study develops and validates a collaborative framework integrating deep learning and data visualization for personalized English instruction. By integrating multi-source learning evidence, the framework improves the efficiency of error diagnosis, learning-weakness identification, and feedback. In terms of research methods, firstly, a multimodal data fusion framework combining Robustly Optimized BERT Pretraining Approach (RoBERTa) and Temporal Fusion Transformer is designed to analyze students’ learning behavior and knowledge mastery accurately. Secondly, the model’s performance is optimized and enhanced by combining comparative learning in fine-grained tasks (such as weak point location). Finally, the interactive heat map and learning progress map are developed using dynamic data visualization technology to help students and teachers track learning progress in real time and adjust teaching strategies. The results reveal that the proposed model’s classification accuracy, recall, and F1 score reach 90.0%, 90.1%, and 90.0%, respectively, exceeding traditional methods and other comparative models. At the same time, the accuracy of weak point locating reaches 86.8%, effectively improving the pertinence and accuracy of teaching. The intervention time of teachers is mitigated by 44.4%, and the tracking time of students’ learning progress is reduced by 38.3%, remarkably indicating the effectiveness of visualization tools in teaching practice. The results indicate that the proposed method can provide short-term personalized feedback support for English teaching through multimodal data fusion and visualization tools. The primary contributions of this study are reflected in error diagnosis, identification of learner weaknesses, and improvement of feedback efficiency. However, knowledge retention, transfer ability, and long-term educational outcomes still require further validation through full-semester longitudinal tracking experiments.