Application of BERT-Based Japanese Writing Intelligent Grading System in Blended Teaching

Abstract

Japanese writing instruction in foreign language education continues to face challenges such as low correction efficiency, limited error identification, and insufficient personalized feedback. This study examines the application of a BERT-based intelligent grading system within a blended teaching framework to address these issues. The research explores three key questions: (1) how BERT can be leveraged for automatic detection of grammatical, spelling, and sentence structure errors in Japanese writing; (2) how the system can be integrated into blended teaching; and (3) what measurable impact it has on student writing outcomes. We developed a BERT-based encoder–decoder model and conducted a controlled experiment involving an experimental group (n=150) using the system and a control group (n=150) relying on manual grading. The results showed that the experimental group achieved higher writing accuracy (89.3 vs. 79.5), improved logical coherence (4.4 vs. 3.7 on a 5-point rubric), and faster feedback (average 4.8 minutes vs. 26 minutes). The system also achieved a grammar error detection F1-score of 84.4%, outperforming traditional RNN and Transformer models. Despite its strengths, limitations persist in addressing discourse-level coherence and context-sensitive semantics. This study offers empirical evidence for integrating deep learning with pedagogy, providing a scalable and effective approach to enhancing writing instruction in second language education.