An Empirical Study on the Improvement of Higher Vocational College Students’ Workplace English Writing Ability by Deep Learning-Driven Intelligent Marking System
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Abstract
To address low grading efficiency, delayed feedback, and insufficient personalized guidance in higher vocational workplace English writing, this study constructs a deep learning-driven intelligent marking system and verifies its teaching effect through empirical research. A total of 120 students from two higher vocational English classes were divided into an experimental group using the intelligent grading system and a control group using conventional teacher grading. During the 16-week experiment, writing tests, questionnaires, interviews, and learning records were collected and statistically analyzed. The system integrates BERT, LSTM, natural language processing, and workplace-document evaluation rules to assess grammatical accuracy, sentence diversity, content relevance, and workplace standardization. The results show that the experimental group significantly outperformed the control group in overall writing scores and all core dimensions, while writing interest, autonomous learning, and confidence also improved. The proposed system reduces teacher workload and provides a data-driven feedback pipeline suitable for smart educational environments based on networked human-machine interaction.
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