Deep Learning-Driven Curriculum Optimization: Enhancing the English-Vocational Competency Matrix for Vocational Education under Digital Transformation

Vocational education systems worldwide are under increasing pressure to adapt their curricula to the demands of digital transformation, particularly in enhancing English language integration within vocational competency frameworks. However, most existing curriculum designs are static and fail to dynamically respond to evolving learner needs and industry standards. To address this challenge, this study proposes a deep learning-driven framework for optimizing the English-Vocational competency matrix using advanced machine learning models. A simulation-based dataset was generated to replicate learner performance scenarios, and three models, Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), were developed and tested using Python in Google Colab. Results showed that the Neural Network significantly outperformed other models with an accuracy of 99.0%, precision of 100%, and F1-score of 99.1%, indicating its superior capability in capturing nonlinear patterns in educational data. This approach contributes a scalable and intelligent solution to dynamic curriculum adaptation in vocational education. The study concludes by recommending the integration of such models into adaptive learning platforms for real-time curriculum adjustment. The practical implication lies in enabling data-informed policy and pedagogical interventions, although limitations such as reliance on simulated data warrant future application to real-world datasets for validation.