Construction and Empirical Study of Digital Literacy Evaluation Model for Young Vocational Education Teachers Based on Multimodal Data

Abstract

With the increasing deployment of intelligent teaching environments and wireless digital infrastructures, efficient multimodal information acquisition and transmission have become essential for objective assessment in vocational education. To overcome the subjectivity and limited dimensionality of conventional evaluation methods, this study proposes a digital literacy evaluation model for young vocational education teachers based on a multimodal Transformer fusion network. Semantic representations of instructional texts are extracted using BERT, spatiotemporal behavioral features from classroom videos are learned through 3D Convolutional Neural Networks, and emotional acoustic characteristics are obtained from speech signals using OpenSMILE and temporal modeling. A cross-modal attention mechanism constrained by the Technological Pedagogical Content Knowledge (TPACK) framework is introduced to integrate heterogeneous information and dynamically optimize feature weighting across instructional scenarios. An end-to-end multi-task prediction architecture subsequently generates quantitative evaluations for six digital literacy dimensions with enhanced interpretability. Experimental results demonstrate approximately 92% classification accuracy on the test set, while quantitative scores for all six secondary dimensions exceed 85.5 in both theoretical instruction and practical training environments. The proposed framework establishes a high-precision and objective assessment methodology for teacher digital literacy and provides an effective engineering solution for multimodal data fusion, intelligent educational sensing, and distributed information processing. Furthermore, its multimodal perception and communication framework offers valuable insights for electromagnetic-enabled smart education systems, wireless sensing platforms, and next-generation intelligent information transmission technologies.