Research on the Intelligent Evaluation System of Nursing Teaching Quality in Universities Driven by Multimodal Data
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Abstract
Against the background of educational digital transformation and the rapid expansion of smart teaching, traditional evaluation of nursing teaching quality in universities is limited by single data sources, subjective dependence, delayed feedback, and insufficient connection with clinical practice. Nursing teaching is highly practical, operational, scenariobased, and humanistic, so single indicators cannot fully reflect the teaching process, professional competence, practical skills, learning outcomes, humanistic literacy, and clinical adaptability. This study introduces multimodal data and artificial intelligence into nursing teaching evaluation and constructs an intelligent evaluation system integrating classroom teaching, skill operation, online learning, clinical internship, teacher-student interaction, and emotional feedback. A six-dimensional evaluation index system and a machine-learning-based model are designed using video, audio, text, behavior logs, numerical scores, and emotional data. Empirical testing across nursing programs shows that the system improves evaluation objectivity, reduces human scoring bias, and supports timely diagnostic feedback. The architecture is also relevant to wireless sensing, edge computing, and electromagnetic-compatible clinical simulation environments.
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