Construction and Practice of Teaching Process Quality Assessment Model in Higher Vocational Education Professional Accreditation System Based on Deep Learning

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Huizhong Zhang, Fanrong Meng


This paper delves into the construction and implementation of a Teaching Process Quality Assessment Model within the context of Higher Vocational Education (HVE), underpinned by the integration of deep learning methodologies. In response to the evolving demands of industries and the imperative to equip students with relevant skills, this study elucidates the multifaceted dimensions of HVE, including curriculum design, teaching strategies, and assessment practices. By advocating for a data-driven approach, this paper proposes the utilization of deep learning techniques to construct a comprehensive assessment model capable of capturing the nuances of educational interactions and learning outcomes. Through an exploration of key components and practices, this study endeavors to foster a culture of continuous improvement and innovation in teaching practices, thereby enhancing the educational experience for students. Drawing upon empirical research and theoretical frameworks, this paper contributes to the discourse surrounding educational quality assurance, pedagogical innovation, and the cultivation of a skilled workforce. Ultimately, this study underscores the transformative potential of integrating deep learning methodologies into the fabric of HVE, paving the way for a future where educational endeavors are synonymous with empowerment, innovation, and inclusive growth

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