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Exploring the Application of the SWOT Model in the Development of a “Dual-Qualified” Teacher Workforce in Higher Vocational Education in the Context of Big Data

By: Ping Zheng 1, Qinghua Xiao 1, Mengqing Tang 2, Ying Yang 1, Chunhua Wen 3, Ziqi Liu 4
1Department of Natural Resources, Hunan Vocational College of Engineering, Changsha, Hunan, 410151, China
2Belarusian State University, Minsk, 220070, Republic of Belarus
3 Hunan Geological Survey Institute, Changsha, Hunan, 410114, China
4Guangzhou Hanshi Technology Co., Ltd, Guangzhou, Guangdong, 510799, China

Abstract

In higher vocational colleges, the development status of “dual-qualified” teachers profoundly influences the future height and breadth of higher vocational education, and has thus gradually become a top priority in the construction of the teaching staff in higher vocational colleges. This paper selects the SWOT model as a research tool, combining the SWOT model to clarify the internal strengths, internal weaknesses, and external opportunities in the training of “dual-qualified” teachers in higher vocational colleges. Within the framework of constructing a digital portrait of teachers’ teaching capabilities based on performance-based evidence, evidence is categorized into five types: text-based, audio-visual, scale-based, platform-based, and product-based. The LDA model is employed to analyze high-frequency words and extract and classify text topics, thereby generating evaluation result features and social relationship features to construct a digital portrait of teachers’ teaching capabilities. Department of Natural Resource of this Vocational College were selected as the research sample, and student evaluations of teachers and MPCK knowledge feature extraction were conducted. Based on the extracted teacher MPCK features, the construction of the “dual-qualified” teacher team in Department of Natural Resource of this Vocational College was analyzed. Overall, only the KSU (knowledge related to student understanding) feature value was below the qualified value (6.00), and it is recommended as a key direction for future development and optimization.