Enhanced Methodology for Production-Education Integration and Quality Evaluation of Rural Vocational Education Under Rural Revitalization with 2-Tuple Linguistic Neutrosophic Numbers

Rural vocational education (RVE) plays a crucial role in nurturing practical talents for the development of rural economy and society in the new era, as well as cultivating future generations of agricultural successors. Quality evaluation serves as an essential means of ensuring educational excellence. It acts as a key element for supervision, assurance, and enhancement of educational quality. For the production-education integration (PEI) in RVE, establishing a quality evaluation system that aligns with national and rural conditions, caters to the needs of modern agricultural industry development, and reflects the characteristics of RVE is crucial. Such a system plays a vital role in leading and promoting the deep integration of industry and education in rural vocational colleges. The PEI quality evaluation in RVE under rural revitalization involves MAGDM. Currently, Exponential TODIM (ExpTODIM) approach and grey relational analysis (GRA) approach has been utilized to address MAGDM challenges. To handle uncertain information in PEI quality evaluation in RVE under rural revitalization, 2-tuple linguistic neutrosophic sets (2TLNSs) are conducted as a valuable tool. This paper introduces the implementation of the 2-tuple linguistic neutrosophic number Exponential TODIM-GRA (2TLNN-ExpTODIM-GRA) approach to effectively manage MAGDM problems using 2TLNSs. Additionally, a numerical study is conducted to validate the application of this approach for PEI quality evaluation in RVE under rural revitalization.

@article{Wang2024,
  title     = {Enhanced Methodology for Production-Education Integration and Quality Evaluation of Rural Vocational Education Under Rural Revitalization with 2-Tuple Linguistic Neutrosophic Numbers},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Xingli Wang},
  doi       = {10.14569/IJACSA.2024.0151039},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151039}
}