Building the '324' Smart Field: Innovations and Practices of Forestry Digital Intelligence Talent Training System
Digital transformation of forestry is advancing rapidly, in this context, it has become an urgent need for industry development to cultivate high-quality skilled talents with the new-quality productive characteristics of “understanding technology, being able to innovate, and being able to apply”.In response to the shortcomings of traditional forestry vocational education in digital teaching scenarios, teaching models and evaluation systems,this study innovatively building the “324” smart field talent training system. It reconstructs teaching scenarios through the “three-link” teaching system (integration of theory and practice, online and offline, base site),uses the “eight-step teaching method” (inquiry, lead-in, analysis, learning, discussion, training, assessment, and reflection) to innovate the teaching model, and establishes a “four-dimensional evaluation” system (multi-agent, multi-dimensional, multi-criteria, quantitative-qualitative) to improve quality evaluation. This model deeply integrates the mechanism of collaborative education among industry, academia, and research, effectively enhancing the digital teaching ability of teachers, and cultivating students' technological and practical application abilities. The implementation results show that the proportion of “double-qualified” professional teachers has increased significantly, the employment rate of students has remained at 100% for five consecutive years,and the forestry technology major has been successfully selected for the new round of high-level professional construction plan in Guangxi. The output value of related technological achievements has reached 258 million yuan. It providing a replicable educational practice paradigm for the cultivation of new quality productivity in forestry.
Authors
- Liang Bin Jiang
- Guo Jiang
- Dongsheng Jiang
- G. Zhang
Institutions
- Guangxi University of Science and Technology (CN)
Publication Details
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1145/3746469.3746540
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00