农林院校大数据拔尖创新人才三元耦合培养模式构建研究

A Study on the Construction of a Ternary Coupling Cultivation Model for Top-Notch Innovative Big Data Talents in Agriculture and Forestry Universities

摘要: 就目前而言，农林院校大数据专业人才培养中存在的学科交叉表面化、AI赋能标签化、培养模式同质化、实践教学与产业需求脱节等问题较为普遍。本文以浙江农林大学大数据专业为研究对象，建立了交叉融合、AI赋能、一专多能三个相互耦合的要素模型。该模式以农业和AI复合型人才培养为主要目的，创建了“一个目标、三大支撑、五阶路径”的理论框架，并且附加上教学模式的革新、实践体系的重建、评价机制的改进以及组织机制的保证这四个方面来实现落地。以场景、工具、路径的闭环联动为依托，可以给农林院校大数据专业拔尖创新人才培育提供理论上的参照和可行的方案设计思路，也可为同类涉农高校新工科专业建设提供借鉴。

Abstract: At present, issues such as superficial interdisciplinary integration, tokenistic AI empowerment, homogenized cultivation models, and the disconnection between practical teaching and industry needs are widespread in the cultivation of big data majors in agriculture and forestry universities. Taking the big data major at Zhejiang A&F University as the research object, this paper establishes a three-element coupling model consisting of cross-integration, AI empowerment, and one specialty with multiple competencies. Aiming primarily at cultivating interdisciplinary talents proficient in both agriculture and AI, this model constructs a theoretical framework of “one goal, three supports, and five-stage pathways”, and ensures its implementation through four aspects: innovation in teaching models, reconstruction of the practical system, improvement of evaluation mechanisms, and organizational safeguards. Relying on the closed-loop linkage of scenarios, tools, and pathways, this study can provide theoretical references and feasible design ideas for the cultivation of top-notch innovative big data talents in agriculture and forestry universities, and can also offer a reference for the construction of new engineering majors in similar agriculture-related universities.