Construction of a Knowledge Graph System for the Python Programming Fundamentals Course Oriented to Agricultural Application Scenarios
茵 刘 · Vocational Education · 2025
为解决农业院校《Python程序设计基础》课程中存在的知识点抽象孤立、学习动机不足及资源分散低效等问题，本文构建了一套深度融合农业应用场景的课程知识图谱体系。基于超星平台，围绕“Python语法主线 + 农业场景融合”双层目标，系统完成了知识图谱建设的全流程实践。通过将Python基础语法与传感器数据处理、智能灌溉决策、农产品库存管理等真实农业案例精准映射，形成结构化、可导航、可交互的知识网络。教学实证表明，该图谱显著提升了学生的学习兴趣与知识迁移能力，并大幅提高了资源查找效率，为农业信息类课程教学提供了系统化、可持续迭代的结构化支撑。To address issues such as abstract and isolated knowledge points, insufficient learning motivation, and scattered and inefficient resources in the course Python Programming Fundamentals in agricultural colleges, this paper constructs a knowledge graph system deeply integrated with agricultural application scenarios. Based on the ChaoXing platform, centering on the dual goals of “Python Syntax Main Line + Agricultural Scenario integration”, the whole process practice in knowledge graph construction is systematically completed. By accurately mapping Python basic syntax with real agricultural cases such as sensor data processing, intelligent irrigation decision-making, and agricultural product inventory management, a structured, navigable, and interactive knowledge network is formed. Teaching empirical results show that this knowledge graph significantly improves students’ learning interest and knowledge transfer ability, and greatly enhances the efficiency of resource retrieval, providing systematic and sustainably iterable structural support for the teaching of agricultural information-related courses.