机器学习课程“三阶四融”思政教学模式研究

Research on the “Three-Stage Four-Integration” Ideological and Political Teaching Model for Machine Learning

摘要: 农科院校机器学习课程存在算法教学与价值引领融合缺失、实践育人与情感认同断裂、伦理教育与技术应用错位等问题。通过构建技术隐喻机制，建立算法原理与农业智慧的深度映射关系；形成涵盖家国情怀、协作精神、工匠精神、生态伦理的四维农业价值观融合体系；设计知识传授、实践体验、价值内化的“三阶四融”渐进式教学模式。从教学内容重构、实践模式创新、评价体系构建三个维度构建实施策略，形成“案例导入→原理映射→前沿拓展”的内容体系和“田间–云端”协同育人路径。实践表明，该教学体系有效解决了工科课程“价值悬浮”问题，实现了专业教育与思政教育的有机统一。

Abstract: Machine learning education in agricultural universities confronts significant challenges: algorithmic instruction lacks value-oriented guidance, practical education is disconnected from emotional engagement, and ethical considerations are misaligned with technical applications. To address these issues, this study develops technical metaphor mechanisms that establish profound connections between algorithmic principles and agricultural wisdom, creates a four-dimensional value integration framework encompassing patriotism, collaboration, craftsmanship, and ecological ethics, and implements a “three-stage four-integration” progressive pedagogical model that integrates knowledge delivery, experiential learning, and value internalization. The implementation approach encompasses three key dimensions: curriculum redesign, innovative practice models, and comprehensive assessment systems, resulting in a structured learning pathway from “case studies → principle mapping → cutting-edge applications” alongside a “field-to-cloud” collaborative educational framework. Empirical evidence demonstrates that this pedagogical approach effectively resolves the “value detachment” issue prevalent in engineering education while achieving seamless integration between technical training and ideological development.