摘要
农业物联网是设施农业科学与工程等农学类专业的核心课程.在智慧农业蓬勃发展的时代背景下,将发展智慧农业作为思政育人题材与农业物联网教学相结合,培养出符合时代发展需求的高水平、复合型农业专业人才是时代发展的趋势.本研究旨在深度挖掘农业物联网课程中的思政育人元素,并探索其实施路径.首先探讨了物联网课程思政教学现状,分析了课程思政育入中实施智慧农业理念的难点;其次从知农爱农、科技兴农、爱国情怀、专业认同四个育人层次中挖掘出可行的思政育人元素;最后探索了在"农业物联网"课程中实现思政育人的路径,包括提升专业课教师思政素养、改进教学手段和优化学生考核机制等.本研究为智慧农业背景下的思政育人提供思路,以期培养更多高层次、新时代的农业专业人才.
Abstract
The Agricultural Internet of Things is a core course for agronomy majors,such as facility agriculture science and engi-neering.In the context of the rapid development of smart agriculture,integrating smart agriculture as a topic of ideological and political Education with the teaching of the Agricultural Internet of Things aligns with contemporary trends.This approach aims to cultivate high-level,versatile agricultural professionals who meet the evolving needs of the industry.This study seeks to deeply explore the elements of ideological and political education within the Agricultural Internet of Things course and to de-velop effective implementation strategies.Firstly,we examined the current state of ideological and political education in the In-ternet of Things course and analyzed the challenges of incorporating smart agriculture concepts into ideological and political ed-ucation.Secondly,we identified feasible elements of ideological and political education across four educational dimensions:ag-ricultural knowledge,passion for agriculture,technological advancement in agriculture,patriotism,and professional identity.Lastly,we explored the paths to implement ideological and political education in the Agricultural Internet of Things course,in-cluding enhancing the ideological and political education skills of professional teachers,improving teaching methods,and opti-mizing the student assessment mechanisms.This study provides a reference framework for integrating ideological and political education within the context of intelligent agriculture,aiming to cultivate more high-level,modern agricultural professionals.
基金项目
河南省重点研发与推广专项(科技攻关)(232102111118)
信阳农林学院高水平科研孵化器建设基金资助项目(FCL202106)
NETL
NSTL
万方数据