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高职思政课智慧课堂模型构建与应用

Construction and Application of Smart Classroom Model for Ideological and Political Courses in Vocational Colle ges
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摘要 智慧学习是“大智移云”时代教育的本质特征,构建智慧课堂则是实现智慧学习的基础和关键。针对现有高职思政课堂个性化不足等问题,利用大数据、人工智能等新型技术,从学习环境、知识图谱、教学结构、数据分析四个方面构建了高职思政课智慧课堂模型。此外,依托超星学习平台,以“思想道德与法治”课程为例进行了思政课智慧课堂应用,并从学习过程、学习知识、学习能力三个角度评价了智慧课堂模型的效果。研究结果表明,基于智慧课堂开展教学能更好地促进学习者的知识体系构建与能力培养,真正实现个性化教育。 Smart learning is the essential feature of education in the era of“Big Data,Artificial Intelligence,Mobile Internet and Cloud Computing”,and the construction of smart classrooms is the foundation and key to realize smart learning.Aiming at the lack of personalization in the existing ideological and political classes in vocational colleges,this study uses emerging technologies such as big data and artificial intelligence to construct a smart classroom model for ideological and political courses in vocational colleges from four aspects:learning environment,knowledge map,teaching structure and data analysis.In addition,relying on the smart learning platform of Chaoxing,the study carried out the application of smart classroom in ideological and political courses by taking the Ideological Morality and Rule of Law course as an example.Meanwhile,the application effectiveness of the smart classroom model was evaluated from three perspectives of learning process,learning knowledge and learning ability.The research results indicate that teaching based on smart classroom can better promote the construction of learners'knowledge system and ability training,and truly realize personalized education.
作者 陈彩华 龙卫兵 龙心远 CHEN Caihua;LONG Weibing;LONG Xinyuan(Hunan Open University,Changsha 410004,China;Hunan Vocational College of Engineering,Changsha 410151,China;Yongcheng Education Co.,Ltd.,Changsha 410150,China)
出处 《工业技术与职业教育》 2023年第2期93-97,共5页 Industrial Technology and Vocational Education
基金 湖南省社会科学成果评审委员会课题“‘教育信息化2.0’视域下基于教育大数据的高职思政智慧课堂构建研究”(课题编号:XSP21YBC057),主持人龙卫兵。
关键词 思政课 智慧课堂 模型构建 知识图谱 个性化学习 ideological and political courses smart classroom model const ruction knowledge mapping personalized learning
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