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作 者:贾璞 宋乃庆[2] JIA Pu;SONG Naiqing(School of Mathematics and Statistics,Southwest University,Chongqing 400715;Southwest University Branch Center of Collaborative Innovation Center of Assessment toward Basic Education Quality,Southwest University,Chongqing 400715)
机构地区:[1]西南大学数学与统计学院,重庆400715 [2]西南大学中国基础教育质量监测协同创新中心西南大学分中心,重庆400715
出 处:《电化教育研究》2020年第12期28-34,58,共8页E-education Research
基 金:2018年度中国基础教育质量监测协同创新中心重大成果培育性项目“我国基础教育测评模型构建范式研究”(项目编号:2018-06-002-BZPK01);2017年度西南大学基本科研业务费专项资金项目(项目编号:XDJK2017D136)
摘 要:数据素养是信息素养的一种具体形式,逐渐成为大数据时代公民不可或缺的基本素养,培养学生的数据素养也成为新一轮中小学课程改革关注的重要课题。研究从提出背景、要素属性、方法属性和结构属性等方面回顾和分析了数据素养的理论基础与概念演进,在此基础上,结合中学生的发展需求与认知特点,探讨大数据时代下中学生需要培养的数据素养。研究认为,中学生数据素养是指中学生掌握统计方法和信息技术,在真实情境中运用数据的知识、技能和思维解决实际问题的综合能力;中学生数据素养在社会发展、人才培养和课程教学三个方面具有重要价值;基于加涅学习结果分类理论,中学生数据素养的构成维度主要包括数据知识、数据技能和数据思维三个要素维度。Data literacy is a specific form of information literacy,which has gradually become an indispensable basic literacy for citizens in the era of big data.The cultivation of students'data literacy has also become an important topic in the new round of curriculum reform in primary and secondary schools.The study reviews and analyzes the theoretical foundations and conceptual evolution of data literacy in terms of proposed background,element attributes,method attributes,and structure attributes,and discusses the data literacy that middle school students need to cultivate in the era of big data,taking into account the developmental needs and cognitive characteristics of middle school students.The study suggests that data literacy of middle school students refers to their comprehensive ability to master statistical methods and information technology,and to apply data knowledge,skills and thinking to solve practical problems in real-world contexts.Data literacy of middle school students is of great value in social development,talent training and curriculum teaching.Based on R.M.Gagné's classification of learning outcomes,the compositional dimensions of middle school students'data literacy mainly include three aspects of data knowledge,data skills,and data thinking.
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