摘要
作为信息素养的延伸和拓展,数据素养正逐渐成为智能时代学生解决问题不可或缺的一项技能,是适应人工智能和大数据技术快速发展的重要能力。在新一轮基础教育课程改革中,对于如何培养学生的数据管理、分析和应用能力也成为重点关注的话题。学生数据素养的构成研究,作为开展数据素养教育和测评的基础环节,在国内仍处于研究的起始阶段。基于对国内外14种数据素养能力模型的比较分析,可以从数据意识、数据知识和技能、数据思维和数据道德规范四个层面,构建我国学生数据素养的能力模型。而对于数据素养的培养,应更加强调数据使用的高阶思维能力和道德规范,在学校教育中应以学科融合为依托,突出教学体系的层次性和教学模式的创新性,同时开发权威科学的测评工具作为支撑,以不断促进学生数据知识、技能与思维的发展。
As an extension and expansion of information literacy,data literacy is gradually becoming an indispensable skill for students in the intelligent age to solve problems,and an important ability to adapt to the rapid development of artificial intelligence and big data technology.In the new round of basic education curriculum reform,how to cultivate students’data management,analysis and application abilities has also become a topic of focus.The research on the composition of student data literacy,as a basic link in the development of data literacy education and evaluation,is still in the initial stage of research in China.Based on the comparative analysis of 14 domestic and foreign data literacy ability models,a data literacy ability model for Chinese students is constructed from four levels:data awareness,data knowledge and skill,data thinking,and data ethics.For the cultivation of data literacy,more emphasis should be placed on the high-level thinking ability and ethics of the use of data.School education should be based on the integration of disciplines,highlight the level of teaching system and the innovation of teaching models,and develop authoritative scientific evaluation tools as a support to help students develop their data knowledge,skills and thinking.
作者
惠恭健
曾磊
Hui Gongjian;Zeng Lei(Jiangsu Research Center of“Internet+Education”,Jiangnan University,Wuxi Jiangsu 214122)
出处
《远程教育杂志》
CSSCI
北大核心
2021年第4期52-61,共10页
Journal of Distance Education
基金
江苏省社会科学基金青年项目“人工智能驱动的交互式编程学习环境设计与应用研究”(项目编号:19JYC008)的研究成果。
关键词
数据素养
能力模型
指标体系
数据素养教育
Data Literacy
Ability Model
Index System
Data Literacy Education
作者简介
惠恭健,江南大学江苏“互联网+教育”研究基地副教授,硕士生导师,研究方向:STEM教育、教育传播;曾磊,江南大学江苏“互联网+教育”研究基地在读硕士研究生,研究方向:数据素养、STEM教育、数字化学习资源与环境。
