美国中小学教师数据驱动教学决策:实践框架、影响因素与推进举措  

Data-driven teaching decision-making in American primary and secondary school teachers practice framework influencing factors and promotion measures

作　　者：娄元元[1] LOU Yuan-yuan(Professional and Continuing Education College,Yunnan University,Kunming,Yunnan 650091,China)

机构地区：[1]云南大学职业与继续教育学院,云南昆明650091

出　　处：《教学研究》2021年第3期73-78,共6页Research in Teaching

基　　金：教育部人文社会科学研究青年基金项目(18YJC880062)。

摘　　要：数据驱动教学决策在美国教育领域备受关注,已经成为了改进教学的重要驱动因素。美国中小学教师收集数据的类型主要有人口统计数据、过程数据、学习评价数据、感知数据等;数据驱动教学决策的过程包括收集数据、分析数据、阐释数据、运用数据制定决策;影响数据驱动教学决策的因素主要有数据基础设施不足、教师数据观念的限制、学校数据文化的缺乏以及数据素养教育缺乏。鉴于此,美国采取了建立高质量数据管理系统,制定相关数据政策,培育学校数据文化,开展教师数据素养教育等重要举措,以促进教师有效运用数据驱动教学决策。Data-driven teaching decision-making has received much attention in the field of education in the United States,and has become an important driving factor for improving teaching.The types of data collected by primary and secondary school teachers in the United States mainly include demographic data,process data,learning evaluation data,perception data,etc.;the process of data-driven teaching decision-making includes collecting data,analyzing data,interpreting data,and using data to make decisions;affecting data-driven teaching the main factors for decision-making are insufficient data infrastructure,limitation of teachers′data concept,lack of school data culture and lack of data literacy education.In view of this,the United States has adopted important measures such as establishing a high-quality data management system,formulating relevant data policies,cultivating school data culture,and developing teacher data literacy education to promote the effective use of data to drive teaching decisions.

关 键 词：数据 教学决策 教学改进 

分 类 号：G639[文化科学—教育学]