不同教师群体对教育大数据的认知及影响因素——基于全国5434名教师的调查
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摘要
教师作为大数据与教育、教学、管理的交汇主体,其重要作用不言而喻。本研究通过SPSS25.0统计分析软件对全国5434名教师对教育大数据的认知和影响因素展开调查,并进行描述性分析、方差分析、皮尔逊相关分析、多元回归分析。结果显示:被调查教师群体对大数据特征的理解主要集中于"数量大、类型多、速度快",东、中、西部教师没有显著差异;不同教龄、家校距离、区域(东、中、西部)、任教学校类型的教师群体对教育大数据的积极性存在显著差异;教师工作时间、接触数据权限、教学需求、学校软件配套等外部条件的局限对教师应用教育大数据有负面影响;评价方式、教师态度、学科适用、学校投入、传播效果能正向预测教师对教育大数据的积极性。最后,文章从政策实施、理论研究、培训设计和学校执行四方面提出了建议。
The big data in education is a necessary condition for the sustainable development of educational informatization 2.0. As the intersection subject of big data and education, teaching and management, teachers play an important role. In this study, descriptive analysis, variance analysis, Pearson correlation analysis, and multiple regression analysis were carried out by SPSS to investigate the cognition and influencing factors of 5434 teachers on educational development data. The survey results show that the teachers' understanding of big data characteristics is mainly focused on "large quantity, large type and fast speed"; There is no significant regional difference in the east, middle and west regions; There are significant differences in teachers' enthusiasm for big data of education among different teaching age, distance between home and school, region(east, middle and west) and type of teaching school. The limitations of external conditions such as teachers' working hours, access to data, teaching needs, school software support have a negative impact on teachers' application of big data in education. Evaluation methods, teachers' attitudes, subject application, school input, and dissemination effects can positively predict teachers' activity for big data in education. Finally, it puts forward countermeasures and suggestions from four aspects: policy implementation, theoretical research, training design, and school implementation.
The big data in education is a necessary condition for the sustainable development of educational informatization 2.0. As the intersection subject of big data and education, teaching and management, teachers play an important role. In this study, descriptive analysis, variance analysis, Pearson correlation analysis, and multiple regression analysis were carried out by SPSS to investigate the cognition and influencing factors of 5434 teachers on educational development data. The survey results show that the teachers' understanding of big data characteristics is mainly focused on "large quantity, large type and fast speed"; There is no significant regional difference in the east, middle and west regions; There are significant differences in teachers' enthusiasm for big data of education among different teaching age, distance between home and school, region(east, middle and west) and type of teaching school. The limitations of external conditions such as teachers' working hours, access to data, teaching needs, school software support have a negative impact on teachers' application of big data in education. Evaluation methods, teachers' attitudes, subject application, school input, and dissemination effects can positively predict teachers' activity for big data in education. Finally, it puts forward countermeasures and suggestions from four aspects: policy implementation, theoretical research, training design, and school implementation.
引文
[1]高巍,刘瑞(2018).基于教育大数据的学习行为分析研究:教师评学与导学的角度[J].教师教育论坛,(7):38-42.
[2]Hattie,H.(2009).Visible learning:A synthesis of over 800 meta-analyses relating to achievement[M].Routledge,237-238,238-239,258.
[3]Iyer,K.V.(2018).Big data analytics:The stakes for students,scientists & managers-management perspective[A].Accepted for oral presentation at EeL 2018 conference,Singapore,(9):1-9.
[4]Joshua,E.(2017).Teaching future big data analysts:Curriculum and experience report[R].Parallel & Distributed Processing,(7):346-351.
[5]李芒,孔维宏,李子运(2017).问“乔布斯之问”:以什么衡量教育信息化作用[J].现代远程教育研究,(3):3-10.
[6]刘雅馨,杨现民,李新,田雪松(2018).大数据时代教师数据素养模型构建[J].电化教育研究,(2):109-116.
[7]李青,赵欢欢(2018).教师数据素养评价指标体系研究[J].电化教育研究,(10):104-110.
[8]Means,B.,Gallagher,L.,& Padilla,C.(2008).Teachers' use of student data systems to improve instruction[R].U.S.Department of Education:35.
[9]Marie,B.,Mingyu,F.,& Barbara,M.(2012).Enhancing teaching and learning through educational data mining and learning analytics:An issue brief[R].U.S.Department of Education,(10):9-51.
[10]王正青,徐辉(2018a).大数据时代美国的教育大数据战略与实施[J].教育研究,(2):120-126.
[11]维克托·迈尔-舍恩伯格,肯尼思·库克耶(2015).与大数据同行——学习和教育的未来[M].赵中建,张燕南译.上海:华东师范大学出版社.
[12]王正青,张力文(2018b).大数据时代美国发展教师数据素养的基础与路径[J].比较教育研究,(2):68-75.
[13]王学男(2018).从大数据中提升学校教育的获得感[J].教学与管理,(12):31-34.
[14]Yu,X.,& Wu,S.(2016).Typical applications of big data in education[J].Educational Innovation Through Technology,(4):103-106.
[15]杨现民.王榴卉,唐斯斯(2015).教育大数据的应用模式与政策建议[J].电化教育研究,(9):54-61.
[16]杨现民,田雪松(2016).互联网+教育蓝皮书:中国基础教育大数据[M].北京:电子工业出版社:221-256.
[17]杨现民,田雪松(2018).中国基础教育大数据2016-2017:走向数据驱动的精准教学[M].北京:科学出版社:216-221.
[18]祝智庭,孙妍妍,彭红超(2017).解读教育大数据的文化意蕴[J].电化教育研究,(1):28-36.
[19]郑燕林,李卢一(2015).对大数据支持的学习分析与评价的需求调查:基于教师的视角[J].现代远距离教育,(2):36-42.
(1)重点委托是指委托这一地区教育行政相关人员,根据具体要求,向本地区发放问卷,填写问卷的教师人数不低于200人。
(2)除宁夏回族自治区以外,其他省份均有被试填写调查问卷。
(3)多重响应用于多选题分析,分析多选题选项的选择比例情况等,主要涉及到两个概念:响应率和普及率。响应率用于对比各个选项的相对选择比例情况;普及率用于某项的选择普及情况,即整体上看多选题各选项占所有选择的比例情况。
(4)初级职称包括:未定级、小学三级、小学二级、小学一级、中学三级、中学二级;中级职称包括小教高级、中教一级;高级职称包括中教高级、特级教师。
[2]Hattie,H.(2009).Visible learning:A synthesis of over 800 meta-analyses relating to achievement[M].Routledge,237-238,238-239,258.
[3]Iyer,K.V.(2018).Big data analytics:The stakes for students,scientists & managers-management perspective[A].Accepted for oral presentation at EeL 2018 conference,Singapore,(9):1-9.
[4]Joshua,E.(2017).Teaching future big data analysts:Curriculum and experience report[R].Parallel & Distributed Processing,(7):346-351.
[5]李芒,孔维宏,李子运(2017).问“乔布斯之问”:以什么衡量教育信息化作用[J].现代远程教育研究,(3):3-10.
[6]刘雅馨,杨现民,李新,田雪松(2018).大数据时代教师数据素养模型构建[J].电化教育研究,(2):109-116.
[7]李青,赵欢欢(2018).教师数据素养评价指标体系研究[J].电化教育研究,(10):104-110.
[8]Means,B.,Gallagher,L.,& Padilla,C.(2008).Teachers' use of student data systems to improve instruction[R].U.S.Department of Education:35.
[9]Marie,B.,Mingyu,F.,& Barbara,M.(2012).Enhancing teaching and learning through educational data mining and learning analytics:An issue brief[R].U.S.Department of Education,(10):9-51.
[10]王正青,徐辉(2018a).大数据时代美国的教育大数据战略与实施[J].教育研究,(2):120-126.
[11]维克托·迈尔-舍恩伯格,肯尼思·库克耶(2015).与大数据同行——学习和教育的未来[M].赵中建,张燕南译.上海:华东师范大学出版社.
[12]王正青,张力文(2018b).大数据时代美国发展教师数据素养的基础与路径[J].比较教育研究,(2):68-75.
[13]王学男(2018).从大数据中提升学校教育的获得感[J].教学与管理,(12):31-34.
[14]Yu,X.,& Wu,S.(2016).Typical applications of big data in education[J].Educational Innovation Through Technology,(4):103-106.
[15]杨现民.王榴卉,唐斯斯(2015).教育大数据的应用模式与政策建议[J].电化教育研究,(9):54-61.
[16]杨现民,田雪松(2016).互联网+教育蓝皮书:中国基础教育大数据[M].北京:电子工业出版社:221-256.
[17]杨现民,田雪松(2018).中国基础教育大数据2016-2017:走向数据驱动的精准教学[M].北京:科学出版社:216-221.
[18]祝智庭,孙妍妍,彭红超(2017).解读教育大数据的文化意蕴[J].电化教育研究,(1):28-36.
[19]郑燕林,李卢一(2015).对大数据支持的学习分析与评价的需求调查:基于教师的视角[J].现代远距离教育,(2):36-42.
(1)重点委托是指委托这一地区教育行政相关人员,根据具体要求,向本地区发放问卷,填写问卷的教师人数不低于200人。
(2)除宁夏回族自治区以外,其他省份均有被试填写调查问卷。
(3)多重响应用于多选题分析,分析多选题选项的选择比例情况等,主要涉及到两个概念:响应率和普及率。响应率用于对比各个选项的相对选择比例情况;普及率用于某项的选择普及情况,即整体上看多选题各选项占所有选择的比例情况。
(4)初级职称包括:未定级、小学三级、小学二级、小学一级、中学三级、中学二级;中级职称包括小教高级、中教一级;高级职称包括中教高级、特级教师。