大数据知识发现的教师成长行为路径
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摘要
文章对来自福建省厦门市思明区95位教师的课堂教学行为大数据、实践性知识大数据和在线学习行为大数据,运用路径分析法、聚类分析方法、COI认知临场分析法、文本可视化方法、主成分分析法、多维尺度分析法和相关分析法等基于大数据的知识发现方法与技术进行了深入分析,发现了七条典型的教师成长行为路径,以及实践性知识是教师成长行为路径中的重要中介变量,其中,教师的教育信念、策略知识、情境知识和反思知识是最主要的中介变量。发现的七条典型的教师成长行为路径为网络研修设计者和一线教师均带来了一些重要启示。
This paper deeply analyzes the big data of classroom teaching behaviors, practical knowledge and online learning behaviors of 95 teachers in Siming District of Xiamen, Fujian Province by using knowledge discovery methods and technologies of the path analysis, cluster analysis, COI cognitive field analysis, text visualization, principal component analysis, multidimensional analysis and correlation analysis. 7 typical paths of teacher's growth behavior have been worked out and it is found that practical knowledge is an important mediating variable. Among them, educational beliefs, strategic knowledge, situational knowledge and reflective knowledge are the most important mediating variables. The seven typical paths of teacher's growth behavior could bring some important enlightenment to the designers of network training and classroom teachers.
This paper deeply analyzes the big data of classroom teaching behaviors, practical knowledge and online learning behaviors of 95 teachers in Siming District of Xiamen, Fujian Province by using knowledge discovery methods and technologies of the path analysis, cluster analysis, COI cognitive field analysis, text visualization, principal component analysis, multidimensional analysis and correlation analysis. 7 typical paths of teacher's growth behavior have been worked out and it is found that practical knowledge is an important mediating variable. Among them, educational beliefs, strategic knowledge, situational knowledge and reflective knowledge are the most important mediating variables. The seven typical paths of teacher's growth behavior could bring some important enlightenment to the designers of network training and classroom teachers.
引文
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[28]陈向明.实践性知识:教师专业发展的知识基础[J].北京大学教育评论,2003(1):105-113.
[2]BEEBY C F.The quality of education in developing countries[M].Cambridge:Harvard University Press,1966.
[3]田汉族,王子行.基于产权视角的义务教育阶段教师配置初探[J].教学与管理,2016(30):57-60.
[4]田梅.基于关联主义学习理论的智慧图书馆服务模式构建[J].图书馆学研究,2014(19):64-67,46.
[5]刘江玲.面向大数据的知识发现系统研究[J].情报科学,2014,32(3):90-92,101.
[6]维克托·迈尔-舍恩伯格,肯尼斯库克耶.大数据时代:生活、工作与思维的大变革[M].周涛,译.杭州:浙江人民出版社,2013.
[7]FRAWLEY W J,PIATETSKY-SHAPIRO G,MATHEUS C J.Knowledge discovery in databases:an overview[J].AI Magazine,1992,13(3):57-70.
[8]王陆,马如霞,张薇.教育大数据的知识发现方法与技术[M].北京:北京师范大学出版社,2018.
[9]PITRE R,KOLEKAR V.A survey paper on data mining with big data[J].International journal of innovative research and advanced engineering(IJIRAE),2014,1(1):178-180.
[10]余文森,连榕.教师专业发展[M].福州:福建教育出版社,2007.
[11]饶见维.教师专业发展:理论与实务[M].台北:五南出版社,2003.
[12]KARIUKI C.Professional development for 21st century teachers:effective classroom management[R].Alabama:Jackson State University,2009.
[13]HARGREAVES A.Four ages of professionalism and professional learning[J].Teachers and teaching:theory and practice,2000,6(2):151-182.
[14]ZEPEDA S J.Staff development:practices that promote leadership in learning communities[M].NY:Eye on education,1999.
[15]JACKSON A W,DAVIS G A.Turning points 2000:educating adolescents in the 21st century[M].New York:Teachers College Press,2000.
[16]教育部.中小学教师国家级培训计划取得显著成效[EB/OL].(2011-05-20)[2018-09-25].http://www.moe.edu.cn/jyb_xwfb/xw_zt/moe_357/s3582/s3922/s4568/201105/t20110530_120285.html.
[17]王陆.教师在线实践社区的知识共享与知识创新的机理分析[J].电化教育研究,2015(5):101-107.
[18]王陆,蔡荣啸.课堂大数据视角下的提问倾向研究[J].电化教育研究,2016(7):82-92.
[19]GUSKEY T R.Staff development and the process of teacher change[J].Educational researcher,1986,15(5):5-12.
[20]JOHNSON N,OWEN J.The two cultures revisted:integrating messages from models of teaching and clinical supervision to encourage improvement in teaching[C]//Proceedings of the Australian Educational Research Association Annual Conference,Melbourne,1986.
[21]CLARKE D,HOLLINGSWORTH H.Elaborating a model of teacher professional growth[J].Teaching and teacher education,2002,18:947-967.
[22]张燕南.大数据的教育领域应用研究---基于美国的应用实践[D].上海:华东师范大学,2016.
[23]HEY T,TANSLEY S,Tolle K.The fourth paradigm:Data-intensive scientific discovery,chapter Jim Gray on eScience:a transformed scientific method[M].Redmond,WA:Microsoft Research,2009.
[24]邓仲华,李志芳.科学研究范式的演化---大数据时代的科学研究第四范式[J].情报资料工作,2013(4):19-23.
[25]李金昌.大数据与统计新思维[J].统计研究,2014(1):10-17.
[26]SZETO E.Community of inquiry as an instructional approach:what effects of teaching,social and cognitive presences are there in blended[J].Computers&education,2015,81:191-201.
[27]ANDERSON T,ROURKE L,GARRISON D R,et al.Assessing teaching presence in a computer conferencing context[J].Journal of asynchronous learning networks,2001,5(2):1-17.
[28]陈向明.实践性知识:教师专业发展的知识基础[J].北京大学教育评论,2003(1):105-113.