A Study on the Relationship Between Teacher’s Data-Driven DecisionMaking and Professional Learning Community at Elementary Schools in Taipei City

• Main Authors: Hsu, Chun-Yi, 許群翊
• Other Authors: 黃旭鈞 博士
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/56407458289272379453

碩士 === 臺北市立大學 === 教育行政與評鑑研究所碩士在職專班 === 103 === The purpose of this study is to investigate the current situation of elementary school teachers’ perception of data-driven decision making and professional learning community and the relationship between these two variables. For the purpose of this study, a questionnaire is employed as the tool to survey 600 teachers of 42 public elementary schools in Taipei City, and 545 are valid samples. The data is analyzed by using descriptive statistics, t-test, one way ANOVA, exploratory factor analysis and structctural equation modeling to examine teachers’ perception of data-driven decision making and professional learning community and the relationship between them. The findings of the study are as follows: 1. The overall rating toward elementary school teachers’ perception of data-driven decision making and professional learning community is at a “good” level. 2. Teachers’ perception of data-driven decision making does cause significant differences according to different personal variables (age, educational level, present position, the years of teaching). 3. Teachers’ perception of professional learning community does cause significant differences according to different personal variables (age, the years of teaching). 4. Elementary school teachers’ perception of data-driven decision making has positive affect on professional learning community in Taipei City. According to the research findings, some suggestions are proposed for the educational administrative agencies, schools, teachers and for future research as well. Key words：elementary schools teacher, data-driven decision making, professional learning community, exploratory factor analysis, structctural equation modeling