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題名:國民小學資料導向決定模式建構之研究
作者:蘇玲慧
作者(外文):Su, Ling-Hui
校院名稱:臺北市立大學
系所名稱:教育學系
指導教授:吳清山
學位類別:博士
出版日期:2016
主題關鍵詞:資料導向決定模糊德懷術層級分析法data-driven decision makingFuzzy DelphiAnalytic Hierarchy Process
原始連結:連回原系統網址new window
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本研究旨在建構適於國內的國民小學資料導向決定模式。首先透過資料導向決定之相關文獻探討,作為建立資料導向決定模式內涵之初步建構;其次,敦請12位專家學者進行問卷內容效度審查,已確立模式的內涵與組成要素;再者採用模糊德懷術與層級分析法進行探究,邀請19位專家學者進行模式的適切性意見調查與相對權重問卷調查,並運用FuziCalc 1.51版、Excel 2010版及Power Choice V2.5軟體進行統計分析。本研究之主要結論如下:
一、國民小學資料導向決定模式包含「計畫階段」、「執行階段」、「評估階段」、「確認成效階段」與「回饋階段」等五個階段。
二、國民小學資料導向決定模式共有十五個組成要素,在計畫階段包含「學校目標」、「利害關係人」、「資料來源」三個要素,在執行階段包括「上級支持」、「領導風格」、「成員態度」、「資料庫建置」、「資料使用文化」五個要素,在評估階段包括「工具使用的適當」、「解讀資料的專業知能」、「時間的安排與運用」三個組成要素,在確認成效階段包括「學生學習成就」、「學生學習態度」兩個組成要素,在回饋階段包括「目標達成」及「檢討與修正」兩個組成要素。
三、資料導向決定模式之五個階段權重排序,以「計畫階段」之重要性程度最高,以「回饋階段」之重要性程度最低。
四、資料導向決定模式之十五個組成要素權重排序,分別以「學校目標」、「學生學習態度」、「目標達成」、「利害關係人」與「學生學習成就」,重要性位居前五。
根據研究結果提出建議,俾供教育行政主管機關、國民小學及未來研究作為參考。
The purpose of this study was to established a data-driven decision making model to be applied in elementary schools in Taiwan. Based on a literature review of data-driven decision making research, a preliminary model of data-driven decision making was constructed. After consulting with 12 scholars in this field for determining content validity, the content and composition of the model were be established. Moreover, this study adopted Fuzzy Delphi and Analytic Hierarchy Process for analysis. Nineteen experts participated in the propriety standards and relative weight surveys. Statistical analysis was conducted using FuziCalc 1.51, Excel 2010, Power Choice V2.5. The main findings of this study were as follows:
1. The data-driven decision making model of elementary schools involves 5 stages: planning, implementation, evaluation, result verification, and feedback.
2. The data-driven decision making model of elementary schools comprises 15 composition elements. At the planning stage, it consists of three components, including school goals, stakeholders and data sources. At the implementation stage, it consists of five components, including supervisory support, leadership style, member attitude, establishment of database and culture of data use. At the evaluation stage, it consists of three components, including tool applicability, data interpretation literacy, time arrangement. At result verification stage, it consist of two components, including student learning results and student learning attitude. At feedback stage, it consists of two components, including goal achievement and reviews and improvements.
3. According to the order based on the relative weight, planning was the most important stage of the data-driven decision making model.
4. According to the order based on the relative weight, school goals, student learning attitude, goal achievement, stakeholders, and student learning results were the 5 most crucial composition elements.
Based on these findings, this study proposes several suggestions as a reference for education administration authorities, elementary schools, and future research.
中文部分
王文科(1996)。教育研究法。臺北市:五南。new window
王世英、謝雅惠(2005)。從資料驅動觀點簡介國立教育資料館教育資源。教育資料與研究雙月刊,67,37-52。new window
王如哲(2000)。知識管理的理論與應用—以教育領域及其革新為例。臺北市:五南。new window
何奇南(2011)。國民中學校長資訊使用環境對資料導向決策影響之研究:結構方程模式之應用(未出版之碩士論文)。國立政治大學,臺北市。
余民寧、許嘉家、陳柏霖(2010)。中小學教師工作時數與憂鬱的關係:主觀幸福感的觀點。教育心理學報,42(2),229-252。new window
吳宗立(2005)。學校行政決策。高雄市:麗文。
吳政達(1999)。國民小學教師評鑑指標體系建構之研究(未出版之博士論文)。國立政治大學,臺北市。new window
吳政達(2008)。教育政策分析:概念、方法與應用(二版)。臺北市:高等教育。
吳清山(2004)。學校行政(第六版)。臺北市:心理。
吳清山、林天祐(1994)。全面品質管理及其在教育上的應用。初等教育學刊,3,1-28。new window
吳清山、林天祐(2006)。資料驅動決定。教育研究月刊,143,140。
吳清山、黃旭鈞(1995)。提昇教育品質的一股新動力:談全面品質管理及其在教育上的應用。教育資料與研究,2,74-83。new window
吳清山、黃旭鈞(2006)。國民小學推動知識管理之研究—有利條件、困境、功能與策略。教育研究月刊,52(2),33-65。
林其賢、高熏芳(2009)。資料導向決策系統之設計:校長決策領導的新思維。學校行政雙月刊,62,80-97。
林倩文(2014)。北北基高中職教學輔導教師運用資料導向決定之研究(未出版之碩士論文)。國立臺灣師範大學,臺北市。
林新發、黃秋鑾(2014)。推動校長教學領導以提升教師專業學習社群互動之策略。臺灣教育評論月刊,3(1),43-62。
范熾文(2007)。國民小學全面品質管理與學校組織績效的關係。載於中華民國品質學會第43屆年會暨第13屆品質管理研討會論文摘要集(13頁),臺北:中華民國品質學會。(ISBN978-95791467-4)
孫志麟(2002)。知識管理在學校組織的應用。教育研究月刊,99,44-45。new window
秦夢群(2006)。教育行政—理論部分。臺北市:五南。
張奕華(2010)。國民中小學校長資料導向決策(DDDM)及影響因素之分析:以資訊使用環境為前置變項。行政院國家科學委員會專題研究計畫(NSC 99-2410-H-004-025-MY2)。
張奕華(2013)。運用分析網路程序法建構國民小學校長資料導向決策指標之研究。教育行政研究,3(1),105-129。
張奕華、許正妹(2008)。 研究方法與軟體應用—概念與實例。臺北市:心理。
張奕華、彭文彬(2012)。高中職校長資訊使用環境對資料導向決策影響之研究。學校行政雙月刊,79,20-42。
張奕華、顏弘欽(2010)。教師專業能力發展新取向:DDDM模式的實踐。北縣教育季刊,71,11-16。
張淑涵(2011)。驅動學校發展的資料運用:一所公立高中之實踐經驗(未出版之碩士論文)。國立臺灣師範大學,臺北市。
張媛甯(2006)。高等教育機構組織文化改造之探討—應用TQM策略以建構教學品保系統為例。學校行政,43,64-81。
郭昭佑(2001)。教育評鑑指標建構方法探究。國教學報,13,251-278。
陳紹賓(2009)。資料導向決定在國民小學校長願景領導應用之研究—以臺北縣為例(未出版之碩士論文)。國立臺北教育大學,臺北市。
曾偉誠(2012)。臺北市國民小學教育人員資料導向決定與學校創新經營關係之研究(未出版之碩士論文)。臺北市立教育大學,臺北市。
評鑑雙月刊編輯部(2015)。大數據治校。評鑑雙月刊,57,8。
黃旭鈞(2013)。促進學校改進的策略:「資料導向決定」的觀點。教育研究月刊,232,65-79。new window
黃建翔(2014)。國民小學校長知識領導、資料導向決定與學校創新經營效能關係之研究(未出版之博士論文)。臺北市立大學,臺北市。
詹秀雯(2013)。運用資料導向決策教學提升高中身障生學習成效—以數學科為例。身心障礙研究,11(1),27-43。
劉名峯(2006)。國民小學校長應用資料導向決定之研究(未出版之碩士論文)。國立臺北教育大學,臺北市。
劉春榮(2003)。教師績效評鑑的教育品質觀點。教育資料與研究,53,13-19。new window
劉約蘭(2009)。全球在地化教育行政決策模式建構之研究(未出版之博士論文)。國立臺灣師範大學,臺北市。new window
鄧振源、曾國雄(1989)。層級分析法(AHP)的內涵特性與應用(上)。中國統計學報,27(6),13707-13724。new window
蕭佳純、胡夢鯨(2007)。影響成人教育工作者知識管理能力因素之跨層次分析。教育學刊,29,1-36。new window
謝文全(2009)。教育行政學(三版)。臺北市:高等教育。
簡瑋成(2012)。中小學教師素質管理探究之全面品質管理觀點。教育人力與專業發展,29(1),37-48。

英文部分
Abbott, D. V. (2008). A functionality framework for educational organizations: Achieving accountability at scale. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp. 257-276). New York, NY: Teachers College Press.
Andriessen, J. H. E., Soekijad M., & Keasberry, H. J. (2002). Support for knowledge sharing in communities. Delft, Netherlands: Delft University Press.
Cabrera, E. F., & Cabrera, A. (2005). Fostering knowledge sharing through people management practices. The International Journal of Human Resource Management, 16(5), 720-735.
Campbell, C., & Levin, B. (2009). Using data to support educational improvement. Educational Assessment, Evaluation and Accountability, 21(1), 47-65.
Carlson, D., Borman, G., & Robinson, M. (2011). A multistate district-level cluster randomized trial of the impact of data-driven reform on reading and mathematics achievement. Educational Evaluation and Policy Analysis, 33(3), 378-398.
Confrey, J., & Makar, K. M. (2005). Critiquing and improving the use of data from high-stakes tests with the aid of dynamic statistics software. In C. Dede, J. P. Honan, & L. C. Peters (Eds.), Scaling up success: Lessons learned from technology-based educational improvement (pp. 198-226). San Francisco, CA: Jossey-Bass.
Danielian, H. J. (2009). District level practices in data driven decision making (Unpublished doctoral dissertation). University of Southern California, Los Angeles, California.
Datnow, A., & Park, V. (2009). School system strategies for supporting data use. In T. J. Kowalski & T. J. Lasley II (Eds.), Handbook of data-based decision making in education (pp. 191-206). New York, NY: Routledge.
Datnow, A., & Park, V. (2014). Data-driven leadership. San Francisco, CA: Jossey-Bass.
Datnow, A., Park, V., & Kennedy-Lewis, B. (2012). High school teachers’ use of data to inform instruction. Journal of Education for Students Placed at Risk, 17(4), 247-265.
Datnow, A., Park, V., & Wohlstetter, P. (2007). Achieving with data: How high-performing school systems use data to improve instruction for elementary students. San Francisco, CA: Center on Educational Governance University of California.
de Lima, J. Á. (2008). Department networks and distributed leadership in schools. School Leadership & Management, 28(2), 159-187.
Dembosky, J. W., Pane, J. F., Barney, H., & Christina, R. (2005). Data drive decision making in Southwestern Pennsylvania school districts. RAND Corporation. Retrieved from http://www.rand.org/pubs/working_papers/2006/RAND_WR326.sum.pdf
Dunn, K. E., Airola, D. T., Lo, W. J., & Garrison, M. (2013). What teachers think about what they can do with data: Development and validation of the data driven decision-making efficacy and anxiety inventory. Contemporary Educational Psychology, 38(1), 87-98.
Earl, L. M., & Katz, S. (2010). Creating a culture of inquiry: Harnessing data for professional learning. In A. M. Blankstein, P. D. Houston, & R. W. Cole (Eds.), Data enhanced leadership (pp. 9-30). Thousand Oaks, CA: Corwin.
Easton, J. Q. (2009, July). Using data systems to drive school improvement. Keynote address at the STATS-DC 2009 Conference, Bethesda, MD.
Farooq, M. S., Akhtar, M. S., Ullah, S. Z., & Memon, R. A. (2007). Application of total quality management in education. Journal of Quality and Technology Management, 3 (2), 87-97.
Feldman, J., & Tung, R. (2001a). Using data-based inquiry and decision-making to improve instruction. ERS Spectrum, 19(3), 10-19.
Feldman, J., & Tung, R. (April 10-14, 2001b). Whole school reform: How schools use the data-based inquiry and decision making process. Paper presented at the American educational research association conference, Seattle.
Gentry, D. R. (2005). Technology supported data-driven decision-making in an Oklahoma elementary school (Unpublished doctoral dissertation). University of Oklahoma, Norman, Oklahoma.
Halverson, R., Grigg, J., Prichett, R. B., & Thomas, C. (2007). The new instructional leadership: Creating data-driven instructional systems in schools. Journal of School Leadership, 17(2), 159-193.
Hamilton, L. S., Stecher, B. M., & Klein, S. P. (2002). Making sense of test-based accountability in education. Santa Monica, CA: RAND Education.
Hamilton, L. S., Stecher, B. M., & Yuan, K. (2009). Standards-based reform in the United States: History, research, and future directions. Washington, DC: Center on Education Policy.
Hamilton, L., Halverson, R., Jackson, S., Mandinach, E., Supovitz, J., & Wayman, J. (2009). Using student achievement data to support instructional decision making (NCEE 2009-4067). Washington, DC: National Center for Education and Regional Assistance, Institute of Education Sciences, U.S. Department of Education. Retrieved from http://ies.ed.gov/ncee/wwc/publications/practice guides/
Hammerman, J. K., & Rubin, A. (2003). Reasoning in the presence of variability. Paper presented at the Third International Research Forum on Statistical Reasoning, Thinking, and Literacy (SRTL-3), Lincoln, NE.
Hargreaves, A., & Fullan, M. (2012). Professional capital: Transforming teaching in every school. New York, NY: Teachers College Press.
Hargreaves, A., Morton, B., Braun, H., & Gurn, A. M. (2015). The changing dynamics of educational judgment and decision making in a data-driven world. In S. Chitpin & C. W. Evers (Eds.), Decision making in educational leadership: Principles, policies, and practices (pp. 3-20). New York, NY: Routledge.
Ikemoto, G. S., & Marsh, J. A. (2007). Cutting through the “data-driven” mantra: Different conceptions of data-driven decision making. In P. A. Moss (Ed.), Evidence and decision making: 106th yearbook of the National Society for the Study of Education: Part I (pp. 104-131). Malden, MA: Blackwell Publishing.
Ingram, D., Louis, K. S., & Schroeder, R. G. (2004). Accountability policies and teacher decision making: Barriers to the use of data to improve practice. Teachers College Record, 106(6), 1258-1287.
Kalling, T., & Styhre. A. (2003). Knowledge sharing in organizations. Copenhagen, Sweden: Copenhagen Business School Press.
Kennedy, B. L., & Datnow, A. (2011). Student involvement and data-driven decision making: Developing a new typology. Youth & Society, 43(4), 1246-1271.
Lai, M. K., & McNaughton, S. (2013). Analysis and discussion of classroom and achievement data to raise student achievement. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 23-47). New York, NY: Springer.
Lai, M. K., & Schildkamp, K. (2013). Data-based decision making: An overview. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 9-21). New York, NY: Springer.
Lai, M. K., McNaughton, S., Amituanai-Toloa, M., Turner, R., & Hsiao, S. (2009). Sustained acceleration of achievement in reading comprehension: The New Zealand experience. Reading Research Quarterly, 44(1), 30-56.
Ledoux, G., Blok, H., Boogaard, M., & Krüger, M. (2009). Opbregstgericht werken. Over de waarde van meetgestuurd onderwijs (Data-driven decision making: the value of data-driven education). Amsterdam: SCO-Kohnstamm Instituut.
Lemak, D. J., Mero, N. P., & Reed, R. (2002). When quality works: A premature post-mortem on TQM. Journal of Business and Management, 8(4), 391-410.
Levin, J. A., & Datnow, A. (2012). The principal role in data-driven decision making: Using case-study data to develop multi-mediator models of educational reform. School Effectiveness and School Improvement, 23(2), 179-201.
Light, D., Wexler, D., & Heinze, J. (2004, April). How practitioners interpret and link data to instruction: Research findings on New York City Schools’ implementation of the Grow Network. Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.
Long, L., Rivas, L., Light, D., & Mandinach, E. B. (2008). The evolution of a homegrown data warehouse: TUSDstats. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp. 209-232). New York, NY: Teachers College Press.
Love, N., Stiles, K. E., Mundry, S., DiRanna, K. (2008). The data coach’s guide to improving learning for all students: Unleashing the power of collaborative inquiry. Thousand Oaks, CA: Corwin.
Mandinach, E. B., & Gummer, E. S. (2011). The complexities of integrating data-driven decision making into professional preparation in schools of education: It’s harder than you think. Alexandria, VA, Portland, OR, and Washington, DC: CAN Education, Education Northwest, and WesEd.
Mandinach, E. B., & Jackson, S. S. (2012). Transforming teaching and learning through data-driven decision making. Thousand Oaks, CA: Corwin.
Mandinach, E. B., & Smith, N. J. (2011). Leveraging the power of state longitudinal data systems: Building capacity to turn data into useful information. Washington, DC: Data Quality Campaign.
Mandinach, E. B., Honey, M., Light, D., & Brunner, C. (2008). A conceptual framework for data-driven decision making. In E. B. Mandinach & M. Honey (Eds.), Data-driven school improvement: Linking data and learning (pp. 13-31). New York, NY: Teachers College Press.
Mandinach, E. B., Honey, M., Light, D., Heinze, J., & Rivas, L. (2005). Technology-based tools that facilitate data-driven instructional decision making. Paper presented at the ICCE Conference, Singapore.
Marsh, J. A., McCombs, J.S., & Martorell, F. (2010). How instructional coaches support data-driven decision making policy implementation and effects in Florida middle schools. Educational Policy, 24(6), 872-907.
Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education. Retrieved from http://www.rand.org/pubs/occasional_papers/2006/RAND_OP170.pdf
Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. London: John Murray.
McElroy, M. W. (2003). The new knowledge management: Complexity, learning and sustainable innovation. Burlington, MA: Butterworth-Heinemann.
Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers’ ability to use data to inform instruction: Challenges and supports. Washington, D.C: U.S. Department of Education Office of Planning, Evaluation and Policy Development. Retrieved from the http://www2.ed.gov/rschstat/eval/data-to-inform-instruction/report.pdf
Means, B., Padilla, C., & Gallagher, L. (2010). Use of education data at the local level: From accountability to instructional improvement. Washington, DC: U.S. Department of Education, Office of Planning, Evaluation, and Policy Development.
Militello, M. (2005, April). Too much information: A case study of assessment and accountability in an urban school district. Paper presented at the annual meeting for American Educational Research Association, Montreal, Canada.
Mukhopadhyay, M. (2005). Total quality management in education (2nd ed.). Thousand Oaks, CA: Sage.
Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press.
Nurluoz, Ö., & Birol, C. (2011). The impact of knowledge management and technology: An analysis of administrative behaviours. The Turkish Online Journal of Educational Technology, 10(1), 202-208.
Owlia, M. S. (2010). A framework for quality dimensions of knowledge management Systems. Total Quality Management & Business Excellence, 21(11), 1215-1228.
Paliszkiewicz, J. (2007). Knowledge management: An integrative view and empirical examination. Cybernetics and Systems, 38(8), 825-836.
Park, V., & Datnow, A. (2009). Co-constructing distributed leadership: District and school connections in data-driven decision-making. School Leadership and Management, 29(5), 477-494.
Petrides, L., & Nguyen, L. (2006). Knowledge management trends: Challenges and opportunities for education institutions. In A. S. Metcalfe (Ed.), Knowledge management and higher education: A critical analysis (pp. 21-33). Hershey, PA: Idea Group.
Picciano, A. G. (2006). Data-driven decision making for effective school leadership. Upper Saddle River, NJ: Prentice Hall.
Ribeiro, J., & Moreira, A. (2010). ICT training for special education frontline professionals: A perspective from students of a master’s degree on special education. International Journal of Emerging Technologies in Learning, 5(2), 55-59.
Roberts, J. (2010). Communities of management knowledge diffusion. Prometheus, 28(2), 111-132.
Robinson, V. M. J., & Lai, M. K. (2006). Practitioner research for educators: A guide to improving classrooms and schools. California: Thousand Oaks Corwin.
Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
Schildkamp, K., & Ehren, M. (2013). From “Intuition” to “Data”-based decision making in Dutch secondary schools? In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 49-67). New York, NY: Springer.
Schildkamp, K., & Kuiper, W. (2010). Data-informed curriculum reform: Which data, what purposes, and promoting and hindering factors. Teaching and Teacher Education, 26, 482-496.
Schildkamp, K., & Lai, M. K. (2013). Conclusions and a data use framework. In K. Schildkamp, M. K. Lai, & L. Earl (Eds.), Data-based decision making in education: Challenges and opportunities (pp. 177-191). New York, NY: Springer.
Shoham, S., & Perry, M. (2009). Knowledge management as a mechanism for technological and organizational change management in Israeli universities. Higher Education: The International Journal of Higher Education and Educational Planning, 57(2), 227-246.
Sikes, K. A. (2008). Investigating teachers’ perceptions of the data-driven decision making process at a Georgia elementary school (Unpublished doctoral dissertation). Cambridge College, Cambridge, Massachusetts.
Simpson, G. H. (2011). School leaders’ use of data-driven decision-making for school improvement: A study of promising practices in two California charter schools (Unpublished doctoral dissertation). University of Southern California, Los Angeles, California.
Song, S., Nerur, S., & Teng, T. C. (2007). An exploratory study on the roles of network structure and knowledge processing orientation in work unit knowledge management. Database for Advances in Information Systems, 38, 8-26.
Streifer, P. A. (2002). Using data to make better educational decisions. Lanham, MD: Scarecrow Press.
Sun, J. P. (2015). Principals’ evidence-based decision making: Its nature and impacts. In S. Chitpin & C. W. Evers (Eds.), Decision making in educational leadership: Principles, policies, and practices (pp. 21-37). New York, NY: Routledge.
Timperley, H., Wilson, A., Barrar, H., & Fung, I. (2007). Best evidence synthesis iterations (BES) on professional learning and development. Wellington: Ministry of Education.
Wayman, J. C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of Education for Students Placed at Risk, 10(3), 295-308.
Wayman, J. C. (2007). Student data systems for school improvement: The state of the field. TCEA In Educational Technology Research Symposium: Vol. 1 (pp. 156-162). Lancaster, PA: ProActive.
Wayman, J. C., & Stringfield, S. (2006). Technology-supported involvement of entire faculties in examination of student data for instructional improvement. American Journal of Education, 112, 549-571.
Wayman, J. C., Cho, V., Jimerson, J. B., & Snodgrass Rangel, V. W. (2010, May). The data-informed district: A systemic approach to educational data use. Paper presented at the annual meeting of the American Educational Research Association, Denver.
Wayman, J. C., Spring, S. D., Lemke, M. A., & Lehr, M. D. (2012, April). Using data to inform practice: Effective principal leadership strategies. Paper presented at the 2012 Annual Meeting of the American Educational Research Association, Vancouver, Canada.
White, V. C. (2008). Relationships among principals’ beliefs about data-driven decision making, principal and school characteristics, and student achievement in elementary schools (Unpublished doctoral dissertation). University of Florida, Gainesville, Florida.
Wohlstetter, P., Datnow, A., & Park, V. (2008). Creating a system for data-driven decision-making: Applying the principal-agent framework. School Effectiveness and School Improvement, 19(3), 239-259.
Young, V. M. (2006). Teachers’ use of data: Loose coupling, agenda setting, and team norms. American Journal of Education, 112(4), 521-548.

 
 
 
 
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