English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 112704/143671 (78%)
Visitors : 49721517      Online Users : 540
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/153073


    Title: 臺北市國民中學教師資料導向決策與學生學習成效關係之研究:校長科技領導觀點
    A Study on the Relationship between Data-Driven Decision Making and Student Learning Outcomes among Junior High School Teachers in Taipei: A Perspective on Principal Technology Leadership
    Authors: 徐霈真
    Hsu, Pei-Chen
    Contributors: 張奕華
    Chang, I-Hua
    徐霈真
    Hsu, Pei-Chen
    Keywords: 資料導向決策
    學生學習成效
    校長科技領導
    結構方程模型
    Data-riven decision making
    Student learning outcomes
    Principal technology leadership
    Structural equation modeling
    Date: 2024
    Issue Date: 2024-09-04 13:43:24 (UTC+8)
    Abstract: 本研究旨在瞭解臺北市國民中教師在教師資料導向決策、學生學習成效與校長科技領導關係之現況分析,不同背景變項對教師資料導向決策、學生學習成效與校長科技領導之差異情形,研究分析此三變項間之相關,並建構其結構方程模型。本研究採調查研究法,研究工具為研究者自編問卷,問卷具備良好的信度與效度。本研究以臺北市國民中學教師為研究對象,抽樣48所學校,共計發放412份電子問卷,回收有效率達100%。資料處理分別以描述性統計、獨立樣本t檢定、變異數分析、皮爾森積差相關、結構方程模型進行分析。
    本研究主要研究發現如下:
    一、臺北市國中教師對教師資料導向決策、學生學習成效與校長科技領導之認同程度均屬高程度。
    二、教師因教育程度及擔任職務之不同,在知覺教師資料導向決策上有顯著差異。
    三、教師因擔任職務之不同,在知覺學生學習成效上有顯著差異。
    四、教師因性別、擔任職務及學校規模之不同,在知覺校長科技領導上有顯著差異。
    五、教師資料導向決策、學生學習成效與校長科技領導整體及各層面,彼此之間具有顯著正相關。
    六、校長科技領導對教師資料導向決策有顯著正向直接效果。
    七、教師資料導向決策對學生學習成效有顯著正向直接效果。
    八、校長科技領導對學生學習成效無顯著正向直接效果,但有顯著正向間接效果。
    本研究依據以上結論,分別提供教育行政機關、各級學校校長、教師、教育人員及未來後續研究作參考。
    This research aims to understand the current status of the relationship between data-driven decision making among junior high school teachers in Taipei, student learning outcomes, and principal technology leadership. It also analyzes the differences in teachers’ data-driven decision making, student learning outcomes, and principal technology leadership across different background variables. Furthermore, the study explores the correlation between these three factors and attempts to construct a structural equation model for them.
    In the research method, a questionnaire survey was adopted, and the questionnaire was developed based on literature and related scales. The population for the research consisted of current junior high school teachers in Taipei. A total of 48 schools were randomly selected using stratified sampling, and 412 valid questionnaires were collected, achieving an effective response rate of 100%. Data analysis includes descriptive statistics, independent sample t-tests, analysis of variance, Pearson product-moment correlation, and structural equation modeling to examine the mediating effect.
    The main findings of this study are as follows:
    1.The perception level of junior high school teachers in Taipei regarding data-driven decision making, student learning outcomes, and principal technology leadership is at an upper-moderate level.
    2.There are significant differences in teachers’ data-driven decision making based on educational attainment and job position.
    3.There are significant differences in student learning outcomes based on job position.
    4.There are significant differences in principal technology leadership based on gender, job position, and school size.
    5.Teachers’ data-driven decision making, student learning outcomes, and principal technology leadership are positively correlated.
    6.Principal technology leadership has a positive and significant effect on teachers’ data-driven decision making.
    7.Teachers’ data-driven decision making has a positive and significant effect on student learning outcomes.
    8.Principal technology leadership does not have a positive and significant effect on student learning outcomes, but has a positive indirect effect.
    Based on these conclusions, the study provides recommendations for educational administrative agencies, school principals at all levels, teachers, educational personnel, and future research directions.
    Reference: 王世英、謝雅惠(2005)。從資料驅動決定觀點簡介國立教育資料館教育資源。教育資料與研究,67,37-52。
    付達傑(2018)。教育大數據對教學決策的影響及優化策略。湖北成人教育學院學報,24,90-93。
    吳清山、林天祐(2007)。教育e辭書。高等教育出版社。
    李勇輝(2017)。學習動機、學習策略與學習成效關係之研究-以數位學習為例。經營管理學刊,14,68-86。
    阮士桂、鄭燕林(2016)。教師數據素養的構成、功用與發展策略。現代遠距離教育,163,60-65。
    林書兵、陳思琪、張學波(2021)。從數據素養到數據智慧:教學決策的實踐脈 絡與績效追問。中國電化教育,9,79-87。
    秦夢群(2010)。教育領導理論與應用。五南出版社。
    秦夢群、張奕華(2006)。校長科技領導層面與實施現況之研究。教育與心理研究,29(1),1-27。
    張奕華(2010)。校長科技領導—模式、指標與應用。洪葉出版社。
    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。
    張偉豪(2011)。SEM論文寫作不求人。鼎茂圖書。
    張維修(2018)。校長科技領導運用於學校系統改善之研究。清華教育學報,35(1),29-69。
    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。
    賈懷勤(2004)。數據、模型與決策。北京:對外經濟貿易大學出版社。
    謝文貴、黃旭鈞(2016)。國民小學分布式領導對學校效能影響之研究--以資料導向為中介變項。學校行政,105,63-84。
    A’ mar, F., & Eleyan, D. (2022). Effect of Principal’s Technology Leadership on Teacher’s Technology Integration. International Journal of Instruction, 15(1),781-798. https://doi.org/10.29333/iji.2022.15145a
    Abbott, M., Beecher, C., Petersen, S., Greenwood, C., & Atwater, J. (2017). A team approach to data-driven decision-making literacy instruction in preschool classrooms: child assessment and intervention through classroom team self-reflection. Young Exceptional Children, 20(3), 117-132. https://doi.org/10.1177/1096250615602297
    Adam, S. (2008). Learning Outcomes Current Developments in Europe: Update on the Issues and Applications of Learning Outcomes Associated with the Bologna Process. Bologna Expert.
    Anderson, R. E., & Dexter, S. (2005). School Technology Leadership: An Empirical Investigation of Prevalence and Effect. Educational Administration Quarterly, 41(1), 49-82. https://doi.org/10.1177/0013161X04269517
    Baig, M.I., Shuib, L. & Yadegaridehkordi, E. (2020). Big data in education: a state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education, 44, 1-23. https://doi.org/10.1186/s41239-020-00223-0
    BANOĞLUa, K. (2011). School Principals’ Technology Leadership Competency and Technology Coordinatorship. Educational Sciences: Theory and Practice, 11(1), 208-213.
    Barr, R. B., & Tagg, J. (2004). From Teaching to Learning -A New Paradigm for Undergraduate Education. Palomar College.
    Botvin M, Hershkovitz A, Forkosh-Baruch A. (2023). Data‑driven decision‑making in emergency remote teaching. Education and Information Technologies, 28(1), 489-506. https://doi.org/10.1007/s10639-022-11176-4
    Brown, L. (2014). Best Practices of Leadership in Educational Technology. Journal of Educational Technology, 11(1), 1-6. https://doi.org/10.26634/jet.11.1.2668
    Byrom, E., & Bingham M. (2001). Factors Influencingthe EffectiveUse of Technolofor Teachingand Learning. SERVE.
    ÇEVİK, M. S., & DOĞAN, E. (2023). The Mediating and Moderating Effects of Knowledge Management in the Relationship between Technological Leadership Behaviors of School Principals and Data-Driven Decision-Making. Educational Policy Analysis and Strategic Research 2023, 18(1), 50-76. https://doi.org/10.29329/epasr.2023.525.3
    Chauhan, S. (2017). A meta-analysis of the impact of technology on learning effectiveness of elementary students. Computers & Education, 105, 14-30. https://doi.org/10.1016/j.compedu.2016.11.005
    Chua, Y. P., & Chua, Y. P. (2017). Developing a Grounded Model for Educational Technology Leadership Practices. Education and Science, 42(189), 73-84. https://doi.org/10.15390/EB.2016.6705
    Clark, C. M., & Peterson, P. L. (1984). Teachers' Thought Processes. Occasional Paper No. 72. National Inst. of Education.
    Datnow, A., & Hubbard, L. (2016). Teacher Capacity for and Beliefs about Data-Driven Decision Making: A Literature Review of International Research. Springer.
    Doğan, E., & Demirbolat, A. O. (2021). Data-driven Decision-Making in Schools Scale: A Study of Validity and Reliability. International Journal of Curriculum and Instruction, 13(1), 507-523.
    Doğan, İ. (2018). Examination of the Technology Leadership Self-Efficacy Perceptions of Educational Managers in Terms of the Self-Efficacy Perceptions of Information Technologies (Malatya Province Case). Participatory Educational Research, 5(2), 51-66. https://doi.org/10.17275/per.18.9.5.2
    Duerden, M. D., & Rowan, J. C. (2023). Transformative Learning Outcomes: Shifting the Learning Outcome Conversation from Assessment to Design. International Journal of Teaching and Learning in Higher Education, 35(1), 186-194. https://doi.org/10.1016/j.jclepro.2020.125343
    Dunn, K. E., Skutnik, A., Patti, C., & Sohn, B. (2019). Disdain to Acceptance: Future Teachers’ Conceptual Change Related to Data-Driven Decision Making. Action in Teacher Education, 41(3), 193-211. https://doi.org/10.1080/01626620.2019.1582116
    Duque, L. C., & Weeks, J. R. (2010). Towards a model and methodology for assessing student learning outcomes and satisfaction. Quality Assurance in Education: An International Perspective, 18(2), 84-105. https://doi.org/10.21900/j.alise.2022.1048
    Ernawati, M. D. W., Sudarmin, S., Asrial, A., Haryanto, H., Azzahra, M. Z. & Triani, E. (2022).A study of attitude and interest in the student’s lessons. Cypriot Journal of Educational Sciences, 17(6), 1901-1913. https://doi.org/10.18844/cjes.v17i6.7484
    ESSA(2015). Every Student Succeeds Act of 2015. https://www.ed.gov/essa?src=rn
    Firestone, W. A., & Donaldson, M. L. (2019). Teacher evaluation as data use: what recent research suggests. Educational Assessment, Evaluation and Accountability, 31(3), 289-314. http://dx.doi.org/10.1007/s11092-019-09300-z
    Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130-160. https://doi.org/10.3102/0091732X20903304
    Fowler, R. L. (1992). Using the Extreme Groups Strategy When Measures Are Not Normally Distributed. Applied Psychological Measurement, 16(3), 249-259. https://doi.org/10.1177/014662169201600305
    Fox, D. (2013). The Principal's Mind-Set for Data. Leadership, 42(3), 12-16.
    Galbraith, C.S., Merrill, G.B. & Kline, D.M. (2012). Are Student Evaluations of Teaching Effectiveness Valid for Measuring Student Learning Outcomes in Business Related Classes? A Neural Network and Bayesian Analyses. Research in Higher Education, 53, 353-374. https://doi.org/10.1007/s11162-011-9229-0
    Gill, B., Coffee-Borden, B., & Hallgren, K. (2014). A Conceptual Framework for DataDriven Decision Making. Mathematica Policy Research. Mathematica Policy Research.
    Glasow, P. A. (2005). Fundamentals of Survey Research Methodology. MITRE Department.
    Hamilton, L., Halverson, R., Jackson, S.S., Mandinach, E., Supovitz, J.A., & Wayman, J.C. (2009). Using Student Achievement Data to Support Instructional Decision Making. National Center for Education Evaluation and Regional Assistance.
    Hamilton, V. M., & Reeves, T. D. (2021). Relationships between Course Taking and Teacher Self-Efficacy and Anxiety for Data-Driven Decision Making. Teacher Educator, 57(2), 136-154. https://doi.org/10.1080/08878730.2021.1965682
    Hero, J. L. (2019). The Impact of Technology Integration in Teaching Performance. Online Submission, International Journal of Sciences: Basic and Applied Research (IJSBAR), 48(1), 101-114.
    Hero, J. L. (2020). Exploring the Principal's Technology Leadership: Its Influence on Teachers' Technological Proficiency. International Journal of Academic Pedagogical Research, 4(6), 4-10.
    Hsieh, C.-C., Yen H.-C., & Kuan L.-Y. (2014). The Relationship among Principals' Technology Leadership, Teaching Innovation, and Students' Academic Optimism in Elementary Schools. International Conferences on Educational Technologies 2014.
    Huguet, A., Marsh, J. A., & Farrell, C. (2014). Building teachers' data-use capacity: insights from strong and developing coaches. Education Policy Analysis Archives, 22, 52. https://doi.org/10.14507/epaa.v22n52.2014
    Hunter, M. (1979). Teaching is decision making. Educational Leadership, 37(1), 62-64, 67.
    IFIP WG 3.4/3.7 International Conferences. (2014). Key Competencies in ICT and Informatics: Implications and Issues for Educational Professionals and Management. KCICTP and ITEM 2014.
    Kahraman, D., & Koc, M. (2022). Primary School Teachers’ Views on the Technological Competencies of School Principles. International Society for Technology, Education, and Science, Paper presented at the International Conference on Social and Education Sciences (IConSES).
    Kippers, W. B., Poortman, C. L., Schildkamp, K., & Visscher, A. J. (2018). Data literacy: What do educators learn and struggle with during a data use intervention? Studies in Educational Evaluation, 56, 21-31. https://doi.org/10.1016/j.stueduc.2017.11.001
    Kurtz, A. K., & Mayo, S. T. (1979). Statistical Methods in Education and Psychology. Springer.
    Lane, D. (2022). Analysis of Variance. https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Introductory_Statistics_(Lane)/15%3A_Analysis_of_Variance
    Li, X., & Xia, J. (2020). School-Based Practice Based on Supplemental Instruction of Big Data in Education. Science Insights Education Frontiers, 7(2), 913-933. https://doi.org/10.15354/sief.20.or063
    Lockton, M., Weddle, H., & Datnow, A. (2020). When Data Don't Drive: Teacher Agency in Data Use Efforts in Low-Performing Schools. School Effectiveness and School Improvement, 31(2), 243-265. https://doi.org/10.1080/09243453.2019.1647442
    Mandinach, E. B. (2012). A perfect time for data use: using data-driven decision making to inform practice. Educational Psychologist, 47(2), 71-85. https://doi.org/10.1080/00461520.2012.667064
    Mandinach, E. B., & Gummer, E. (2015). Data-Driven Decision Making: Components of the Enculturation of Data Use in Education. Teachers College Record: The Voice of Scholarship in Education, 117(4), 1-8. https://doi.org/10.1177/016146811511700402
    Mandinach, E. B., & Gummer, E. S. (2013). A Systemic View of Implementing Data Literacy in Educator Preparation. Educational Researcher, 42(1), 30-37. https://doi.org/10.3102/0013189X12459803
    Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366-376. https://doi.org/10.1016/j.tate.2016.07.011
    Mandinach, E. B., & Jimerson, J. B. (2016). Teachers learning how to use data: A synthesis of the issues and what is known. Teaching and Teacher Education, 60, 452-457. https://doi.org/10.1016/j.tate.2016.07.009
    Mandinach, E. B., & Schildkamp, K. (2021). Misconceptions about data-based decision making in education: An exploration of the literature. Studies in Educational Evaluation, 69, 1-10. https://doi.org/10.1016/j.stueduc.2020.100842
    Marantika, J. E. R. (2022). The relationship between learning styles, gender and learning outcomes. Cypriot Journal of Educational Science, 17(1), 56-67. https://doi.org/10.18844/cjes.v17i1.6681
    Maria, A., Maria Paz, P. E., & Isabel, G. P. (2023).Data-driven decision making as a model to improve in primary education. Journal of Education and e-Learning Research, 10(1), 36-42. https://doi.org/10.20448/jeelr.v10i1.4337
    Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. Santa Monica, CA: RAND Corporation.
    McNaught, C., Lam, P. & Cheng, K. F. (2012). Investigating Relationships between Features of Learning Designs and Student Learning Outcomes. Educational Technology Research and Development, 60(2), 271-286. https://doi.org/10.1007/s11423-011-9226-1
    Means, B., Padilla, C., & Gallagher, L. (2010). Use of Education Data at the Local Level. U.S. Department of Education.
    Nardi, P. M. (2018). Doing Survey Research:A Guide to Quantitative Methods. Routledge.
    Ohia, U. O. (2011). A Model For Effectively Assessing Student Learning Outcomes. Contemporary Issues in Education Research, 4(3), 25-32.
    Prenger, R., & Schildkamp, K. (2018). Data-based decision making for teacher and student learning: a psychological perspective on the role of the teacher. Educational Psychology, 38(6), 734-752. https://doi.org/10.1080/01443410.2018.1426834
    Puniatmaja,G. A., Parwati, N. N., Tegeh, I. M., & Sudatha, I. G. W. (2024). The Effect of E-learning and Students’ Digital Literacy towards Their Learning Outcomes. Pegem Journal of Education and Instruction, 14(1), 348-356. https://doi.org/10.47 750/pegegog.14.01.39
    Raman, A., Thannimalai, R., & Ismail, S. N. (2019). Principals' Technology Leadership and Its Effect on Teachers' Technology Integration in 21st Century Classrooms. International Journal of Instruction, 12(4), 423-442. https://doi.org/10.29333/iji.2019.12428a
    Roegman, R., Kenney, R., Maeda, Y., & Johns, G. (2021). When Data-Driven Decision Making Becomes Data-Driven Test Taking: A Case Study of a Midwestern High School. Educational Policy, 35(4), 535-565. https://doi.org/10.1177/089590481882374
    Samuels, P., & Gilchrist, M. (2014). Independent Samples t-test. https://www.statstutor.ac.uk/resources/uploaded/independentsamplesttest5.pdf
    Schildkamp, K. (2019). Data-Based Decision-Making for School Improvement: Research Insights and Gaps. Educational Research, 61(3), 257-273. https://doi.org/10.1080/00131881.2019.1625716
    Shavelson, R. J. (1973). The basic teaching skill: Decision making. Standford University.
    Smart, J.C. (2010). Differential Patterns of Change and Stability in Student Learning Outcomes in Holland's Academic Environments: The Role of Environmental Consistency. Research in Higher Education, 51, 468-482. http://dx.doi.org/10.1007/s11162-010-9163-6
    Suhr, D. D. (2006). Exploratory or Confirmatory Factor Analysis?Statistics and Data Analysis, 31, 1-17.
    Summers, D. (2023). Teachers' Use of Assessment Data to Improve Instruction and Student Achievement. Literature Reviews in Education and Human Services, 2(2), 21-49.
    Thakkar, J. J. (2020). Structural Equation Modelling Application for Research and Practice (with AMOS and R). Springer.
    Townsley, M., & Snyder, R. (2022). What are We Talking About? Data Use Among Education Leaders of Change. International Journal of Educational Leadership Preparation, 17(1), 88-100.
    Tsokos, C. P., & Ramachandran, K. M. (2009). Mathematical Statistics with Applications. Academic Press.
    TURAN, S., & GÖKBULUT, B. (2022). An Analysis of the Technology Leadership Behaviours of School Principals from the Perspective of Teachers. Turkish Online Journal of Educational Technology - TOJET, 21(1), 35-44. https://doi.org/10.17275/per.23.79.10.5
    van Geel, M., Keuning, T., Visscher, A., & Fox, J.-P. (2017). Changes in educators' data literacy during a data-based decision making intervention. Teaching and Teacher Education, 64,187-198. https://doi.org/10.1016/j.tate.2017.02.015
    Vannette, D. L., & Krosnick, J. A. (2018). The Palgrave Handbook of Survey Research. Palgrave Macmillan. https://doi.org/10.1007/978-3-319-54395-6
    Volkwein, J. F., Lattuca, L.R., Harper, B.J., & Domingo, R.J. (2007). Measuring the Impact of Professional Accreditation on Student Experiences and Learning Outcomes. Research in Higher Education, 48, 251-282.
    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. https://doi.org/10.1207/s15327671espr1003_5
    Description: 碩士
    國立政治大學
    學校行政碩士在職專班
    106911022
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106911022
    Data Type: thesis
    Appears in Collections:[學校行政碩士在職專班] 學位論文

    Files in This Item:

    File SizeFormat
    102201.pdf3404KbAdobe PDF0View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback