簡易檢索 / 詳目顯示

研究生: 胡秋帆
Hu, Chiu-Fan
論文名稱: 高中生運算思維評量工具之發展
Development of a Computational Thinking Test for Senior High School Students
指導教授: 吳正己
Wu, Cheng-Chih
口試委員: 陳學志
Chen, Hsueh-Chih
林育慈
Lin, Yu-Tzu
劉晨鐘
Liu, Chen-Chung
邱瓊芳
Chiu, Chiung-Fang
吳正己
Wu, Cheng-Chih
口試日期: 2022/01/20
學位類別: 博士
Doctor
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2022
畢業學年度: 110
語文別: 中文
論文頁數: 70
中文關鍵詞: 運算思維評量測驗
英文關鍵詞: computational thinking, assessment, measurement
研究方法: 調查研究
DOI URL: http://doi.org/10.6345/NTNU202200325
論文種類: 學術論文
相關次數: 點閱:204下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 現有運算思維的評量工具,有些為自我評量方式,偏向瞭解學生對運算思維的理解與態度;有些為測驗方式,著重評量學生程式設計或資訊科學技能,但卻未能真正評量學生的運算思維通用能力。而且大多數現有評量工具,缺乏嚴謹試題發展過程及信、效度檢驗。
    本研究目的為發展評量高中生運算思維能力的工具,並探討與高中生運算思維能力有關的因素。本研究發展的運算思維測驗包含問題分解、資料表示、演算法、與模式一般化等四個構念。測驗試題由八位中學資訊教師出題及討論修改,再由四位大學資訊科學教育專家審題編修,另由一位教育測驗專家協助測驗實施與分析,以確保測驗信、效度。測驗試題共有12題,題型包括單選題及填充題,學生不需具備程式設計能力或資訊科學知識即可答題。本研究以北北基地區為施測範圍,採分層抽樣,分層標準為會考成績、年級及班群,總計有249位高中學生參與測驗。
    研究結果顯示,本測驗試題難度接近適中,並具鑑別度,Cronbach's alpha係數與折半信度顯示試題具內部一致性,個別試題也皆與總分呈正相關,所有題目具有同質性與穩定度。本測驗試題係由專家發展與編修,具專家效度;測驗總分與國際運算思維挑戰賽分數呈正相關,具有效標關聯效度。然而,在CFA模型檢驗結果顯示,四個構念相對適配度指標多未達良好適配度,構念效度仍待進一步檢驗。本研究分析結果也發現運算思維能力與性別、班群、程式設計學習傾向、選修程式設計相關課程經驗以及相關領域學科成就有關。
    本測驗發展嚴謹,具信、效度,測驗成績可呈現學生運算思維整體能力,並作為學生修習及升學資訊科學相關領域的參考。建議未來研究可精進試題之構念解釋力,精簡試題內容敘述,增加研究參與者數量,以及測驗施測安排等做適當調整。

    Some existing computational thinking (CT) assessment tools adopt self-report methods which measure students’ understanding, attitude, or disposition of CT. Others are tests that assess students’ knowledge and skills of programming and computer science but cannot effectively assess CT generic skills. In addition, most CT assessments lack a strict development procedure and reliability and validity tests.
    The purpose of this study is to develop an effective assessment for CT skills in senior high school. Additionally, this study also explored factors associated with CT skills. This study, as a result, established a language/tool/knowledge-independent test for CT in terms of the four CT concepts: decomposition, data representation, algorithm, and pattern generalization. Eight high school computing teachers drafted the items and then four computer scientists evaluated and revised the items. In addition, a testing specialist assisted with test administration and statistical analysis to ensure the tool’s reliability and validity. Finally, the CT test was composed of 12 items and was used in this study. The participants of this study consisted of 249 students from 4 senior high schools in Taipei metropolitan area in Taiwan. Stratified sampling (schools, grades, major subjects) was applied when recruiting the participants.
    Item analysis shows that this CT test is at medium difficulty level and each item is effective on assessing students’ CT skills. Based on the reliability and validity analysis result, Cronbach's alpha and Guttman values report acceptable internal consistency, and each item is positively correlated with total scores, indicating that there is homogeneity between 12 items and this CT test. The verification of content validity was conducted by computer scientists. The correlation between this CT test and the Bebras Challenge is positively significant. This proves the criterion-related validity. However, the CFA results show that the constructs of this CT test need to be examined in the future. This study finds that CT skills are under the influence of gender, major, programming disposition, taking programming related courses, and the performance of science and mathematics subjects.
    Through the strict development procedure, this CT test is a valid and reliable tool measuring overall CT skills. Also, it helps students determine whether taking computer science-related courses in high school or pursuing majors in university. This study proposed some recommendations for improving the explanatory power of this CT model, simplifying the context of items, increasing the number of participants, and test administration.

    第一章 緒論 1 第一節 研究背景 1 第二節 研究目的與問題 3 第二章 文獻探討 5 第一節 運算思維 5 第二節 運算思維評量 10 第三節 與運算思維有關的因素 21 第三章 研究方法 23 第一節 運算思維測驗試題發展 23 第二節 研究參與者 28 第三節 施測程序 29 第四節 研究工具 29 第五節 資料分析方法 31 第六節 前導研究 31 第四章 結果與討論 38 第一節 試題分析 38 第二節 信、效度檢驗 39 第三節 與學生運算思維能力有關因素探討 43 第五章 結論與建議 49 第一節 結論 49 第二節 建議 50 參考文獻 52 附錄-運算思維測驗試題 61

    余民寧(2011)。教育測驗與評量:成就測驗與教學評量。台北市:心理。
    周文欽、歐滄和、許擇基、盧欽銘、金樹人、范德鑫(1995)。心理與教育測驗。台北市:心理。
    郭生玉(2004)。心理與教育測驗。台北:精華。
    胡秋帆、王恩慈、吳正己、林育慈(2020)。十二年國教資訊科技科目學習次概念之探究。教育研究集刊,66(1),77-102。
    胡秋帆、吳正己、林育慈、游志弘(2021)。高中生運算思維測驗發展。數位學習科技期刊,13(1),1-21。
    教育部(2018)。十二年國民基本教育課程綱要:國民中小學暨普通型高級中等學校『科技領域』。臺北市:作者。
    蔡崇建(1991)。智力的評量與分析。台北市:心理。
    Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832-835. https://doi.org/10.1093/comjnl/bxs074
    Ambrósio, A. P., Xavier, C., & Georges, F. (2014). Digital ink for cognitive assessment of computational thinking. Proceedings of 2014 IEEE Frontiers in Education Conference (FIE) (pp. 1-7). IEEE. https://doi.org/10.1109/FIE.2014.7044237
    Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47-57.
    Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661-670. https://doi.org/10.1016/j.robot.2015.10.008
    Australian Curriculum, Assessment, Reporting Authority (ACARA). (2013). Digital Technologies. https://www.australiancurriculum.edu.au/f-10-curriculum/technologies/digital-technologies/
    Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: what is Involved and what is the role of the computer science education community? Inroads, 2(1), 48-54. https://doi.org/10.1145/1929887.1929905
    Bentler, P. M., & Yuan, K. H. (1999). Structural equation modeling with small samples: Test statistics. Multivariate Behavioral Research, 34(2), 181-197. https://doi.org/10.1207/S15327906Mb340203
    Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 annual meeting of the American educational research association (pp. 1-25).
    Buitrago Flórez, F., Casallas, R., Hernández, M., Reyes, A., Restrepo, S., & Danies, G. (2017). Changing a Generation’s Way of Thinking: Teaching Computational Thinking Through Programming. Review of Educational Research, 87(4), 834-860. doi:10.3102/0034654317710096
    Chen, G., Shen, J., Barth-Cohen, L., Jiang, S., Huang, X., & Eltoukhy, M. (2017). Assessing elementary students’ computational thinking in everyday reasoning and robotics programming. Computers & Education, 109, 162-175. https://doi.org/10.1016/j.compedu.2017.03.001
    Chen, L. M., Liu, K. S., & Cheng, Y. Y. (2012). Validation of the perceived school bullying severity scale. Educational Psychology, 32(2), 169-182. https://doi.org/10.1080/01443410.2011.633495
    Chi, H., & Jain, H. (2011). Teaching computing to STEM students via visualization tools. Procedia Computer Science, 4, 1937-1943. https://doi.org/10.1016/j.procs.2011.04.211
    Chu, Y. K., Liang, J. C., & Tsai, M. J. (2019). Development of a Computational Thinking Scale for Programming. Proceedings of International Conference on Computational Thinking Education 2019 (pp. 185-189). EdUHK.
    College Board. (2020). AP Computer Science Principles. https://apcentral.collegeboard.org/pdf/ap-computer-science-principles-course-and-exam-description.pdf
    Computer Science Teachers Association (CSTA). (2017). K-12 Computer Science Standards. http://www.csteachers.org/page/standards
    Curzon, P., Dorling, M., Ng, T., Selby, C., & Woollard, J. (2014). Developing computational thinking in the classroom: a framework. http://www.agbonline.co.uk/Files/Nat 20Curric 20Computing 202014/DevelopingComputationalThinkingInTheClassroomaFramework.pdf
    Dagienė, V., & Futschek, G. (2008). Bebras international contest on informatics and computer literacy: Criteria for good tasks. In R.T. Mittermeir, M.M. Sysło (Eds.), Lecture Notes in Computer Science: Vol. 5090. Informatics Education - Supporting Computational Thinking (pp. 15–29). Springer. https://doi.org/10.1007/978-3-540-69924-8_2
    Dagienė, V., & Stupuriene, G. (2016). Bebras--A Sustainable Community Building Model for the Concept Based Learning of Informatics and Computational Thinking. Informatics in education, 15(1), 25-44.
    Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? Computers & Education, 58(1), 240–249. https://doi.org/10.1016/j.compedu.2011.08.006
    Department for Education (DfE). (2013). National curriculum in England: computing programmes of study. https://www.gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study
    Djambong, T., & Freiman, V. (2016). Task-based assessment of students’ computational thinking skills developed through visual programming or tangible. Proceedings of 13th International Conference on Cogition and Exploratory Learning in Digital Age (pp.41–51). ERIC.
    Durak, H. Y., & Saritepeci, M. (2018). Analysis of the relation between computational thinking skills and various variables with the structural equation model. Computers & Education, 116, 191-202. https://doi.org/10.1016/j.compedu.2017.09.004
    Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling: a Multidisciplinary Journal, 6(1), 56-83. https://doi.org/10.1080/10705519909540119
    Google. (2015). Computational Thinking for Educators. https://computationalthinkingcourse.withgoogle.com/
    Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38-43. https://doi.org/10.3102/0013189X12463051
    Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., & Hosking, A. L. (2009). A multidisciplinary approach towards computational thinking for science majors. ACM SIGCSE Bulletin, 41(1), 183-187. https://doi.org/10.1145/1539024.1508931
    Henderson, P. B., Cortina, T. J., Hazzan O., & Wing, J. M. (2007). Computational thinking. ACM SIGCSE Bulletin, 39(1), 195-196. https://doi.org/10.1145/1227504.1227378
    Hu, C. F., Lin, Y. T., Wu, C. C., & Chen, H. C. (2022). A Programming Disposition Scale for High School Students. Journal of Educational Technology & Society, 25(2), 1-14.
    International Society for Technology in Education (ISTE) & Computer Science Teachers Association (CSTA). (2011). Operational definition of computational thinking for K–12 Education. https://cdn.iste.org/www-root/Computational_Thinking_Operational_Definition_ISTE.pdf
    Israel-Fishelson, R., Hershkovitz, A., Eguíluz, A., Garaizar, P., & Guenaga, M. (2020). The Associations Between Computational Thinking and Creativity: The Role of Personal Characteristics. Journal of Educational Computing Research, https://doi.org/10.1177/0735633120940954
    K–12 Computer Science Framework. (2016). https://k12cs.org/wp-content/uploads/2016/09/K–12-Computer-Science-Framework.pdf
    Kalelioglu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583.
    Kim, B., Kim, T., & Kim, J. (2013). Paper-and-Pencil Programming Strategy toward Computational Thinking for Non-Majors: Design Your Solution. Journal of Educational Computing Research, 49(4), 437-459. https://doi.org/10.2190/EC.49.4.b
    Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behavior, 72, 558-569. https://doi.org/10.1016/j.chb.2017.01.005
    Kramer, J. (2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 36-42. https://doi.org/10.1145/1232743.1232745
    Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J.,& Werner, L. (2011). Computational thinking for youth in practice. Acm Inroads, 2(1), 32-37. https://doi.org/10.1145/1929887.1929902
    Lin, Y. T., Wang, M. T., & Wu, C. C. (2019). Design and implementation of interdisciplinary STEM instruction: Teaching programming by computational physics. The Asia-Pacific Education Researcher, 28(1), 77-91. https://doi.org/10.1007/s40299-018-0415-0
    Lockwood, J., & Mooney, A. (2018). Computational Thinking in Education: Where does it fit? A systematic literary review. International Journal of Computer Science Education in Schools, 2(1), 41-60. https://doi.org/10.21585/ijcses.v2i1.26
    Marsh, H. W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling: A Multidisciplinary Journal, 5(1), 22-36. https://doi.org/10.1080/10705519809540087
    McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64–82. https://doi.org/10.1037/1082-989X.7.1.64
    Moreno-León, J., Robles, G., & Román-González, M. (2015). Dr. Scratch: Automatic analysis of scratch projects to assess and foster computational thinking. RED. Revista de Educación a Distancia, (46), 1-23.
    Mouza, C., Pan, Y. C., Yang, H., & Pollock, L. (2020). A Multiyear Investigation of Student Computational Thinking Concepts, Practices, and Perspectives in an After-School Computing Program. Journal of Educational Computing Research, https://doi.org/10.1177/0735633120905605
    Psycharis, S. (2013). Examining the effect of the computational models on learning performance, scientific reasoning, epistemic beliefs and argumentation: An implication for the STEM agenda. Computers & Education, 68, 253-265. https://doi.org/10.1016/j.compedu.2013.05.015
    Relkin, E., de Ruiter, L., & Bers, M. U. (2020). TechCheck: Development and Validation of an Unplugged Assessment of Computational Thinking in Early Childhood Education. Journal of Science Education and Technology, 29, 482-498. https://doi.org/10.1007/s10956-020-09831-x
    Rich, P. J., Egan, G., & Ellsworth, J. (2019). A Framework for Decomposition in Computational Thinking. Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (pp. 416-421). ACM. https://doi.org/10.1145/3304221.3319793
    Rogaten, J., & Moneta, G. B. (2015). Development and validation of the short use of creative cognition scale in studying. Educational Psychology, 35(3), 294-314. https://doi.org/10.1080/01443410.2013.857011
    Román-González, M., Pérez-González, J. C., Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 77, 678-691. https://doi.org/10.1016/j.chb.2016.08.047
    Rose, S., Habgood, J., & Jay, T. (2017). An exploration of the role of visual programming tools in the development of young children’s computational thinking. Electronic journal of e-learning, 15(4), 297-309. https://doi.org/10.34190/ejel.15.4.2368
    Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. https://eprints.soton.ac.uk/356481/
    Selby, C., Dorling, M., & Woollard, J. (2014). Evidence of assessing computational thinking. https://eprints.soton.ac.uk/372409/
    Shute, V. J., Sun, C., Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142-158. https://doi.org/10.1016/j.edurev.2017.09.003
    Singh, A. S. (2017). Common procedures for development, validity and reliability of a questionnaire. International Journal of Economics, Commerce and Management, 5(5), 790-801.
    So, H. J., Jong, M. S. Y., & Liu, C. C. (2020). Computational thinking education in the Asian Pacific region. The Asia-Pacific Education Researcher, 29(1), 1-8. https://doi.org/10.1007/s40299-019-00494-w
    Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 103798. https://doi.org/10.1016/j.compedu.2019.103798
    Tsai, M. J., Chien, F. P. F., Lee, S. W. Y., Hsu, C. Y., & Liang, J. C. (2022). Development and validation of the computational thinking test for elementary school students (CTT-ES): Correlate CT competency with Ct disposition. Journal of Educational Computing Research. https://doi.org/10.1177/07356331211051043
    Tsai, M. J., Liang, J. C., & Hsu, C. Y. (2021). The computational thinking scale for computer literacy education. Journal of Educational Computing Research, 59(4), 579-602. https://doi.org/10.1177/0735633120972356
    Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127-147. https://doi.org/10.1007/s10956-015-9581-5.
    Werner, L., Denner, J., Campe, S., & Kawamoto, D. C. (2012). The fairy performance assessment: measuring computational thinking in middle school. Proceedings of the 43rd ACM technical symposium on Computer Science Education (pp.215-220).ACM. https://doi.org/10.1145/2157136.2157200
    Wiebe, E., London, J., Aksit, O., Mott, B. W., Boyer, K. E., & Lester, J. C. (2019). Development of a lean computational thinking abilities assessment for middle grades students. Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 456-461). ACM. https://doi.org/10.1145/3287324.3287390
    Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
    Wing, J. M. (2011). Research notebook: Computational thinking—What and why. The Link Magazine, 20-23.
    Yurdakul, I. K., Odabasi, H. F., Kilicer, K., Coklar, A. N., Birinci, G., & Kurt, A. A. (2012). The development, validity and reliability of TPACK-deep: A technological pedagogical content knowledge scale. Computers & Education, 58(3), 964-977. https://doi.org/10.1016/j.compedu.2011.10.012
    Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education, 141, Article 103607. https://doi.org/10.1016/j.compedu.2019.103607
    Zhong, B., Wang, Q., Chen, J., & Li, Y. (2016). An exploration of three-dimensional integrated assessment for computational thinking. Journal of Educational Computing Research, 53(4), 562-590. https://doi.org/10.1177/0735633115608444

    無法下載圖示 電子全文延後公開
    2027/02/09
    QR CODE