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研究生: 陳瑞婷
Chen, Jui-Ting
論文名稱: STEM策略對不同學習風格學生之影響:以巨量資料學習為例
The Effects of STEM Approach on Students with Different Learning Styles: Learning Big Data as an Example
指導教授: 吳正己
Wu, Cheng-Chih
口試委員: 吳正己
Wu, Cheng-Chih
林育慈
Lin, Yu-Tzu
胡秋帆
Hu, Chiu-Fan
口試日期: 2024/07/19
學位類別: 碩士
Master
系所名稱: 資訊教育研究所
Graduate Institute of Information and Computer Education
論文出版年: 2024
畢業學年度: 112
語文別: 中文
論文頁數: 106
中文關鍵詞: STEM策略巨量資料Kolb學習風格
英文關鍵詞: STEM Approach, Big Data, Kolb Learning Style
研究方法: 實驗設計法
DOI URL: http://doi.org/10.6345/NTNU202401476
論文種類: 學術論文
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  • 本研究以STEM科際整合教學策略(簡稱STEM策略)發展適合高中生學習的巨量資料課程單元,並且探討此教學策略對不同學習風格學生學習巨量資料概念之影響。STEM策略為將科學、科技、工程與數學等領域知識進行整合,以解決實際生活問題,本研究以生活中常見的議題──「空氣汙染」為學習情境,運用STEM策略設計實作活動,讓學生進行巨量資料概念的學習。研究採單組後測設計,參與者為47名台北市某公立高中之高二學生,教學實驗(含後測)為期四週共八節課。學生的學習風格分為資訊感知(具體經驗、抽象概念)與資訊轉換(反思觀察、主動驗證)兩個向度,探討STEM策略對其學習的影響。

    研究結果發現:(1)STEM策略能幫助學生理解巨量資料概念;(2)STEM策略對學生學習巨量資料的態度有正向影響;(3)STEM策略對於不同學習風格學生的學習成就、學習態度皆無顯著差異。建議未來研究應增加受試者人數;在巨量資料教學設計上,預留足夠教學時間進行實作活動,並以生活情境為脈絡進行教學時補充多元的例子說明,由不同情境中理解巨量資料概念。

    The study aims to develop a big data instructional module for high school students using the STEM approach and to evaluate the effects of the STEM approach on students' achievement and learning attitudes between different learning styles. The STEM approach integrates science, technology, engineering, and mathematics skills into the instructional module. In this study, the instructional module was developed based on “air pollution issue” and used it as the learning context. Hands-on activities that apply the STEM approach are designed for students to learn big data concepts. A single-group posttest design was implemented in the study. The participants were 47 10th grade students from a public high school in Taipei. The experiment lasted for four weeks. Learning styles are divided into two dimensions, including “grasping information” (concrete experience, abstract conceptualization) and “transforming information” (reflective observation, active experimentation). The study explored the effects of STEM approach on students between different learning styles.

    The research results showed that the STEM approach helps students realize big data concepts and facilitates students' positive attitude toward learning big data. No significant differences were observed in students’ learning achievement and attitudes between different learning styles when applying STEM approach. It is suggested that future studies should increase the number of participants, provide students with enough time for hands-on activities, and explain concepts through diverse examples when teaching big data.

    第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的與待答問題 3 第三節 研究限制 4 第四節 名詞釋義 4 第二章 文獻探討 6 第一節 STEM策略 6 第二節 Kolb學習風格 10 第三節 巨量資料學習 15 第四節 巨量資料概念教學困境與建議 21 第三章 研究方法 23 第一節 研究設計 23 第二節 研究參與者 24 第三節 研究流程 25 第四節 教學設計 26 第五節 研究工具 39 第六節 資料蒐集與分析 42 第七節 前導研究 43 第四章 結果與討論 48 第一節 STEM策略巨量資料課程單元評估 48 第二節 STEM策略對不同學習風格學生學習之影響 58 第五章 結論與建議 73 第一節 結論 73 第二節 建議 76 參考文獻 78 附錄 86

    AI4Kids(2021)。探究巨量資料:洞察大數據的奧秘。全華圖書股份有限公司。
    李新鄉、吳裕聖(2012)。〈族群、學習風格與 STS 教學對國中生自然與生活科技學習成效之影響 ─新住民子女是學習的弱勢者嗎?〉。《臺灣教育社會學研究》,(12)2,1-33。
    林坤誼(2018)。STEM教育在台灣推行的現況與省思。青年研究學報,21 (1),1-9。
    林清山(1992)。心理與教育統計學。東華書局。
    梁峻晢(2012)。國中生的學習概念類型與學習風格及學習態度對理化學習成就關係之研究。國立高雄師範大學碩士學位論文,高雄市。取自https://hdl.handle.net/11296/p286rc
    教育部(2018)。十二年國民基本教育課程綱要總綱。台北市。
    教育部(2019)。和AI做朋友 相知篇:從0開始學AI。
    楊雅斐(2015)。結對程式設計活動結合學習風格以提升學生學習動機與學習成就之研究。國立臺南大學博士學位論文,臺南市。取自https://hdl.handle.net/11296/5r3q9t
    臺北市政府教育局(2020)。高中AI生活大智慧。取自https://sites.google.com/csjh.tp.edu.tw/taipei-ai/
    蔡淑薇(2004)。高中職學生學習風格、自我調整學習與學業成就之關係。國立彰化師範大學碩士學位論文,彰化縣。取自https://hdl.handle.net/11296/64t8w7
    Adkins, D., & Guerreiro, M. (2018). Learning styles: Considerations for technology enhanced item design. British Journal of Educational Technology, 49(3), 574–583. https://doi.org/10.1111/bjet.12556
    Aqlan, F., Nwokeji, J. C., & Shamsan, A. (2020). Teaching an introductory data analytics course using microsoft access® and excel®. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1-10). IEEE.
    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, 17(1), 44. https://doi.org/10.1186/s41239-020-00223-0
    Becker, K., & Park, K. (2011). Effects of integrative approaches among science, technology, engineering, and mathematics (STEM) subjects on students’ learning: A preliminary meta-analysis.
    Buffum, P. S., Martinez-Arocho, A. G., Frankosky, M. H., Rodriguez, F. J., Wiebe, E. N., & Boyer, K. E. (2014). CS principles goes to middle school: Learning how to teach “Big Data.” Proceedings of the 45th ACM Technical Symposium on Computer Science Education, 151–156. https://doi.org/10.1145/2538862.2538949
    Cano-García, F., & Hughes, E. H. (2000). Learning and Thinking Styles: An analysis of their interrelationship and influence on academic achievement. Educational Psychology, 20(4), 413–430. https://doi.org/10.1080/713663755
    Caprile, M., Palmén, R., Sanz, P., & Dente, G. (2015). Encouraging STEM studies labour market situation and comparison of practices targeted at young people in different member states. Policy Department A, 12.
    Chang, C.-C., & Chen, Y. (2022). Using mastery learning theory to develop task-centered hands-on STEM learning of Arduino-based educational robotics: Psychomotor performance and perception by a convergent parallel mixed method. Interactive Learning Environments, 30(9), 1677–1692. https://doi.org/10.1080/10494820.2020.1741400
    Code.org. (2018). Computer Science Principles Curriculum Guide 2018-2019. Retrieved from https://fliphtml5.com/pcrbk/tfbw/basic
    Code.org. (2021). Computer Science Principles Curriculum Guide 2021-2022. Retrieved from https://docs.google.com/document/d/1k0wytFCORchnEg4G214yVFMZXJcUa2PoKaw-TrxtLjI/preview#heading=h.121itm2pggif
    Council, N. R. (2011). Successful K-12 STEM education: Identifying effective approaches in Science, Technology, Engineering, and Mathematics. The National Academies Press. https://doi.org/doi:10.17226/13158
    CSTA. (2017). CSTA K-12 Computer Science Standards. Retrieved from https://www.csteachers.org/page/standards
    Cuperman, D., & Verner, I. M. (2013). Learning through creating robotic models of biological systems. International Journal of Technology and Design Education, 23(4), 849–866. https://doi.org/10.1007/s10798-013-9235-y
    Demirbas, O. O., & Demirkan, H. (2007). Learning styles of design students and the relationship of academic performance and gender in design education. Learning and Instruction, 17(3), 345–359. https://doi.org/10.1016/j.learninstruc.2007.02.007
    Demirkan, H. (2016). An inquiry into the learning-style and knowledge-building preferences of interior architecture students. Design Studies, 44, 28-51.
    Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251-19257.
    Dewey, J.(1986). Experience and Education. The Educational Forum, 50(3), 241-252. https://doi.org/10.1080/00131728609335764
    Dryer, A., Walia, N., & Chattopadhyay, A. (2018). A Middle-School module for introducing data-mining, big-data, ethics and privacy using RapidMiner and a Hollywood theme. Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 753–758. https://doi.org/10.1145/3159450.3159553
    European Parliament, Directorate-General for Internal Policies of the Union, Caprile, M., Dente, G., Palmén, R., & Sanz, P. (2015). Encouraging STEM studies: Labour market situation and comparison of practices targeted at young people in different Member States. Publications Office. https://doi.org/10.2861/519030
    Gil, E., & Gibbs, A. (2016). Introducing secondary school students to big data and its social impact: A study within an innovative learning environment. Promoting Understanding of Statistics about Society IASE Roundtable Conference. https://doi.org/10.52041/SRAP.16401
    Hawk, T. F., & Shah, A. J. (2007). Using learning style instruments to enhance student learning. Decision Sciences Journal of Innovative Education, 5(1), 1–19. https://doi.org/10.1111/j.1540-4609.2007.00125.x
    Heffler, B. (2001). Individual learning style and the learning style inventory. Educational Studies, 27(3), 307–316. https://doi.org/10.1080/03055690120076583
    ISTE. (2017). ISTE Standards. Retrieved from https://cdn.iste.org/www-root/PDF/ISTE%20Standards-One-Sheet_Combined_09-2021_vF3.pdf
    K–12 Computer Science Framework. (2016). Retrieved from http://www.k12cs.org.
    Kolb, D. A. (1984). Experiential Learning: Experience as the source of learning and evelopment. New Jersey: Prentice Hall.
    Kolb, A. Y., & Kolb, D. A. (2005). The Kolb Learning Style Inventory—Version 3.1 2005 Technical Specifications.
    Kolb, A. Y., & Kolb, D. A. (2013). A Comprehensive Guide to the Theory, Psychometrics, Research on Validity and Educational Applications.
    Kulturel-Konak, S., D’Allegro, M. L., & Dickinson, S. (2011). Review Of Gender Differences In Learning Styles: Suggestions For STEM Education. Contemporary Issues in Education Research (CIER), 4(3), 9–18. https://doi.org/10.19030/cier.v4i3.4116
    Lee, M.-H., Chai, C. S., & Hong, H.-Y. (2019). STEM Education in Asia Pacific: Challenges and Development. The Asia-Pacific Education Researcher, 28(1), 1–4. https://doi.org/10.1007/s40299-018-0424-z
    Lou, S.-J., Shih, R.-C., Ray Diez, C., & Tseng, K.-H. (2011). The impact of problem-based learning strategies on STEM knowledge integration and attitudes: An exploratory study among female Taiwanese senior high school students. International Journal of Technology and Design Education, 21(2), 195–215. https://doi.org/10.1007/s10798-010-9114-8
    Mazur, D. J. (2016). Analyzing and interpreting “imperfect” Big Data in the 1600s. Big Data & Society, 3(1), 205395171560908. https://doi.org/10.1177/2053951715609082
    N. W. Grady, M. Underwood, A. Roy, & W. L. Chang. (2014). Big Data: Challenges, practices and technologies: NIST Big Data Public Working Group workshop at IEEE Big Data 2014. 2014 IEEE International Conference on Big Data (Big Data), 11–15. https://doi.org/10.1109/BigData.2014.7004470
    Park, Y., & Shin, Y. (2021). Tooee: A novel Scratch extension for K-12 big data and artificial intelligence education using text-based visual blocks. IEEE Access, 9, 149630–149646. https://doi.org/10.1109/ACCESS.2021.3125060
    Podworny, S., Fleischer, Y., Hüsing, S., Biehler, R., Frischemeier, D., Höper, L., & Schulte, C. (2021). Using data cards for teaching data based decision trees in middle school. Proceedings of the 21st Koli Calling International Conference on Computing Education Research, 1–3. https://doi.org/10.1145/3488042.3489966
    Rawson, K. A., Thomas, R. C., & Jacoby, L. L. (2015). The power of examples: Illustrative examples enhance conceptual learning of declarative concepts. Educational Psychology Review, 27, 483-504.
    Reynolds, Q. J., Gilliland, K. O., Smith, K., Walker, J. A., & Beck Dallaghan, G. L. (2020). Differences in medical student performance on examinations: exploring score variance between Kolb's learning style inventory classifications. BMC Medical Education, 20, 1-7.
    Sanders, M. E. (2012). Integrative STEM education as “best practice”. Griffith Institute for Educational Research, Queensland, Australia.
    Sangeetha, S., & Sreeja, A. (2015). No science no humans, no new technologies no changes" big data a great revolution. International Journal of Computer Science and Information Technologies, 6(4), 3269–3274.
    Science on Stage Europe. (2019). Coding in STEM Education. Retrieved from https://www.science-on-stage.eu/sites/default/files/material/coding_in_stem_education_en_2nd_edition.pdf
    Seyal, A., Mey, Y., Matusin, M., Siau, nor zainah, & Rahman, A. (2015). Understanding students learning style and their performance in computer programming course: Evidence from Bruneian technical institution of higher learning. International Journal of Computer Theory and Engineering, 7, 241–247. https://doi.org/10.7763/IJCTE.2015.V7.964
    Schultheis, E. H., & Kjelvik, M. K. (2015). Data nuggets: Bringing real data into the classroom to unearth students’ quantitative & inquiry skills. The American Biology Teacher, 77(1), 19-29.
    Song, I.-Y., & Zhu, Y. (2016). Big data and data science: What should we teach? Expert Systems, 33(4), 364–373. https://doi.org/10.1111/exsy.12130
    Sopapradit, S. (2022). STEM learning system with the Internet of Things through cloud learning to develop the digital literacy and creative products of higher education students in the 21st century. Journal of Theoretical and Applied Information Technology, 100(20).
    Svinicki, M. D., & Dixon, N. M. (1987). The Kolb model modified for classroom activities. College teaching, 35(4), 141-146.
    Thibaut, L., Ceuppens, S., De Loof, H., De Meester, J., Goovaerts, L., Struyf, A., Boeve-de Pauw, J., Dehaene, W., Deprez, J., De Cock, M., Hellinckx, L., Knipprath, H., Langie, G., Struyven, K., Van De Velde, D., Van Petegem, P., & Depaepe, F. (2018). Integrated STEM education: A systematic review of instructional practices in secondary education. European Journal of STEM Education, 3(1). https://doi.org/10.20897/ejsteme/85525
    Tong, P., & Yong, F. (2015). Implementing and Developing Big Data Analytics in the K-12 Curriculum- A Preliminary Stage.
    Vasquez J. A. (2013). Stem lesson essentials grades 3-8 : Integrating Science Technology Engineering and Mathematics. Heinemann.
    Wittrock, M. C. (2010). Learning as a generative process. Educational Psychologist, 45(1), 40-45.
    Wu, C. C., Dale, N. B., & Bethel, L. J. (1998). Conceptual models and cognitive learning styles in teaching recursion. In Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education (pp. 292-296).
    Wynd, W. R., & Bozman, C. S. (1996). Student learning style: A segmentation strategy for higher education. Journal of Education for Business, 71(4), 232-235.
    Zulfiani, Z., Suwarna, I. P., & Sumantri, M. F. (2020). Science Adaptive Assessment Tool: Kolb’ s Learning Style Profile and Student’ s Higher Order Thinking Skill Level. Jurnal Pendidikan IPA Indonesia, 9(2), 194-207.

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