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一、中文文獻 王聖灃(2021)。融合機器人的程式設計教學模式對國小學生程式設計與運算思維能力之研究[未出版之碩士論文]。國立臺北教育大學。 王鼎銘(1999)。科技發展與科技教育學習經驗。生活科技教育,32(4),6-14。 行政院(2021)。國情統計通報(135)。行政院主計處。 李柏勳(2022)。探討以6E模式結合遊戲化機制之教育機器人在STEM實作課程對國中生學習成效、動機及創造力之研究[未出版之碩士論文]。國立臺灣師範大學。 呂沂蓁(2020)。以小組合作學習進行 Scratch 程式設計對國中生運算思維的影響[未出版之碩士論文]。國立臺中教育大學。 汪殿杰、巫鍵志、王意蘭、吳致娟(2014)。強調動手實作的科技教育-以臺北市立大同高中為例。中等教育,65(4),141-151。 吳萬萊、湯庭禎(2021)。國中科技一上。翰林出版事業股份有限公司。 吳愛玲(1999)。台北市國民小學級任教師領導行為類型與學生學習行為之相關研究[未出版之碩士論文]。臺北市立師範學院。 周甘逢(1999)。品格與態度教學。載於林生傳(主編),教育心理學(頁141-170)。五南圖書出版公司。 林令惠(2021)。探討有無提示在程式積木學習中對於國小學童程式自我效能、運算思維之影響:以Hour of Code課程為例 [未出版之碩士論文]。國立臺灣科技大學。 林玉織(2022)。以遊戲式程式設計教學提升國小學生程式學習動機與運算思維能力 [未出版之碩士論文]。國立陽明交通大學。 洪詩玲(2010)。完成問題策略對基本程式概念教學的學習成效研究─以國小四年級學童為例[未出版之碩士論文]。國立交通大學。 洪聖宇(2021)。探討於STEM課程中十年級學生科學概念、工程設計能力及STEM態度之間的關係[未出版之碩士論文]。國立彰化師範大學。 施良方(1996)。學習理論。麗文文化公司。 教育部(2018)。十二年國民基本教育課程綱要─科技領域。 教育部(2020)。十二年國民基本教育課程綱要議題融入說明手冊。 教育部(2022)。性別平等教育白皮書2.0。 柳棟、吳俊杰、謝作如、沈涓(2013)。STEM、STEAM課程與可能的實踐路線。中小學訊息技術雜誌,6,39-41。 康致禎(2021)。編碼融入STEM課程對國小學生學習成效之影響 [未出版之碩士論文]。國立屏東大學。 陳怡倩(2016)。課程設計:統整課程設計的思維與趨勢。洪葉文化。 陳政佑(2019)。Micro:bit程式設計課程教學對於國小五年級兒童運算思維之影響 [未出版之碩士論文]。臺北市立大學。 陳政翰(2017)。設計思考模式結合STEM教學在高中生活科技機電整合單元之研究[未出版之碩士論文]。國立臺灣師範大學。 湯維玲(2019)。探究美國STEM與STEAM教育的發展。課程與教學,22(2),49-77。 黃敦晴(2018,8月28日)。從美國白宮發動的教改,為什麼STEM、STEAM教育這麼重要。親子天下。https://www.parenting.com.tw/article/5077735 黃雅戀(2019)。機器人程式設計課程對學童運算思維能力及 STEM 學習態度之影響 [未出版之碩士論文]。國立臺北教育大學。 鄔珮甄(2021)。運算思維自我效能量表編製與信效度驗證。[未出版之碩士論文]。國立台中教育大學。 蔡崇華(2019)。中小學生學習程式設計動機之研究。[未出版之碩士論文]。國立臺灣師範大學。 駱俞衡(2021)。探討STEM課程對十年級學生工程設計能力及STEM態度的影響 [未出版之碩士論文]。國立彰化師範大學。 羅希哲、陳柏豪、石儒居、蔡華齡、蔡慧音(2009)。STEM整合式教學法在國民中學自然與生活技術領域之研究。人文社會科學研究,3(3),42-66。
二、外文文獻 Arslan, K., & Tanel, Z. (2021). Analyzing the effects of Arduino applications on students’ opinions, attitude and self-efficacy in programming class. Education and Information Technologies, 26(1), 1143-1163. Autio, O., Hietanoro, J., & Ruismäki, H. (2011). Taking part in technology education: Elements in students’ motivation. International Journal of Technology and Design Education, 21(3), 349-361. Becker, K. H., & Park, K. (2011). Integrative approaches among science, technology, engineering, and mathematics (STEM) subjects on students’ learning: A meta-analysis. Journal of STEM education: Innovations and research, 12(5), 23-37. Bernhardt, S. (1997). Self-efficacy and second language learning. The NCLRC Language. Barry, N. B. (2014). The ITEEA 6E learning by DeSIGN model. Technology and Engineering Teacher, 73(6), 14-19. Berry III, R. Q., Reed, P. A., Ritz, J. M., Lin, C. Y., Hsiung, S., & Frazier, W. (2005). STEM initiatives: Stimulating students to improve science and mathematics achievement. The Technology Teacher, 64(4), 23-29. Bybee, R. W. (2010). What is STEM education? . Science, 329(5995), 996-996. Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. National Science Teachers Association. Caplan, M. (2018). Assessment of the impact of summer STEAM programs on high school participants’ content knowledge and attitude towards STEAM careers. Purdue e-Pubs. https://doi.org/10.5703/1288284316849 Chen, Y., Chow, S. C. F., & So, W. W. M. (2020). School-STEM professional collaboration to diversify stereotypes and increase interest in STEM careers among primary school students. Asia Pacific Journal of Education, 42(3), 1-18. https://doi.org/10.1080/02188791.2020.1841604 Choi, N. (2005). Self‐efficacy and self‐concept as predictors of college students' academic performance. Psychology in the Schools, 42(2), 197-205. Christensen, R., Knezek, G., & Tyler-Wood, T. (2015). Alignment of hands-on STEM engagement activities with positive STEM dispositions in secondary school students. Journal of Science Education and Technology, 24(6), 898-909. Chung, C.-C., & Lou, S.-J. (2021). Physical Computing Strategy to Support Students’ Coding Literacy: An Educational Experiment with Arduino Boards. Applied Sciences, 11(4), 1830. De la Fuente, J., & Cardelle-Elawar, M. (2009). Research on action–emotion style and study habits: Effects of individual differences on learning and academic performance of undergraduate students. Learning and Individual Differences, 19(4), 567-576. Department for Education, UK. (2013). National curriculum in England: computing programmes of study. Education, S. R. I. (2015). Principled assessment of computational thinking. Eguchi, A. (2016). RoboCupJunior for promoting STEM education, 21st century skills, and technological advancement through robotics competition. Robotics and Autonomous Systems, 75, 692-699. Ejiwale, J. A. (2012). Facilitating teaching and learning across STEM fields. Journal of STEM Education: Innovations and Research, 13(3), 87-94. Google(2010). Exploring Computational Thinking. https://www.google.com/edu/computational-thinking/ Gonzalez, H. B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics (STEM) education: A primer. Washington, DC: Congressional Research Service, Library of Congress. Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational researcher, 42(1), 38-43. Howland, K., Good, J., & Nicholson, K. (2009). Language-based support for computational thinking. 2009 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), 147-150. Jonas, M., & Sabin, M. (2015). Computational thinking in Greenfoot: AI game strageties for CS1: Conference workshop. Journal of Computing Sciences in Colleges, 30(6), 8-10. Kijima, R., & Sun, K. L. (2020). ‘Females don’t need to be reluctant’: Employing design thinking to harness creative confidence and interest in STEAM. International Journal of Art & Design Education, 40(1), 66-81. https://doi.org/10.1111/jade.12307 Ke, F. (2014). An implementation of design-based learning through creating educational computer games: A case study on mathematics learning during design and computing. Computers & Education, 73, 26-39. Kolb, D. A. (2014). Experiential learning: Experience as the source of learning and development. FT press. Kong, S. C. (2017). Development and validation of a programming self-efficacy scale for senior primary school learners. In Proceedings of the International Conference on Computational Thinking Education, 97-102. Kukul, V., & Karatas, S. (2019). Computational thinking self-efficacy scale: Development, validity and reliability. Informatics in Education, 18(1), 151-164. Lewis, M. (2010). Problem Solving through Programming with Greenfoot. Trinity University. Lou, S. J., Liu, Y. H., Shih, R. C., & Tseng, K. H. (2011). The senior high school students’ learning behavioral model of STEM in PBL. International Journal of Technology and Design Education, 21(2), 161-183. Luszczynska, A., Scholz, U., & Schwarzer, R. (2005). The general self-efficacy scale: multicultural validation studies. The Journal of psychology, 139(5), 439-457. Mahoney, M. P. (2010). Students' Attitudes toward STEM: Development of an Instrument for High School STEM-Based Programs. Journal of Technology Studies, 36(1), 24-34. Maiorca, C., Roberts, T., Jackson, C., Bush, S., Delaney, A., Mohr-Schroeder, M. J., & Soledad, S. Y. (2021). Informal Learning Environments and Impact on Interest in STEM Careers. International Journal of Science and Mathematics Education, 19(1), 45-64. https://doi.org/10.1007/s10763-019-10038-9. Massachusetts, D. O. E. (2006). Massachusetts Science and Technology Engineering Curriculum Framework. Malden: Massachusetts Department of Education. Mladenović, M., Žanko, Ž., & Aglić Čuvić, M. (2021). The impact of using program visualization techniques on learning basic programming concepts at the K–12 level. Computer Applications in Engineering Education, 29(1), 145-159. Mohr‐Schroeder, M. J., Jackson, C., Miller, M., Walcott, B., Little, D. L., Speler, L., Schooler, W., & Schroeder, D. C. (2014). Developing Middle School Students' Interests in STEM via Summer Learning Experiences: See Blue STEM Camp. School Science and Mathematics, 114(6), 291-301. National Governors Association (2011). Building a Science, technology, engineering and math agenda.Washington, D. C. National Research Council, N. R. (2011). Successful K-12 STEM education: Identifying effective approaches in science, technology, engineering, and mathematics. National Academies Press. National Science Board (2010). Science and Engineering Indicators. National Science Board. (2010). Science and Technology: Public Attitudes and Understanding. Science and Engineering Indicators(pp. 1-47). Nite, S. B., Morgan, J., Margaret, M., Capraro, R. M., Peterson, C. A., & Ieee. (2014). Science, Technology, Engineering and Mathematics (STEM) Education: A Longitudinal Examination of Secondary School Intervention. IEEE Frontiers in Education Conference, 1-7. Pinelli, T. E., & Haynie III, W. J. (2010). A Case for the Nationwide Inclusion of Engineering in the K-12 Curriculum via Technology Education. Journal of Technology Education, 21(2), 52-68. Plaza, P., Sancristobal, E., Carro, G., Blazquez, M., García-Loro, F., Muñoz, M., Albert, M. J., Moriñigo, B., & Castro, M. (2019). STEM and educational robotics using scratch. 2019 IEEE Global Engineering Education Conference, 330-336. 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. Roberts, T., Jackson, C., Mohr-Schroeder, M. J., Bush, S. B., Maiorca, C., Cavalcanti, M., Schroeder, D. C., Delaney, A., Putnam, L., & Cremeans, C. (2018). Students' perceptions of STEM learning after participating in a summer informal learning experience. International Journal of Stem Education, 5(1), 1-14. Shapiro, J. R., & Williams, A. M. (2012). The role of stereotype threats in undermining girls’ and women’s performance and interest in STEM fields. Sex roles, 66(3), 175-183. Subramaniam, M. M., Ahn, J., Fleischmann, K. R., & Druin, A. (2012). Reimagining the role of school libraries in STEM education: Creating hybrid spaces for exploration. The Library Quarterly, 82(2), 161-182. Tsai, M. J., Wang, C. Y., & Hsu, P. F. (2019). Developing the computer programming self-efficacy scale for computer literacy education. Journal of Educational Computing Research, 56(8), 1345-1360. Tucker, A. (2003). A model curriculum for k--12 computer science: Final report of the acm k--12 task force curriculum committee. Association for Computing Machinery. Unfried, A., Faber, M., & Wiebe, E. (2014). Gender and student attitudes toward science, technology, engineering, and mathematics. The Friday Institute for Educational Innovation at North Carolina State University, 51, 1-26. Vela, K. N., Bicer, A., Capraro, R. M., Barroso, L. R., Caldwell, C., & Ieee. (2018, Oct 03-06). What Matters to My Future: STEM Int-her-est and Expectations. 2018 IEEE Frontiers in Education Conference, 1-7. Voštinár, P., & Knežník, J. (2020). Experience with teaching with BBC micro: bit. 2020 IEEE Global Engineering Education Conference, 1306-131-. Walan, S., & Gericke, N. (2021). Factors from informal learning contributing to the children's interest in STEM - experiences from the out-of-school activity called Children's University. Research in Science & Technological Education, 39(2), 185-205. https://doi.org/10.1080/02635143.2019.1667321 Wang, C., & Frye, M. (2019). Minigems 2018 summer camp evaluation: Empowering middle school girls in steam. 2019 IEEE Integrated STEM Education Conference, 149-155. Weller, D. P., Bott, T. E., Caballero, M. D., & Irving, P. W. (2021). Developing a learning goal framework for computational thinking in computationally integrated physics classrooms. Cornell University. Wing, J. (2017). Computational thinking’s influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717-3725. Wang, L., Fu, L., & Hu, X. (2018). A Series of Scientific Practice Activities for Increasing Middle School Students' Interest in Robot. In Proceedings of the 2018 International Conference on Big data and education, 112-115. Yağcı, M. (2019). A valid and reliable tool for examining computational thinking skills. Education and Information Technologies, 24(1), 929-951. Young, V. M., House, A., Wang, H., Singleton, C., & Klopfenstein, K. (2011). Inclusive STEM schools: Early promise in Texas and unanswered questions. Highly Successful Schools or Programs for K-12 STEM Education. Washington, DC: National Academies, 1, 2014. 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.
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