隨著跨領域學習和科技工具融入教學的趨勢，機器人教學也成為各領域探討的主題。本研究透過延伸計畫行為理論模式，發展中學教師機器人教學行為意向之量表，並以此量表探究中學教師實施機器人教學的影響因素。調查方法採線上問卷填答方式，正式施測之研究對象包含53位具有機器人教學經驗之現職中學教師。利用偏最小平方法結構方程模型進行資料分析，驗證量表之信、效度，以及因素間的影響程度，提出中學教師實施機器人教學的行為模式。研究結果顯示：（1）價值認定、主觀規範、以及知覺行為控制會影響行為意向；（2）態度對行為意向並沒有明顯的影響力；（3）知覺行為控制與行為意向皆會影響實際教學行為；（4）態度、知覺行為控制、以及行為意向分別受到價值認定的影響。此外，依據研究結果與發現於研究最後針對機器人教學實施層面及研究層面提出未來建議。

With the trend of interdisciplinary learning and technological tools being integrated into education, robotics education has become one of the most popular courses. Based on extending the theory of planned behavior (TPB), this research developed a scale of "Robotic education behavior intention of secondary school teachers" with six factors, including attitude, subjective norm, perceived behavior control, intention, behavior, and value. With this scale, the influencing factors of secondary teachers’ robotics education and the relationship among these factors were explored. The online survey was used. There were 53 secondary teachers with experience in robotics education recruited, and the partial least squares-SEM was used for data analysis. The research results indicated that: (1) subjective norm, perceived behavior control, and value were significant predictors of intention; (2) attitude had no influence on intention; (3) perceived behavior control and intention both had positive influences on behavior; (4) there were positive influencing relationship between value and attitude, perceived behavior control, and intention. In addition, according to the research findings, future suggestions were proposed for the implication and research in robotics education.

一、中文部分
林坤誼（2018）。STEM 教育在台灣推行的現況與省思。香港青年研究學報，21(1)，107-115。
胡淑華、蔡孟蓉（2019）。國中機器人STEAM跨領域課程發展研究：以彰化縣二水國中培龍計畫為例。數位學習科技期刊，11(4)，51-75。
張基成、陳怡靜（2018）。機器人跨領域 STEM 主題式統整課程與任務導向式教學的設計及評鑑。科學教育學刊，26(4)，305-331。
張基成、曾繁勛、嚴萬軒、陳怡靜（2019）。帆船機器人 STEM 跨領域統整課程的發展及學生認知成就與態度–網狀式主題統整與重理解的課程設計。第八屆工程、技術與科技教育學術研討會，國立臺灣師範大學。
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。取自 https://www.k12ea.gov.tw/files/class_ schema/課綱/13-科技/13-1/十二年國民基本教育課程綱要國民中學暨普通型高級中等學校─科技領域.pdf
趙嘉浩、梁至中、蔡孟蓉（2017）。機器人課程教材鷹架對高中生未來關鍵學習能力的影響。數位學習科技期刊，9(3)，95-114。

二、外文部分
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11-39). Heidelberg, Germany: Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Alimisis, D. (2012). Robotics in education & education in robotics: Shifting focus from technology to pedagogy. In Proceedings of the 3rd International Conference on Robotics in Education (pp. 7-14).
Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6(1), 63-71.
Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019). A Systematic Review of Studies on Educational Robotics. Journal of Pre-College Engineering Education Research (J-PEER), 9(2), 2.
Arís, N., & Orcos, L. (2019). Educational robotics in the stage of secondary education: empirical study on motivation and STEM skills. Education Sciences, 9(2), 73. doi: 10.3390/educsci9020073
Ariza, D. V., Palacio, A. M., Aragón, I. P., Logreira, E. A., Pulido, C. M., & Mckinley, J. R. (2017). Application of color sensor programming with LEGO-Mindstorms NXT 2.0 to recreate a simplistic plague detection scenario. Scientia et Technica, 22(3), 268-272.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94.
Barak, M., & Zadok, Y. (2009). Robotics projects and learning concepts in science, technology and problem solving. International Journal of Technology and Design Education, 19(3), 289-307.
Benitti, F. B. V. (2012). Exploring the educational potential of robotics in schools: A systematic review. Computers & Education, 58(3), 978-988.
Cheng, Y. W., Sun, P. C., & Chen, N. S. (2018). The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers and instructors. Computers & Education, 126, 399-416. doi: 10.1016/j.compedu.2018.07.020
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press.
Crawley III, F. E. (1990). Intentions of science teachers to use investigative teaching methods: A test of the theory of planned behavior. Journal of Research in Science Teaching, 27(7), 685-697.
Dijkstra, T. K., & Henseler, J. (2015a). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81(1), 10-23.
Dijkstra, T. K., & Henseler, J. (2015b). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297-316.
Duran, M., Höft, M., Lawson, D. B., Medjahed, B., & Orady, E. A. (2014). Urban high school students’ IT/STEM learning: Findings from a collaborative inquiry-and design-based afterschool program. Journal of Science Education and Technology, 23(1), 116-137.
Eguchi, A. (2016). Educational robotics as a learning tool for promoting rich environments for active learning (REALs). In Human-computer interaction: Concepts, methodologies, tools, and applications (pp. 740-767). IGI Global.
Fishbein, M., & Ajzen, I. (1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Journal of Business Venturing, 5, 177-189.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1), 59-109.
Gialamas, V., & Nikolopoulou, K. (2010). In-service and pre-service early childhood teachers’ views and intentions about ICT use in early childhood settings: A comparative study. Computers & Education, 55(1), 333-341.
Gomoll, A., Hmelo-Silver, C. E., Šabanović, S., & Francisco, M. (2016). Dragons, ladybugs, and softballs: Girls’ STEM engagement with human-centered robotics. Journal of Science Education and Technology, 25(6), 899-914.
Goode, J., & Margolis, J. (2011). Exploring computer science: A case study of school reform. ACM Transactions on Computing Education (TOCE), 11(2), 1-16.
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles, LA: Sage publications.
Hair Jr, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. Los Angeles, LA: Sage publications.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis (Vol. 5, No. 3, pp. 207-219). Upper Saddle River, NJ: Prentice hall.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Jaipal-Jamani, K., & Angeli, C. (2017). Effect of robotics on elementary preservice teachers’ self-efficacy, science learning, and computational thinking. Journal of Science Education and Technology, 26(2), 175-192.
Johnson, J. (2003). Children, robotics, and education. Artificial Life and Robotics, 7(1-2), 16-21.
Jung, S. E., & Won, E. S. (2018). Systematic review of research trends in robotics education for young children. Sustainability, 10(4), 905. doi: 10.3390/su10040905
Kandlhofer, M., & Steinbauer, G. (2016). Evaluating the impact of educational robotics on pupils’ technical-and social-skills and science related attitudes. Robotics and Autonomous Systems, 75, 679-685.
Keren, G., & Fridin, M. (2014). Kindergarten Social Assistive Robot (KindSAR) for children’s geometric thinking and metacognitive development in preschool education: A pilot study. Computers in Human Behavior, 35, 400-412. doi: 10.1016/j.chb.2014.03.009
Kim, C., Kim, D., Yuan, J., Hill, R. B., Doshi, P., & Thai, C. N. (2015). Robotics to promote elementary education pre-service teachers' STEM engagement, learning, and teaching. Computers & Education, 91, 14-31.
Kim, C., Yuan, J., Kim, D., Doshi, P., Thai, C. N., Hill, R. B., & Melias, E. (2019). Studying the Usability of an Intervention to Promote Teachers’ Use of Robotics in STEM Education. Journal of Educational Computing Research, 56(8), 1179-1212.
Knop, L., Ziaeefard, S., Ribeiro, G. A., Page, B. R., Ficanha, E., Miller, M. H., ... & Mahmoudian, N. (2017). A human-interactive robotic program for middle school stem education. In 2017 IEEE Frontiers in Education Conference (FIE) (pp. 1-7). IEEE.
Kucuk, S., & Sisman, B. (2017). Behavioral patterns of elementary students and teachers in one-to-one robotics instruction. Computers & Education, 111, 31-43.
Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25(6), 860-876.
Li, C., Kam, W. K. K., & Zhang, M. (2019). Physical Education Teachers’ Behaviors and Intentions of Integrating STEM Education in Teaching. The Physical Educator, 76(4), 1806-1101. doi: 10.18666/TPE-2019-V76-I4-9104
Lin, K. Y., & Williams, P. J. (2016). Taiwanese preservice teachers’ science, technology, engineering, and mathematics teaching intention. International Journal of Science and Mathematics Education, 14(6), 1021-1036.
Lindh, J., & Holgersson, T. (2007). Does lego training stimulate pupils’ ability to solve logical problems?. Computers & Education, 49(4), 1097-1111.
Master, A., Cheryan, S., Moscatelli, A., & Meltzoff, A. N. (2017). Programming experience promotes higher STEM motivation among first-grade girls. Journal of experimental child psychology, 160, 92-106.
Mataric, M. J., Koenig, N. P., & Feil-Seifer, D. (2007). Materials for Enabling Hands-On Robotics and STEM Education. In AAAI spring symposium: Semantic scientific knowledge integration (pp. 99-102).
Melchior, A., Cohen, F., Cutter, T., Leavitt, T., & Manchester, N. H. (2005). More than robots: An evaluation of the first robotics competition participant and institutional impacts. Heller School for Social Policy and Management, Brandeis University.
Melchior, A., Cutter, T., & Cohen, F. (2004). Evaluation of FIRST LEGO league. Waltham, MA: Center for Youth and Communities, Heller Graduate School, Brandeis University.
Menekse, M., Higashi, R., Schunn, C. D., & Baehr, E. (2017). The role of robotics teams’ collaboration quality on team performance in a robotics tournament. Journal of Engineering Education, 106(4), 564-584.
Misirli, A., & Komis, V. (2014). Robotics and programming concepts in Early Childhood Education: A conceptual framework for designing educational scenarios. In Research on e-Learning and ICT in Education (pp. 99-118). Springer, New York, NY.
Mubin, O., Stevens, C. J., Shahid, S., Al Mahmud, A., & Dong, J. J. (2013). A review of the applicability of robots in education. Journal of Technology in Education and Learning, 1(209-0015), 13. doi: 10.2316/Journal.209.2013.1.209-0015
Nugent, G., Barker, B., Grandgenett, N., & Welch, G. (2016). Robotics camps, clubs, and competitions: Results from a US robotics project. Robotics and Autonomous Systems, 75, 686-691.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw- Hill.
Pirouz, D. M. (2006). An overview of partial least squares. Available at SSRN 1631359. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1631359
Pittí, K., Curto, B., Moreno, V., & Rodríguez, M. J. (2013). Resources and features of robotics learning environments (RLEs) in Spain and Latin America. In Proceedings of the First International Conference on Technological Ecosystem for Enhancing Multiculturality (pp. 315-322).
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's Comments: A Critical Look at the Use of PLS-SEM in" MIS Quarterly". MIS Quarterly,36(1), 3-14.
Sadaf, A., Newby, T. J., & Ertmer, P. A. (2012). Exploring pre-service teachers' beliefs about using Web 2.0 technologies in K-12 classroom. Computers & Education, 59(3), 937-945.
Sadaf, A., Newby, T. J., & Ertmer, P. A. (2016). An investigation of the factors that influence preservice teachers’ intentions and integration of Web 2.0 tools. Educational Technology Research and Development, 64(1), 37-64.
Salleh, S. (2016). Examining the influence of teachers' beliefs towards technology integration in classroom. The International Journal of Information and Learning Technology, 33(1), 17-35.
Somyürek, S. (2015). An effective educational tool: construction kits for fun and meaningful learning. International Journal of Technology and Design Education, 25(1), 25-41.
Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279-282.
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967.
Taherdoost, H., & Masrom, M. (2009). An examination of smart card technology acceptance using adoption model. Paper presented at the Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces , Dubrovnik, Croatia.
Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education, 57(2), 1645-1653.
Teo, T., & Van Schaik, P. (2012). Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models. International Journal of Human-Computer Interaction, 28(3), 178-188.
Teo, T., Zhou, M., & Noyes, J. (2016). Teachers and technology: development of an extended theory of planned behavior. Educational Technology Research and Development, 64(6), 1033-1052.
Valtonen, T., Kukkonen, J., Kontkanen, S., Sormunen, K., Dillon, P., & Sointu, E. (2015). The impact of authentic learning experiences with ICT on pre-service teachers' intentions to use ICT for teaching and learning. Computers & Education, 81, 49-58.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Williams, D. C., Ma, Y., Prejean, L., Ford, M. J., & Lai, G. (2007). Acquisition of physics content knowledge and scientific inquiry skills in a robotics summer camp. Journal of Research on Technology in Education, 40(2), 201-216.
Xia, L., & Zhong, B. (2018). A systematic review on teaching and learning robotics content knowledge in K-12. Computers & Education, 127, 267-282. doi: 10.1016/j.compedu.2018.09.007
Zaldivar, D., Cuevas, E., Pérez-Cisneros, M. A., Sossa, J. H., Rodríguez, J. G., & Palafox, E. O. (2013). An educational fuzzy-based control platform using LEGO robots. International Journal of Electrical Engineering Education, 50(2), 157-171.
Zhong, B., & Xia, L. (2020). A Systematic Review on Exploring the Potential of Educational Robotics in Mathematics Education. International Journal of Science and Mathematics Education, 18(1), 79-101.
Zint, M. (2002). Comparing three attitude‐behavior theories for predicting science teachers' intentions. Journal of Research in Science Teaching, 39(9), 819-844.