研究生: |
陳妍妏 Yen-Wen Chen |
---|---|
論文名稱: |
國小教師對於教育機器人入班之科技接受度及影響使用意圖的因素 Elementary school teachers' technological acceptance and actual use intention of educational robots. |
指導教授: |
陳素芬
Su-Fen Chen |
口試委員: |
曾厚強
林志鴻 陳素芬 |
學位類別: |
碩士 Master |
系所名稱: |
人文社會學院 - 數位學習與教育研究所 Graduate Institute of Digital Learning and Education |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 65 |
中文關鍵詞: | 教育機器人 、科技接受模型 、科技接受和使用統一理論 、教師培訓 、跨領域課程 |
外文關鍵詞: | educational robot, technology acceptance model, Unified Theory of Acceptance and Use of Technology model, teacher training, interdisciplinary education |
相關次數: | 點閱:414 下載:2 |
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 |
本研究以科技接受和使用統一理論(UTAUT)為基礎進行混合研究,目的是想探討影響國小教師使用教育機器人入班的主要因素。研究一收到來自臺灣公立和私立國小教師的151份有效問卷回覆,透過路徑分析後發現,影響使用意圖的主要因素為態度和促進條件,態度受感知有用性和適應性影響,降低焦慮將有助於提升感知適應性並間接提升使用意願。研究二另針對臺灣都市30位及鄉鎮23位國小現職教師,分別對其進行2小時AI教育機器人介紹與實作培訓課程,最終回收有效問卷40份。實驗結果表明,內建跨領域教材的AI教育機器人,確實可有效降低教師對於新科技的焦慮心理,系統介面操作簡易且教師可彈性修改教學內容等高度的適應性和有用性,更提高了受試者的使用意圖。尤其鄉鎮教師對於使用教育機器人的正面態度高於都市教師。
然實驗後一學期的追蹤訪談發現,實務上因為學校課程時間安排與設備支援等外在因素,及教師對於教育機器人導入後之課堂秩序維護、替學生程式除錯等教學工作仍感到不安,因此實際導入情況並不佳。因此建議若要推動AI教育機器人入班,除教務系統的強力支持外,還需規劃更長時間的教師培訓,並搭配不插電教材等資源,協助教師內化知識並提升對於教育機器人的熟悉度,方能確實推動。
This project was a mixed method research based on the Unified Theory of Acceptance and Use of Technology (UTAUT), with the aim of exploring the main factors influencing the usage of educational robots by elementary school teachers in class. Two studies were included. Study 1 found that, through a path analysis, the main factors affecting the teachers’ intention of use were their attitude and the facilitating conditions, with 151 valid survey responses from public and private elementary school teachers in Taiwan. In Study 2, 30 urban and 23 rural elementary school teachers in Taiwan were given a 2-hour introductory training session on the AI educational robots. Among them, 40 valid responses were received. The experimental results showed that the AI educational robots with built-in interdisciplinary teaching materials were effective in reducing teachers' anxiety of new technologies. The high adaptability and functionality of the system's easy-to-use interface, and the flexibility in modifying teaching contents increased teachers’ intentions of use. In particular, teachers in the rural areas had a more positive attitude toward the use of educational robots than teachers in the urban areas. However, follow-up interviews in the post-experimental semester revealed that the actual usage was poor due to external factors such as school scheduling issues and insufficient equipment support, as well as teachers' anxiety about maintaining order in the classroom with the introduction of educational robots and debugging for students. Therefore, it is recommended that in order to promote the adoption of AI educational robots into classrooms, in addition to strong support from the school’s teaching and learning system, longer teacher training should be planned, and extra resources such as unplugged teaching materials should be provided to help teachers internalize the knowledge and enhance their familiarity with the educational robots.
一、書籍
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood, NJ:Prentice-Hall.
Fishbein, M. & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, Mass.; Don Mills, Ontario: Addison-Wesley Pub. Co.
二、期刊文章
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), 1-9. https://doi.org/10.1126/scirobotics.aat5954
Broadbent, E., Feerst, D. A., Lee, S. H., Robinson, H., Albo-Canals, J., Ahn, H. S., & MacDonald, B. A. (2018). How could companion robots be useful in rural schools? International Journal of Social Robotics, 10(3), 295–307. https://doi.org/10.1007/s12369-017-0460-5
Buabeng-Andoh, C. (2012). Factors influencing teachers’ adoption and integration of information and communication technology into teaching: A review of the literature. International Journal of Education and Development using Information and Communication Technology (IJEDICT), 8(1), 136-155.
Ceha, J., Law, E., Kulic, D., Oudeyer, P. Y., & Roy, D. (2021). Identifying functions and behaviours of social robots for in-class learning activities: Teachers' perspective. International Journal of Social Robotics, 14, 747–761. https://doi.org/10.1007/s12369-021-00820-7
Chenga, Y. W., Sunb, P. C., & Chenc, N. (2018). The essential applications of educational robot: Requirement analysis from the perspectives of experts, researchers and instructors. Computers & Education, 126(2018), 399-416. https://doi.org/10.1016/j.compedu.2018.07.020
Chevalier, M., Riedo, F., & Mondada, F. (2016). Pedagogical uses of thymio II: How do teachers perceive educational robots in formal education? IEEE Robotics & Automation Magazine, 23(2), 1-1. https://doi.org/10.1109/MRA.2016.2535080
Chin, K. Y., Hong, Z. W., & Chen, Y. L. (2014). Impact of using an educational robot-based learning system on students’ motivation in elementary education. IEEE Transactions on learning technologies, 7(4), 333-345. https://doi.org/10.1109/TLT.2014.2346756
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319—339. https://doi.org/10.2307/249008
de Graaf, M. M. A., & Allouch, S. B. (2013). Exploring influencing variables for the acceptance of social robots. Robotics and Autonomous Systems, 61(12), 1476-1486. https://doi.org/10.1016/j.robot.2013.07.007
Ekstrom, S., & Pareto, L. (2022). The dual role of humanoid robots in education: As didactic tools and social actors. Education and Information Technologies. Published online: 03 June 2022,. https://doi.org/10.1007/s10639-022-11132-2
EI-Deghaidy, H., & Mansour N. (2015). Science teachers’ perceptions of STEM education: Possibilities and challenges. International Journal of Learning and Teaching, 1(1), 51-54. https://doi.org/10.18178/ijlt.1.1.51-54
El-Hamamsy, L., Chessel-Lazzarotto, F., Bruno, B., Roy, D., Cahlikova, T., Chevalier, M., Parriaux, G., Pellet, J. P., Lanarès, J., Zufferey, J. D., & Mondada, F. (2020). A computer science and robotics integration model for primary school: Evaluation of a large-scale in-service K-4 teacher-training program. Education and Information Technologies, 26, 2445–2475. https://doi.org/10.1007/s10639-020-10355-5
Fridin, M., & Belokopytov, M. (2014). Acceptance of socially assistive humanoid robot by preschool and elementary school teachers. Computers in Human Behavior, 33 (2014), 23–31. https://doi.org/10.1016/j.chb.2013.12.016
Guggemos, J., Seufert, S., & Sonderegger, S. (2020). Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT. British Journal of Educational Technology, 51(5), 1864-1883. https://doi.org/10.1111/bjet.13006.
Heerink, M., Kröse, B., Evers, V., & Wielinga, B. (2010). Assessing acceptance of assistive social agent technology by older adults: The Almere model. International Journal of Social Robotics, 2(4), 361–375. https://doi.org/10.1007/s12369-010-0068-5
Kert, S. B., Erkoç, M. F., & Yeni, S. (2020). The effect of robotics on six graders’ academic achievement, computational thinking skills and conceptual knowledge levels. Thinking Skills and Creativity, 38 (2020), 100714. https://doi.org/10.1016/j.tsc.2020.100714
Kopcha, T. J., Ocak, C., & Qian, Y. (2020). Analyzing children’s computational thinking through embodied interaction with technology: A multimodal perspective. Education Technology Research Development. https://doi.org/10.1007/s11423-020-09832-y
Malerba, D., Appice, A., Buono, P., Castellano, G., De Carolis, B., De Gemmis, M., Polignano, M., Rossano, V., & Rudd, L. M. (2019). Advanced programming of intelligent social robots. Journal of e-Learning and Knowledge Society, 15(2), 13-26. https://doi.org/10.20368/1971-8829/1611
Peterson, A. R. (1994). A meta-analysis of Cronbach's coefficient alpha. Journal of Consumer Research, 21(2), 381-391. https://doi.org/10.1086/209405.
Sapounidis, T., Demetriadis, S., & Stamelos, I. (2015). Evaluating children performance with graphical and tangible robot programming tools. Pers Ubiquit Comput, 19: 225–237. https://doi.org/10.1007/s00779-014-0774-3
Serholt, S., Barendregt, W., Leite, I., Hastie, H., Jones, A., Paiva, A., Vasalou, A., & Castellano, G. (2014). Teachers’ views on the use of empathic robotic tutors in the classroom. The 23rd IEEE International Symposium on Robot and Human Interactive Communication, ( August 25-29). Edinburgh, Scotland, UK. https://doi.org/10.1109/ROMAN.2014.6926376
Setyarini, M. C. (2015). Understanding teachers’ computer anxiety. English Teaching Journal ETERNAL , 6(1), 74–87. https://doi.org/10.26877/eternal.v6i1.2297
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649. https://doi.org/10.3102/0034654308325896
Sungur Gül, K. & Marulcu, İ. (2014). Investigation of in service and pre-service science teachers’ perspectives about engineering-design as an instructional method and legos as an instructional material. International Periodical for the Languages, Literature and History of Turkish or Turkic, 9(2), 761-786. https://doi.org/10.7827/TurkishStudies.6561
Venkatesh, V., Morris, M., Davis, G. B., Fred, D., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
Wang, H., Moore, T. J., Roehrig, G. H., & Park, M. S. (2011). STEM integration: Teacher perceptions and practice. Journal of Pre-College Engineering Education Research (J-PEER), 1(2), Article 2. https://doi.org/10.5703/1288284314636
Xia, Y., & LeTendre, G. (2021). Robots for future classrooms: A cross-cultural validation study of "Negative Attitudes Toward Robots Scale" in the US context. International Journal of Social Robotics, 13, 703–714. https://doi.org/10.1007/s12369-020-00669-2
三、網站
教育部統計處
https://depart.moe.edu.tw/ED4500/cp.aspx?n=1AC243AF6EF5E5DD&s=EDC4A4E717ED32CF
四、軟體
SmartPLS: Ringle, C. M., Wende, S., and Becker, J.-M. (2022). "SmartPLS 4." Oststeinbek: SmartPLS GmbH, http://www.smartpls.com.