近年來，許多研究機構都認同物聯網和機器人在國家、社會和個人生活，特別是在教育方面的重要性。物聯網和機器人是智慧教室的必要條件，它們能創造最佳的學習環境。因此，本研究旨在將物聯網和機器人 (即Zenbo)應用到翻轉教室中。並在高互動多媒體課程中實現以學習者為中心的學習環境。此外，本研究的翻轉教室配合相互教學，包括總結、提問、澄清、預測 (SQCP)。我們為Zenbo機器人開發了一套智慧學習系統，包括谷歌教室、Zenbo應用程式、教室控制、QR簽到、人臉辨識簽到，應用於臺灣國立中央大學的課程中。
本研究通過Zenbo機器人和物聯網建構智慧學習環境。並分析了本研究提出之方法的有效性，包括學生對翻轉教室的智慧環境的交互性、有用性、使用意圖和易用性。本研究在兩個學期中進行了兩項實驗，包括前導實驗和正式實驗。在前導實驗中，學生在有Zenbo機器人和物聯網輔助的翻轉教室中學習，而在正式實驗中，學生在有Zenbo機器人和物聯網輔助且有更多智慧的功能、工具和應用的智慧環境中學習。
實驗結果表明，與前一種方法相比， 結合了Zenbo與更多的功能、工具和應用後，顯著改善了學生的學習環境和教師的教學方式。結果還表明，Zenbo機器人使用起來並不方便;然而，為Zenbo機器人開發的功能、工具和應用程式在翻轉教室中非常有用，學生們有繼續使用Zenbo機器人進行學習的意願。此外，Zenbo機器人還存在一些局限性，缺乏主動的、視覺化的程式設計語言，在嘈雜的環境下幾乎無法進行對話。在此基礎上，本研究提出了為智慧學習教室開發機器人的功能和應用的建議。相信此研究將幫助研究人員開發用在學習環境中的物聯網和機器人。

In recent years, many institutions have announced the significance of IoT and robot development for countries, societies, and individuals in almost all areas of life, especially in education. Applying IoT technology and assistant robots is necessary for a smart classroom to facilitate the best studying environment. Therefore, this study aims to implement IoT and robot techniques, namely Zenbo, to the innovative pedagogy, called a flipped classroom, to perform a learner-centred learning environment in the High Interaction in Multimedia course. Flipped classroom in this study coordinated with reciprocal teaching summarizing, questioning, clarifying, prediction (SQCP). A smart learning system was developed for the Zenbo robot, including google classroom, Zenbo application, classroom control, QR check-in, face recognition check-in, applying on this course at National Central University, Taiwan.
This study provides essential features for the smart learning environment in the flipped classroom via Zenbo robot and IoT technology. It investigates the effectiveness of the proposed approach, including students' perception toward interaction, usefulness, intention to use, and easy to use of the smart environment for the flipped classroom. Two experiments, including pilot experiments and regular experiments, were conducted during two semesters. In the first experiment, students learned in the flipped classroom supported by Zenbo robot and IoT, while in the second one, they learned in the smart environment supported by Zenbo robot and IoT with more smart functions, tools, and applications.
The experiment results indicate that in comparison with the former, the proposed approach with Zenbo integrated with more features, tools, and applications, significantly improved the students' learning environment, and teachers' teaching way. The results also show that the Zenbo robot is not easy to use; however, the functions, tools, and applications developed for the Zenbo robot are significantly useful in the flipped classroom, and students intend to use the Zenbo robot next time. Besides, the Zenbo robot has some limitations about lacking active, visual programming language, and low communication ability in noisy places. Based on the identified weaknesses, this research presents recommendations for developing another robot with more capability to make more functions and applications for the smart learning classroom. It is believed that this development would give researchers more perfect results to build IoT and robots in the learning environment.

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