近年來STEAM教育的議題眾所矚目，越來越多的研究致力於STEAM教育以提升學習者未來的競爭力；然而，由於STEAM教育內容的多樣性，導致傳統以教師為中心的教學方法在STEM/STEAM教育上效果不佳；歸因於STEAM教育的實作學習活動內容，這使得動手作學習理論被廣泛地應用於STEAM教育中，並試圖在STEAM教育中找到最合適的教學方法。是故，如何在STEAM教育中有效評估動手作的動作並用以分析學習者的表現便成了教育研究中一項重要的課題。過往的研究指出，學習者在課堂中的表現與其後續發展有一定的關聯；而在STEAM教育中通常以自我報告或觀察法的方式量測表現，自我報告往往受限於學習者的自我意識導自結果有所偏差，並且往往無法有效地了解不同時間段學習者動作的變化；而觀察法則需耗費大量的時間與人力成本進行標記與編碼。因此，隨著人工智慧技術的興起，本研究結合人體姿態估算技術與物件偵測技術，提出一套自動化量測學習者在STEAM實作活動的系統架構，用以有效地識別動手作行為所衍伸的事件與動作，並合理地判斷學習者在STEAM動手作課程的學習表現。
本研究以Micro:bit避障自走車作為STEAM動手作活動並以此驗證所提出系統之準確性；研究結果表明，使用YOLOv4-Large針對STEAM動手作活動中常見的七項物件與工具進行辨識mAp可達96.1%；而使用改良版OpenPose亦可以準確地抓出學習者進行STEAM動手作活動時手部的關鍵點，經評估後可達90%以上的辨識準確度。最終，得力於上述二者良好的辨識結果，在STEAM動手作活動中得平均87%的動作辨識準確度。另一方面，本研究利用STEAM動手作活動之後測分數做為衡量學習成就之因子，並探討動作對於學習成就的影響，發現在STEAM動手作活動中操作相關的物件與程式編寫的次數越多者，其學習成就越高；相反地從事越多與STEAM動手作活動無關的動作者，其學習成就便越低，這相對地也說明學習者更積極地從事STEAM動手作學習活動會獲得更好的學習表現。因此，本研究所提出之系統架構除了在準確度上有所提升更可辨識出小組中個別成員的動作，為STEAM動手作活動的自動化量測提供全新的視野與藍圖，同時也為自動化量測的可行性作出相應的參考依據。

The STEAM is an educational concept of interdisciplinary curriculum integration, which involves that the knowledge and skills of science, technology, engineering, arts, and math courses. Countries all over the world are actively promoting STEAM education plans, as well as related education policies and teaching strategies. Influenced by the idea of learning by doing, STEAM education centered on hands-on learning has gradually been disseminated and promoted. However, previous research was generally used scales or questionnaires for quantitative analysis to evaluate the learning performances. That may be prone to be biased by people’s self-awareness. To measure performance of students who do the hands-on learning in STEAM activity automatically, this study propose a system that use YOLOv4 and OpenPose to recognize the learning materials/tools and find the participants’ hand keypoints, and then combining both of results to recognize students' action. Finally, based on the system output, it can help teachers to realize what participants need on time and what's the relationship between participants' action and learning achievement.

舒一修（民109）。手部姿勢搭配物件偵測於學生參與度之識別：以避障自走車為例（碩士論文）。國立成功大學，臺南市。
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