近年來由於主動思考及動手實作的實踐能力越來越被重視，使得創客教育在世界各國開始蔚為風潮；與傳統教育被動接受知識的方式相比，創客教育的學習方式對於學生影響是非常正面且有效的。與此同時，隨之而來的即是如何有效的評估與分析學生於課堂表現的議題，其中，過往研究指出學生後續的成功發展與其在課堂的參與有相關，因此在創客教育上經常以自我報告的方式量測學生參與度，而隨著深度學習與影像辨識技術的快速發展，這使得自動量測的技術變得可行。因此，本研究旨在提出一個使用自動量測技術於學生參與度識別之新架構，本架構將使用OpenPose網路與YOLO網路，結合本研究自行開發之行為辨識演算法對手部關鍵點與創客物品進行偵測，並透過行為辨識演算法辨識學生在課堂上與創客物品之互動情形進行紀錄與統計。
本研究以避障自走車作為創客活動，並招募30位自願參與者完成所有實驗任務；我們將所有學生的創作過程進行紀錄，並使用了這些創客行為影片對OpenPose進行了測試。研究結果顯示，單手辨識度為92%，雙手辨識度為66%；此外，在物件偵測問題中七項創客物件的辨識度mAp達99.5%，自行開發之行為辨識演算法在七種創客行為的辨識上可達到平均85%的準確率。另一方面，儘管在創客參與評估中與學習參與與情緒不滿量表(Engagement Versus Disaffection With Learning, EvsD)中行為總分無顯著相關；然本研究進一步運用總任務時間作為參與度量測因子，則可得到創客行為次數與參與時間之可靠關係。因此，從研究結果顯示，本研究所提出之架構，即OpenPose網路、YOLO網路以及行為辨識演算法，為創客教育學生參與度提供全新的視野與藍圖，同時，也為創客行為自動化辨識的可行性作出相應的參考依據。

In recent years, the practical ability of active thinking and hands-on practice has been paid more and more attention, making maker education a popular trend in countries all over the world. Past studies have pointed out that the subsequent successful development of students is related to their participation in the classroom. With the rapid development of deep learning and image recognition technology, this makes automatic measurement technology feasible on measure student engagement. Therefore, this research aims to propose a new architecture that uses automatic measurement technology for student engagement recognition. This architecture will use OpenPose network and YOLO network, combined with the behavior recognition algorithm developed by this research. Detecting maker activity items, and using behavior recognition algorithms to identify students’ interactions with maker activity items in class.
In this study, obstacle avoidance car were used as a maker activity, and 30 volunteer participants were recruited to complete all experimental tasks; we recorded the creative process of all students, and used these maker behavior videos to test OpenPose. The research results show that the recognition rate of single hand is 92%, and the recognition rate of pair of hands is 66%; in addition, the recognition rate of seven maker objects in the object detection problem is 99.5%, the self-developed behavior recognition algorithm can achieve an average accuracy of 85% in the recognition of seven kinds of maker behaviors.

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