科學、科技、工程及數學(STEM)教育在培養學生透過跨學科整合的解決問題能力上扮演著關鍵角色，這是21世紀學習者被視為重要的能力。然而，STEM教育向學生為中心學習模式的轉移展現出傳統評估方法，如自我報告和觀察法的劣勢。上述的方法往往存在著偏差，且需要大量的人力時間成本來收集數據，未能準確地辨識出解決問題和跨學科整合的複雜性。儘管深度學習和電腦視覺技術的進步有望提供應對這些挑戰的解決方案，但現有的研究大多集中實驗導向研究工具的開發，而不是作為能夠應對實際STEM教學場域的解決方案。為解決上述的研究限制，本研究開發了CV-STEM，一種基於人工智慧的創新評估工具，其利用先進的深度學習和電腦視覺技術，包括You Only Look Once v4 (YOLOv4) 物體偵測，透過客觀且自動化分析學習者手部與STEM學習材料的互動來即時評估參與度。此外，本研究更額外探索了CV-STEM的兩個擴展應用對學生的幫助，即CV-STEM-TI(及時干預)和CV-STEM-AL(適應性學習)。CV-STEM-TI旨在為教育工作者提供學生學習狀態的即時觀察，使得即時的干預、反饋和指導成為可能。另一方面，CV-STEM-AL則是旨在根據個別學生的需求、偏好和能力來調整內容和進度，從而促進適應性和個性化學習。本研究採用了廣泛的評估框架來評估CV-STEM及其擴展在測量學生參與度的準確性、可靠性和有效性，以及它們在提高教學效率和學生學習成果方面的有效性。結果表明，整合CV-STEM及其擴展應用可以顯著革新傳統評估方法，創建一個動態、互動和以學生為中心的學習環境，支持融合和基於探究的STEM教育的新典範。儘管這些發現令人鼓舞，但研究也承認限制，包括樣本大小、涵蓋STEM領域範圍有限和缺乏長期數據。未來研究的方向可以再進一步開發和全面評估CV-STEM及其擴展，旨在概括它們在不同STEM教育情境中的適用性。

Science, Technology, Engineering, and Mathematics (STEM) education is pivotal in cultivating problem-solving skills among students through interdisciplinary integration, a competency deemed vital for learners in the 21st century. However, the shift towards a student-centered learning paradigm in STEM education has exposed the shortcomings of traditional assessment methods, such as self-reporting and observation. These methods are often biased and require extensive labor for data collection, failing to accurately capture the complexity of problem-solving and interdisciplinary integration. Although advancements in deep learning and computer vision technologies offer promising solutions to these challenges, much of the existing research has focused on their development as research tools rather than as practical solutions for real-world STEM education settings. In this context, this study introduces CV-STEM, an innovative AI-based assessment tool that utilizes advanced deep learning and computer vision technologies, including You Only Look Once v4 (YOLOv4) object detection, to objectively and automatically assess student engagement in real-time through analysis of hand interactions with STEM learning materials. Furthermore, this research explores two extensions of CV-STEM: CV-STEM-TI (Timely Intervention) and CV-STEM-AL (Adaptive Learning). CV-STEM-TI is designed to provide educators with real-time insights into students' learning conditions, enabling timely interventions, feedback, and guidance. Conversely, CV-STEM-AL aims to tailor content and pacing to meet the needs, preferences, and abilities of individual students, thereby promoting adaptive and personalized learning. An extensive evaluation framework was employed to assess the accuracy, reliability, and validity of CV-STEM and its extensions in measuring student engagement, as well as their effectiveness in enhancing teaching efficiency and student learning outcomes. The results indicate that integrating CV-STEM and its extensions can significantly revolutionize traditional assessment methods, creating a dynamic, interactive, and student-centered learning environment that supports the emerging paradigms of integrated and inquiry-based STEM education. Despite these encouraging findings, the study acknowledges limitations, including a small sample size, a limited range of STEM domains covered, and the absence of longitudinal data. It suggests future directions for further development and comprehensive evaluation of CV-STEM and its extensions, with the aim of generalizing their applicability across diverse STEM education contexts.

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