在知識經濟的時代背景下，STEM教育的推廣與普及已獲得全球的高度重視。儘管如此，現今多數的傳統教育體制仍然偏重於以教師為中心的理論授課，在知識的實際應用與STEM學科之間的整合上卻相對缺乏。為了改善此現象，許多教育工作者主張應該將創客文化融入至STEM教育中。為此，本研究試圖探討如何提升學生在STEM創客活動中的學習體驗、學習成效、學習動機與參與度，以此作為重振STEM教育的一種方式。本研究在STEM創客活動中採用精準教育的方法，以期望提升學生的學習體驗與成效，透過多模態學習分析 (MMLA)的技術來觀察學生在STEM創客活動中的學習行為，以此作為精準教育的實踐依據，由於多模態數據涉及大量的資料處理，為克服傳統中央化計算可能出現的效能問題，本研究選擇使用邊緣運算技術來開發多模態學習分析系統 (MMLAS)。此外，本研究也透過在STEM創客活動中融入反思學習的策略，以期望改善學生的學習動機與參與度。
本研究首先驗證了「MMLAS」系統的在自動學生行為編碼的準確性，並且在系統的學習指標編碼與專家編碼具有高度一致性 (Cohen's Kappa係數為0.843)。「MMLAS」系統實現了精準教育的診斷、預防、治療三種功能，透過「診斷」了解學生的學習行為，對於消極參與的學生及時關注他們的學習狀況 (預防)，並根據診斷結果提供每位學生適合的教材 (治療)。本研究透過實驗證實了精準教育策略對於提升學生的學習體驗與成效是有確實效果的。此外，在反思學習的部分，本研究的實驗結果也顯示反思學習策略能有效提升學生的內在學習動機行為參與、認知參與及情感參與，但對外在學習動機的影響相對較小。綜上所述，本研究對於未來在STEM教育實踐中，更有效地利用精準教育與反思學習策略有重要的啟示作用。

In the age of the knowledge economy, there is burgeoning global interest in the promotion and diffusion of STEM education. However, many of today's conventional educational systems still predominantly focus on teacher-centric theoretical instruction. This approach often overlooks the practical application of knowledge and interdisciplinary integration within the STEM fields. Recognizing this gap, numerous educators are championing the incorporation of maker culture into STEM curriculum. Against this context, the present study goes into methodologies that could amplify students' learning experiences, outcomes, enthusiasm, and engagement in STEM maker activities, with the overarching goal of rejuvenating STEM education. A salient approach explored in this research is the adoption of precision education techniques in STEM maker activities, designed to elevate students' learning experiences and achievements. Leveraging the Multimodal Learning Analysis (MMLA) technique, the study observes students' learning behaviors during maker activities, providing a foundational base for the application of precision education. Considering the comprehensive data processing required for multi-modal data and aiming to circumvent the potential drawbacks of traditional centralized computation, this research adopts edge computing technology to craft the MMLAS system. In a bid to further enrich student engagement, the study also interweaves reflective learning strategies into the STEM maker activities, aiming to boost students’ motivation and active involvement.

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