物聯網時代的來臨，數以萬計連網裝置所產生的龐大串流資料需要電腦的即時分析，機器學習也因應此趨勢更受關注。然而，既有機器學習模型往往忽略資料語意內容本身對模型的影響，因為資料語意內容不完整或敏感都會導致機器學習的結果出現偏差。此外，串流資料模型無法得知其真實標籤，所以先前研究也忽視學習後之成效即時檢視，導致無法對後續或未來的學習能加以正面調整，且資料分佈本身也容易潛藏概念飄移(concept drift)。所以本研究針對具有概念飄移的即時串流資料分析提出一個基於人類教育學(Pedagogy)的自我調適(Self-regulation)學習框架以改善既有機器學習模型的缺陷。此框架具備習前預思(Forethought)、習間表現監督(Performance)以及習後反饋(Reflection)三個循環要素。其中，習前預思是指學習者在學習前清楚明白學習的目標為何，習間表現監督乃在學習過程當中，能自我即時監督並調整學習。最後習後反饋是指透過學習後能評量學習結果並更正學習方式。為了使機器具備上述三個要素，在習前預思部分，研究透過提出「習前目標導向的篩選技術」，透過維基百科(Wiki)搜尋欲訓練之資料內容是否與學習目標相牴觸。在習間表現監督部分，本研究透過提出「習中後設認知（Metacognition）即時監控之學習框架」，該框架允許使用者將所選定的機器學習模型強化具有後設認知的能力，透過學什麼(What to learn) 來選擇資料特徵、以如何學(How to learn) 來調整模型、以及透過何時學(When to learn)根據信心不足資料之累積以啟動模型更新，以因應概念飄移的模型調適。最後，習後反饋部分，本研究透過「習後專家模型更正與反饋技術」，針對低信心分類資料做進一步確認，並找出動態微粒(Dynamic particle) 輔助模型給予學習指導標籤，以突破串流資料無法得知真實標籤的限制。實驗結果顯示，本研究所提出的方法在有概念飄移的即時資料分析上，可以在物聯網無人為介入的環境下，讓準確率接近有人為介入的基準，甚至部分資料準確率也獲得提升，另外也初步確保輸入資料能透過語意層級的解讀，讓學習能依循所設定的目標進行。

For the upcoming IoT era, plethora of data needs to be processed on a real-time basis. However, most prior data streaming related researches ignored the semantic meaning of the input data, thus leading to unexpected or even unacceptable models. Such an issue may become worse under the circumstances of concept drift. Since data streaming does not have the ground-truth of the data, it becomes really challenging to reflect upon what has been learned for later continuously improving learning performance. To address above issues, this study incorporates the self-regulation learning from pedagogy. There are three elements in self regulation learning, including Forethought, Performance, and Self-reflection. Forethought refers to setting a goal in advance before learning. Performance refers to self monitoring and controlling how well people learn during learning. Self-reflection refers to how to correct mistakes after learning. To help a machine equip with the above abilities, this study proposes a goal-oriented filtering mechanism for the Forethought stage to exclude those input data not related to the learning goal. Next, the Performance stage is implemented as an on-line metacognition based learning framework, which can empower a selected model with the cognitive abilities under concept drift by the replaceable “What to learn,” “How to learn,” and “When to learn” components. Finally, we propose a post-learning expert-model-assisted correction for the self reflection stage, where we choose the dynamic particle algorithm to serve as an expert model to correct those low confident data under concept drift. Such a design is to reduce human intervention for IoT-enabled data streaming analytics. With the above enhancements, the experiment results of our enhanced model can be as good as those prior studies still needing human intervention. Some results even show slight improvement, meanwhile guaranteeing that the data astrayed from the goal will be exluded.

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