自閉症學生數學數位學習之成效提升，情緒扮演重要角色，為避免其於數學數位學習過程，因情緒問題而導致學習失效，已逐漸成為特殊教育與資訊通訊技術跨領域結合之重要議題。本文針對數學數位學習環境，因缺乏感知自閉症學生情緒，以致無法情感適性化學習，提出一自閉症學生數學數位學習之適性化情緒調適模式，並發展高功能自閉症學生情緒分類機制，透過真實數學數位學習內容，分別誘發高功能自閉症學生平靜(Baseline)、高興(Happy)、焦慮(Anxious)及生氣(Angry)等四種情緒，且同時記錄其生理訊號與臉部表情變化，同步萃取58個情緒特徵；再經由資訊增益與單因子變異數兩種特徵選取方法，保留29個與情緒相關之特徵；接著分別使用支援向量機(SVM)、最鄰近分類(KNN)及分類迴歸樹(CART)三種分類模型，各別嵌入於拔靴集成(Bootstrap)與調適性多模增進(Adaboosting)兩種集成分類模型，以進行四種情緒之分類，經實驗與比較後，由單因子變異數所選用之情緒特徵集合，配合使用SVM嵌入拔靴集成情緒分類模型，其整體情緒分類辨識率可達81%。該情緒分類機制於未來可支援情緒調適模式之運行，協助自閉症學生於學習過程進行情緒調適，提高數學數位學習成效。

Emotional problems of students with autism play an important role in their learning in mathematics e-learning environments. This study proposed an emotional adjustment model for students with high-functioning autism in mathematics e-learning with an emotion classification mechanism. The present paper reports the development of the emotion classification mechanism through evoking autistic students’ emotions in a mathematical e-learning environment and recording changes in their physiological signals and facial expressions. A total of fifty-eight measures were obtained from an experiment, and twenty-nine measures were further extracted from one-way ANOVA and information gain (IG) methodology. Support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were combined with bootstrap aggregating (Bagging) individually and used to classify four emotional categories: calm, happy, anxious, and angry from the twenty-nine features. The accuracy rate of the SVM ensembles using Bagging to classify emotions reached the highest (81%). The emotion classification mechanism developed in the present report could support the emotional adjustment model which aims at classifying and adjusting autistic students’ emotions during mathematics learning and enhancing their learning effectiveness.

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