Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98255
標題: 建立影片式學習情緒辨識與轉移模型之研究
The Study on Developing a Learning Emotion Recognition and Emotion Transferring Model during Video-based Programming Learning
作者: 林裕芳
Yu-Fang Lin
關鍵字: 影片式教學
學習情緒
情緒動態轉移
先備知識
學習成效
Video-Based Learning
Learning Emotions
Emotion Transfer
Prior Knowledge
Learning Outcomes
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摘要: 傳統教室教學和線上學習,都需要評量來確認學生學習的有效性。常用的評量方法有總結性評量和形成性評量。透過觀察學生的臉部表情,可以在教學過程中提供教師回饋,達到形成性評量。 文獻指出在學習環境中使用認知情緒相較於基本情緒更能反應學習過程中之情緒。挫折、困惑、無聊、喜悅、投入以及驚訝等六種認知情緒對於學習具有相關性和影響性。因此本論文將上述六種情緒和中性情緒重新命名為「學習情緒」。 本論文以臉部表情偵測學生學習過程中的情緒並轉換成情緒轉移路徑,透過情緒轉移路徑評估學生的學習成效並回饋給教師,判斷學生是否理解上課內容來調整教學內容和策略。 首先,建立學習情緒辨識模型,使用學習情緒資料庫為模型的訓練資料,選用支援向量機作為分類器,分類器的輸入為臉部特徵點形成之特徵向量,經過基因演算法特徵選取以及支援向量機參數最佳化的處理,本模型平均準確率達85.84%。 其次,蒐集並標記影片式教學實驗中學生的臉部表情標記學生情緒建立轉移路徑模型,歸納分析高、低先備知識和學習成效好與不好之不同學習情緒轉移路徑,研究結果顯示: (1) 在影片式教學中,學習者發生困惑到投入、無聊到投入、投入到無聊、投入到困惑具有顯著性。 (2) 高先備知識且學習成效好,發生無聊到投入、投入到困惑和投入到無聊具有顯著性。 (3) 高先備知識且學習成效不好,發生無聊到投入和投入到無聊具有顯著性。 (4) 低先備知識且學習成效好,發生無聊到投入和投入到無聊具有顯著性。 (5) 低先備知識且學習成效不好,發生無聊到投入和投入到無聊具有顯著性。 最後,基於上述結果,建立學習成效評估模型透過不同先備知識、不同學習成效情況下之不同學習情緒轉移路徑,得到不同教學建議,教學建議可以回饋給教學者即時修改教學策略,達到因材施教的目的。
Traditional classroom teaching and online learning require evaluation to confirm the effectiveness of student learning. Usually used evaluate methods are summative evaluation and formative evaluation. By observing the facial expressions of students, teacher feedback can be provided during the teaching process to implement formative evaluation. The study mentioned that the use of cognitive affective emotions is more significant than basic emotions in learning. Six kinds of cognitive affective emotions are frustration, confusion, boredom, delight, flow and surprise. Therefore, this thesis renames the above six emotions and neutral emotions as 'learning emotions'. This study uses facial expressions to detect students' emotions in learning process and convert them into emotional transfer paths. The emotional transfer path is used to evaluate the students' learning outcomes and feedback to the teacher. It is judged whether the students understand the content of the class and adjust the teaching strategies. First, we build the learning emotion recognition model. The learning emotions database is used as the training data of the model. The classifier is support vector machine. The input of the classifier is the feature values by the facial feature points, and the feature selection is use genetic algorithm. The average accuracy of the model is 85.84%. Secondly, collect and mark the students' facial expression lable in the video based learning, and build the transfer path model for students' emotions. Inductive of high and low prior knowledge and good or poor learning outcomes. The research results show that: (1) In Video-based learning, confusion to flow, boredom to flow, flow to boredom and flow to confusion were significant. (2) Learner with high prior knowledge and good learning outcome, flow to boredom, boredom to flow, and flow to confusion transitions were significant. (3) Learner with high prior knowledge and poor learning outcome, flow to boredom, boredom to flow were significant. (4) Learner with low prior knowledge and good learning outcome, flow to boredom and boredom to flow were significant. (5) Learner with low prior knowledge and poor learning outcome, flow to boredom and boredom to flow were significant. Finally, based on the above results, the learning effectiveness evaluation model is established through different prior knowledge and different learning outcomes, and different instructional strategies are obtained. The instructional strategies can be feedback to the teacher to adjust the teaching strategy in real time.
URI: http://hdl.handle.net/11455/98255
文章公開時間: 10000-01-01
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