Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98254
標題: 建立互動式問題解決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 Interactive Programming Learning
作者: 劉思廷
Szu-Ting Liu
關鍵字: 先備知識
情緒辨識
學習情緒
學習情緒轉移機制
prior knowledge
emotion recognition
learning emotion
emotion transferring mechanism
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摘要: 在互動式程式教學中,教學者最難掌握的就是學習者在學習過程中的學習狀態。學習者面對螢幕與螢幕產生互動,而教學者無法直接觀察學習者的學習狀況,卻可以透過網路攝影機辨識學習者的臉部表情,觀察學習者的情緒來了解學習者的學習狀態。 本論文透過臉部表情來辨識學習情緒,以網路攝影機擷取學習者在電腦前編寫程式的臉部影像。從學習者的臉部表情與情緒反應來評估學習者的學習成效,提供教學者調整題目難度的依據,達到適性化學習的目標。 在沒有網路公開的學習情緒資料庫下,為了建立一個有效的學習情緒預測模型,本研究室首先建立「學習情緒人臉影像資料庫」作為訓練資料。資料庫包含實驗室表情與自然表情的影像資料。其次,透過OpenCV函式庫所提供的訓練級聯分類器,分別建立個別情緒的辨識模型。最後,利用辨識模型直接辨識學習者在「C語言程式設計上機考試」的學習情緒。情緒辨識率(AUC)除了投入以外均達到0.7以上。 高、低先備知識會影響問題解決學習中學習者的學習成效,本論文觀察不同先備知識學習者在學習程式設計的上機考試中,所表現出的學習情緒與學習成效的關係。不同於以往採用單一情緒來預測學習成效,本論文將利用學習情緒轉移機制與情緒轉移出現於前、後段的機率來探討不同先備知識的學習者其學習成效的差異。研究結果顯示: 1.高先備知識學習者出現困惑到驚訝的情緒轉移以及困惑到驚訝發生在後段的可能性增加,會有高學習成效。 2.高先備知識學習者若出現困惑到挫折、挫折到困惑、挫到投入的情緒轉移以及投入到無聊、挫折到困惑發生在後段可能性增加,無聊到投入發生在後段可能性減少,會是低學習成效。 3.低先備知識學習者若出現困惑到驚訝的情緒轉移以及投入到喜悅發生在後段可能性減少,會有高學習成效。 4.低先備知識學習者出現困惑到挫折、挫折到困惑的情緒轉移以及困惑到挫折發生在後段可能性增加,會是低學習成效。
In the environment of Interactive Programming Learning, the most difficult for the teacher is the learners' learning status in the learning process. Learners face the screen interacting with the screen, and the teacher cannot directly observe the learner's learning status. But teacher can observe learner's emotion to understand their learning status through the webcam. We identify learning emotions through facial expressions, and use a webcam to capture the learners' face image when they are coding. The learner's learning effectiveness is evaluated from their facial expressions, and the teacher is provided with the basis for adjusting the difficulty of the test and achieves the goal of adaptive learning. In order to establish a learning emotion prediction model without a publicly available learning emotion database, we first established a 'Learning Emotion Facial Image Database', as the training data for the model. The database contains image data of laboratory expressions and natural expressions. Secondly, through the cascade classifier provided by the OpenCV, we established learning emotion detector individually. Finally, the detector was used to directly identify learners' learning emotions in the 'C language programming test'. The emotion recognition rate (we used AUC as metrics to evaluate our model) reached 0.7 or more in addition to the 'flow'. The prior knowledge will affect the learner's learning effectiveness in problem-solving learning. We will observe the relationship between learning emotions and learning effectiveness from different prior knowledge learners in the programming test. Instead of using single emotion to predict learning effectiveness, we used emotion transferring mechanism and the likelihood of emotion transferring appearing in the former and latter stages of learning. And the research shows: 1.When high prior knowledge learners have the transition of Confusion to Surprise, and the likelihood of Confusion to Surprise increases in the latter stage of learning, it will be great learning effectiveness. 2.When high prior knowledge learners have Confusion to Frustration, Frustration to Confusion, Frustration to Flow and the likelihood of Flow to Boredom, Frustration to Confusion increase in the latter stage of learning, the likelihood of Boredom to Flow decrease in the latter stage of learning, it will be bad learning effectiveness. 3.When low prior knowledge learners have Confusion to Surprise, and the likelihood of Boredom to Flow decreases in the latter stage of learning, it will be great learning effectiveness. 4.When low prior knowledge learners have Confusion to Frustration, Frustration to Confusion and Confusion to Frustration increase in the latter stage of learning, it will be bad learning effectiveness.
URI: http://hdl.handle.net/11455/98254
文章公開時間: 10000-01-01
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