遠距教學情境中自然學習表情之判讀與分析

陳思如

doi:10.6845/NCHU.2012.00370

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遠距教學情境中自然學習表情之判讀與分析

The Judgment and Analysis of Spontaneous Emotion on Distance Learning

陳思如

中興大學資訊管理學系所學位論文； 2012年 (2012 / 01 / 01) ， P1 - 50

繁體中文 DOI： 10.6845/NCHU.2012.00370

情意計算；免疫記憶克隆演算法；支持向量機；同步遠距教學；面部特徵； Affective computing ； support vector machine ； synchronous distance learning ； facial expressions

Abstract │ Reference (41) │ Times Cited (2) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

In recent years, under rapid developments in Information and Communication Technology, distance learning is emerging as a new teaching model. In the domain of distance learning, how to get learners' learning status in an isolated environment without face-to-face communication has become an important topic. Most studies focus on analyzing the students’ attention degree in the class, in order to attract students’ attention and make them focus on class. The subject of this study is further analysis of students’ cognitive status, and feedback to teacher to imply appropriate teaching method. The quick response of teacher can drive students’ active learning attitude, and the different cognitive status corresponds to different teaching programs could achieve the purpose of individualized teaching.
This paper provides a learning state recognition model, analyze a learner's spontaneous expressions in the synchronous distance learning environment, classified the learner's learning state into two understanding labels: 0 and 1, and presented to the teacher to adjusting teaching programs. There are 66 feature points detected in the face detection phase, those points transformed to 66 eigenvalues, passed to feature selection algorithm: Immune Memory Clone Feature Selection (IMCFS) combines Support Vector Machine (SVM), and then get the classification of learning expressions with 85.3% accuracy under 10-fold cross-validation via 495 records. This study attempts to put learners’ facial expressions through the affective computing process, after classified by computer, the teacher could know the learners’ learning status, and use the information to progress teaching mechanism, in order to achieve the goal of adaptive teaching.

Reference ( 41 ) 〈TOP〉

[3] 潘奕安，“低解析度影像序列之自動化表情辨識系統”，碩士論文，資訊工程學系，國立成功大學，台南市，中華民國，2004。
連結：
[8] 中華民國國家高速網路與計算中心。（2012，4月4日）。Colife線上會議系統 [Online]。擷取自：http://meeting.colife.org.tw/index.aspx。
連結：
[11] R. W. Picard, Affective Computing. London, UK: MIT Press, 1997.
連結：
[13] T. M. Cover and J. A. Thomas, Elements of Information Theory. New York, USA: John Wiley & Sons, 1991.
連結：
[18] F. Tsalakanidou and S. Malassiotis, “Real-time 2D+3D facial action and expression recognition,” Pattern Recognition, vol. 43, no. 5, pp. 1763-1775, May, 2010.
連結：

Times Cited ( 2 ) 〈TOP〉

Altmetrics

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