高等教育出版
熱門: 吳清山  丘愛鈴  吳勁甫  九年一貫課程  三角關係  文化領導  
高等教育出版
首頁 臺灣期刊   學校系所   學協會   民間出版   大陸/海外期刊   政府機關   學校系所   學協會   民間出版   DOI註冊服務
閱讀全文
篇名
基于面部表情的学习困惑自动识别法
並列篇名
Automatic Learning Confusion Recognition Using Facial Expressions
作者 江波李萬健李芷璿葉韻
英文摘要
The role of emotion in learning remains largely unexamined and certainly undervalued in the education literature, despite recent molecular research found the evidence of the impact of emotion to learning. Learning emotion recognition is regarded as one of crucial technology in next-generation intelligent tutoring system. Among learning emotion, confusion is one of the most common one, which has a great influence on the learning outcomes. However, automatic recognition of learning confusion is a challenge because of its strong implicitness,compared to basic emotion such as happy and angry. We designed a confusion-inducing experiments within an online test environment and applied machine learning algorithms to detect the confusion emotion reflect from the face of subjects. The subjects were asked to solve a series of test questions with different difficulties within limited time. A high-solution professional camera was set in front of subjects to capture face image in high frequency. Then, the 78 key feature points of human face were extracted as feature vector, and fed in to a machine learning model to predict confusion. To get a robust prediction model, six popular classification algorithms, i. e. , logistic regression, support vector machine, K-nea-rest neighbor, decision tree,random forest,deep learning,were tested. A 10-fold cross validation was used to compare the six algorithms on five performance metrics that calculated using subjects, self-reported confusion labels. The experimental results show that most of the classification algorithms can detect studentsy learning confusion effectively and the average accuracy is over 58 % , and the random forest is the best prediction model with an average accuracy of 71. 18%. The proposed method in this study can provide technical support for the learner affect modeling in the next generation of intelligent tutoring system. The current results are promising,but the evaluation revealed some limitations that could further improve the research. Firstly, the self-reported emotion label is subjective and could be combined with field observation to improve the precision of labeling. In addition,the representation ability of feature point is still very limited. Therefore,in the future,we will attempt to use directly the image to detect confusion with deep neural network technology. Lastly,we also plan to investigate the possibility to simultaneously detect multiple learning emotions, such as confusion, boring and engagement.
起訖頁 101-108
關鍵詞 学习困惑面部表情检测情感计算learning confusiondetection of facial expressionaffective computingmachine learnings artificial intelligenceCSSCI
刊名 開放教育研究  
期數 201808 (24:4期)
出版單位 上海遠程教育集團;上海電視大學
該期刊
上一篇
社会性交互对在线阅读的影响——基于“微信读书”的调查
該期刊
下一篇
中国远程高等教育资源配置政策影响因素结构模型

高等教育知識庫  閱讀計畫  教育研究月刊  新書優惠  

教師服務
合作出版
期刊徵稿
聯絡高教
高教FB
讀者服務
圖書目錄
教育期刊
訂購服務
活動訊息
數位服務
高等教育知識庫
國際資料庫收錄
投審稿系統
DOI註冊
線上購買
高點網路書店 
元照網路書店
博客來網路書店
教育資源
教育網站
國際教育網站
關於高教
高教簡介
出版授權
合作單位
知識達 知識達 知識達 知識達 知識達 知識達
版權所有‧轉載必究 Copyright2011 高等教育文化事業有限公司  All Rights Reserved
服務信箱:edubook@edubook.com.tw 台北市館前路 26 號 6 樓 Tel:+886-2-23885899 Fax:+886-2-23892500