文献の詳細
| 論文の言語 | 日本語 |
|---|---|
| 著者 | ��樽��村 �����昹,Olivier Augereau,卒辰���� 卒丹,族束��促 孫��属狸 |
| 論文名 | 促揃臓村促��属袖�ﾃ昤損促坦促袖造�� Web 促束促叩促辿造嘆����造造造多賊����端村淡其��損谷��属損綻造�ﾂ渋乎�脱����多辰��棚 |
| 発表場所 | 多��孫息���ﾃ�遜続��族単 ��竪多����捉続��遜測族��続��造��孫息続��存側袖脱族単(ALST) |
| 査読の有無 | 無 |
| 年月 | 2019年7月 |
| 要約 | It is important for teachers to grasp students臓�� engagement in order to improve the quality of lectures. When they find that their students are not engaged in the lecture, they can give advice to the students to pay attention to the lecture. However, in the e-learning environment, there is no teacher to grasp the student臓��s engagement. So if the student loses his engagement, no one can help him to regain it. It may cause ineffective learning. The purpose of this study is to grasp the student臓��s engagement by using a pressure mat and web camera. We recorded the students臓�� postural data, that is upper body pressure distribution and upper body pose information, while they were taking e-learning lectures. In order to get the body to pose information from a web camera, we used a human pose estimation library called OpenPose. Then we extracted 38 features from upper body pressure distribution and 33 features from upper body pose information for every minute. We selected effective features by using the Forward Stepwise Selection. Lastly, we estimate whether he or she was engaged in or not with a Support Vector Machine. As a result, the average accuracy was 79.3% for student-dependent estimation. This result shows it is possible to predictthe student臓��s engagement automatically. |
- BibTeX用エントリー
@InCollection{��樽��村2019, author = {��樽��村 �����昹 and Olivier Augereau and 卒辰���� 卒丹 and 族束��促 孫��属狸}, title = {促揃臓村促��属袖�ﾃ昤損促坦促袖造�� Web 促束促叩促辿造嘆����造造造多賊����端村淡其��損谷��属損綻造�ﾂ渋乎�脱����多辰��棚}, year = 2019, month = jul, location = {多��孫息���ﾃ�遜続��族単 ��竪多����捉続��遜測族��続��造��孫息続��存側袖脱族単(ALST)} }