Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration.

Yang Ting Shen, Xin Mao Chen,Pei Wen Lu, Ju Chuan Wu

Lecture Notes in Computer Science(2018)

引用 1|浏览18
暂无评分
摘要
This paper proposes a novel method for evaluating the video-based learning performance by using brain computer interface (BCI). We develop Interactive Brain Tagging system (IBTS) to collect learns' physiological affective metadata: attention. IBTS uses the EEG headset to measure learners' brainwave and convert it into the evaluable attention value. When learners are watching video, their attention values are recorded every one second and marked in each corresponding video clip. We visaulize the variation of attention and tried to find out the continuous duration of higher attention level in a video. We used a 15 min' video to conduct the experiment with 31 subjects. The result presented the difference of individual and collective attention duration. Moreover, in our case, the collected result suggested that the appropriate video time with higher attention may locate in 232 s.
更多
查看译文
关键词
Video-based learning,Attention,BCI,Affective computing,Metadata,EEG,Brainwave
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要