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

• Published in: Learning and Collaboration Technologies. Learning and Teaching Vol. 10925; pp. 407 - 417
• Main Authors: Shen, Yang Ting, Chen, Xin Mao, Lu, Pei Wen, Wu, Ju Chuan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Affective computing; Attention; BCI; Brainwave; EEG; Metadata; Video-based learning
• ISBN: 9783319911519; 3319911511
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-91152-6_31

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.