@inproceedings{3e510885b608424c8507d2cb1fdfed0a,
title = "Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration",
abstract = "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{\^A} 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{\^A} s.",
author = "Shen, {Yang Ting} and Chen, {Xin Mao} and Lu, {Pei Wen} and Wu, {Ju Chuan}",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-91152-6_31",
language = "English",
isbn = "9783319911519",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "407--417",
editor = "Panayiotis Zaphiris and Andri Ioannou",
booktitle = "Learning and Collaboration Technologies. Learning and Teaching - 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Proceedings",
address = "Germany",
note = "5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018 ; Conference date: 15-07-2018 Through 20-07-2018",
}