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 15Â 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 232Â s.
| Original language | English |
|---|---|
| Title of host publication | Learning and Collaboration Technologies. Learning and Teaching - 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Proceedings |
| Editors | Panayiotis Zaphiris, Andri Ioannou |
| Publisher | Springer Verlag |
| Pages | 407-417 |
| Number of pages | 11 |
| ISBN (Print) | 9783319911519 |
| DOIs | |
| Publication status | Published - 2018 Jan 1 |
| Event | 5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018 - Las Vegas, United States Duration: 2018 Jul 15 → 2018 Jul 20 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10925 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 18-07-15 → 18-07-20 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration. / Shen, Yang Ting; Chen, Xin Mao; Lu, Peiwen; Wu, Ju Chuan.
Learning and Collaboration Technologies. Learning and Teaching - 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Proceedings. ed. / Panayiotis Zaphiris; Andri Ioannou. Springer Verlag, 2018. p. 407-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10925 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration
AU - Shen, Yang Ting
AU - Chen, Xin Mao
AU - Lu, Peiwen
AU - Wu, Ju Chuan
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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 15Â 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 232Â s.
AB - 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 15Â 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 232Â s.
UR - http://www.scopus.com/inward/record.url?scp=85050478724&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050478724&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-91152-6_31
DO - 10.1007/978-3-319-91152-6_31
M3 - Conference contribution
AN - SCOPUS:85050478724
SN - 9783319911519
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 407
EP - 417
BT - Learning and Collaboration Technologies. Learning and Teaching - 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Proceedings
A2 - Zaphiris, Panayiotis
A2 - Ioannou, Andri
PB - Springer Verlag
ER -