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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 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.

Original languageEnglish
Title of host publicationLearning and Collaboration Technologies. Learning and Teaching - 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Proceedings
EditorsPanayiotis Zaphiris, Andri Ioannou
PublisherSpringer Verlag
Pages407-417
Number of pages11
ISBN (Print)9783319911519
DOIs
Publication statusPublished - 2018 Jan 1
Event5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018 - Las Vegas, United States
Duration: 2018 Jul 152018 Jul 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10925 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Learning and Collaboration Technologies, LCT 2018 Held as Part of HCI International 2018
CountryUnited States
CityLas Vegas
Period18-07-1518-07-20

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shen, Y. T., Chen, X. M., Lu, P. W., & Wu, J. C. (2018). Use BCI to Generate Attention-Based Metadata for the Assessment of Effective Learning Duration. In P. Zaphiris, & A. Ioannou (Eds.), Learning and Collaboration Technologies. Learning and Teaching - 5th International Conference, LCT 2018, Held as Part of HCI International 2018, Proceedings (pp. 407-417). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10925 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-91152-6_31