Brain tagging: A BCI and HCI tagging system to evaluate the learning contents

Yang Ting Shen, Peiwen Lu, Xin Mao Chen

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

1 Citation (Scopus)

Abstract

In this paper, we propose a novel interactive video-based learning system, Brain Tagging. Through collecting both the passive BCI and the active HCI tagging information, the learns’ objective and subjective metadata to the video contents are generated. The system gives a way to visualize the learning pattern by timeline chart consisted of BCI metadata (attention and meditation) and HCI metadata (good, question, and disagree). This can help to understand the learning process and performance, and thereby to provide proper improvement in e-learning.

Original languageEnglish
Title of host publicationLearning and Collaboration Technologies
Subtitle of host publicationNovel Learning Ecosystems - 4th International Conference, LCT 2017 Held as Part of HCI International 2017, Proceedings
EditorsPanayiotis Zaphiris, Andri Ioannou
PublisherSpringer Verlag
Pages46-54
Number of pages9
ISBN (Print)9783319585086
DOIs
Publication statusPublished - 2017 Jan 1
Event4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCI 2017 - Vancouver, Canada
Duration: 2017 Jul 92017 Jul 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10295 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCI 2017
CountryCanada
CityVancouver
Period17-07-0917-07-14

Fingerprint

Tagging
Human computer interaction
Metadata
Brain
Evaluate
Electronic Learning
Learning Systems
Learning Process
Chart
Learning systems
Learning

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shen, Y. T., Lu, P., & Chen, X. M. (2017). Brain tagging: A BCI and HCI tagging system to evaluate the learning contents. In P. Zaphiris, & A. Ioannou (Eds.), Learning and Collaboration Technologies: Novel Learning Ecosystems - 4th International Conference, LCT 2017 Held as Part of HCI International 2017, Proceedings (pp. 46-54). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10295 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-58509-3_5
Shen, Yang Ting ; Lu, Peiwen ; Chen, Xin Mao. / Brain tagging : A BCI and HCI tagging system to evaluate the learning contents. Learning and Collaboration Technologies: Novel Learning Ecosystems - 4th International Conference, LCT 2017 Held as Part of HCI International 2017, Proceedings. editor / Panayiotis Zaphiris ; Andri Ioannou. Springer Verlag, 2017. pp. 46-54 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Shen, YT, Lu, P & Chen, XM 2017, Brain tagging: A BCI and HCI tagging system to evaluate the learning contents. in P Zaphiris & A Ioannou (eds), Learning and Collaboration Technologies: Novel Learning Ecosystems - 4th International Conference, LCT 2017 Held as Part of HCI International 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10295 LNCS, Springer Verlag, pp. 46-54, 4th International Conference on Learning and Collaboration Technologies, LCT 2017, held as part of the 19th International Conference on Human-Computer Interaction, HCI 2017, Vancouver, Canada, 17-07-09. https://doi.org/10.1007/978-3-319-58509-3_5

Brain tagging : A BCI and HCI tagging system to evaluate the learning contents. / Shen, Yang Ting; Lu, Peiwen; Chen, Xin Mao.

Learning and Collaboration Technologies: Novel Learning Ecosystems - 4th International Conference, LCT 2017 Held as Part of HCI International 2017, Proceedings. ed. / Panayiotis Zaphiris; Andri Ioannou. Springer Verlag, 2017. p. 46-54 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10295 LNCS).

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

TY - GEN

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N2 - In this paper, we propose a novel interactive video-based learning system, Brain Tagging. Through collecting both the passive BCI and the active HCI tagging information, the learns’ objective and subjective metadata to the video contents are generated. The system gives a way to visualize the learning pattern by timeline chart consisted of BCI metadata (attention and meditation) and HCI metadata (good, question, and disagree). This can help to understand the learning process and performance, and thereby to provide proper improvement in e-learning.

AB - In this paper, we propose a novel interactive video-based learning system, Brain Tagging. Through collecting both the passive BCI and the active HCI tagging information, the learns’ objective and subjective metadata to the video contents are generated. The system gives a way to visualize the learning pattern by timeline chart consisted of BCI metadata (attention and meditation) and HCI metadata (good, question, and disagree). This can help to understand the learning process and performance, and thereby to provide proper improvement in e-learning.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Shen YT, Lu P, Chen XM. Brain tagging: A BCI and HCI tagging system to evaluate the learning contents. In Zaphiris P, Ioannou A, editors, Learning and Collaboration Technologies: Novel Learning Ecosystems - 4th International Conference, LCT 2017 Held as Part of HCI International 2017, Proceedings. Springer Verlag. 2017. p. 46-54. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-58509-3_5