Toward real-time brain sensing for learning assessment: Building a rich dataset

Shelby Keating, Erin Walker, Anil Motupali, Erin Solovey

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

1 Scopus citations

Abstract

By integrating real-time brain input into personalized learning environments, it would be possible to capture a learner's changing cognitive state and adapt the learning experience appropriately. Working toward this goal, we aim to develop a robust system that can classify a user's cognitive state during a learning activity, using brain data collected with functional near-infrared spectroscopy, an emerging non-invasive neuroimaging tool. This paper describes preliminary steps we have taken toward this objective as well as the underlying vision and research goals. This work has implications for online education as well as the growing fields of brain-computer interfaces and physiological computing.

Original languageEnglish (US)
Title of host publicationCHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages1698-1705
Number of pages8
Volume07-12-May-2016
ISBN (Electronic)9781450340823
DOIs
StatePublished - May 7 2016
Event34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016 - San Jose, United States
Duration: May 7 2016May 12 2016

Other

Other34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016
CountryUnited States
CitySan Jose
Period5/7/165/12/16

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Keywords

  • Brain-computer interfaces
  • Educational data mining
  • Functional near-infrared spectroscopy
  • Machine learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

Cite this

Keating, S., Walker, E., Motupali, A., & Solovey, E. (2016). Toward real-time brain sensing for learning assessment: Building a rich dataset. In CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems (Vol. 07-12-May-2016, pp. 1698-1705). Association for Computing Machinery. https://doi.org/10.1145/2851581.2892496