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

Shelby Keating; Erin Walker; Anil Motupali; Erin Solovey

doi:10.1145/2851581.2892496

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 proceeding › Conference 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 language	English (US)
Title of host publication	CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems
Publisher	Association for Computing Machinery
Pages	1698-1705
Number of pages	8
Volume	07-12-May-2016
ISBN (Electronic)	9781450340823
DOIs	https://doi.org/10.1145/2851581.2892496
State	Published - May 7 2016
Event	34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016 - San Jose, United States Duration: May 7 2016 → May 12 2016

Other

Other	34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016
Country	United States
City	San Jose
Period	5/7/16 → 5/12/16

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

Access to Document

10.1145/2851581.2892496

Fingerprint Dive into the research topics of 'Toward real-time brain sensing for learning assessment: Building a rich dataset'. Together they form a unique fingerprint.

Cite this

Toward real-time brain sensing for learning assessment : Building a rich dataset. / Keating, Shelby; Walker, Erin; Motupali, Anil; Solovey, Erin.

CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems. Vol. 07-12-May-2016 Association for Computing Machinery, 2016. p. 1698-1705.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, Association for Computing Machinery, pp. 1698-1705, 34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016, San Jose, United States, 5/7/16. https://doi.org/10.1145/2851581.2892496

@inproceedings{2509dcb30fcd40ad84009aea8d65b949,

title = "Toward real-time brain sensing for learning assessment: Building a rich dataset",

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.",

keywords = "Brain-computer interfaces, Educational data mining, Functional near-infrared spectroscopy, Machine learning",

author = "Shelby Keating and Erin Walker and Anil Motupali and Erin Solovey",

year = "2016",

month = may,

day = "7",

doi = "10.1145/2851581.2892496",

language = "English (US)",

volume = "07-12-May-2016",

pages = "1698--1705",

booktitle = "CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery",

note = "34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016 ; Conference date: 07-05-2016 Through 12-05-2016",

}

TY - GEN

T1 - Toward real-time brain sensing for learning assessment

T2 - 34th Annual CHI Conference on Human Factors in Computing Systems, CHI EA 2016

AU - Keating, Shelby

AU - Walker, Erin

AU - Motupali, Anil

AU - Solovey, Erin

PY - 2016/5/7

Y1 - 2016/5/7

N2 - 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.

AB - 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.

KW - Brain-computer interfaces

KW - Educational data mining

KW - Functional near-infrared spectroscopy

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85014657752&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85014657752&partnerID=8YFLogxK

U2 - 10.1145/2851581.2892496

DO - 10.1145/2851581.2892496

M3 - Conference contribution

AN - SCOPUS:85014657752

VL - 07-12-May-2016

SP - 1698

EP - 1705

BT - CHI EA 2016: #chi4good - Extended Abstracts, 34th Annual CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery

Y2 - 7 May 2016 through 12 May 2016

ER -