Abstract
We describe technology to dynamically collect information about students' emotional state, including human observation and real-time multi-modal sensors. Our goal is to identify physical behaviors that are linked to emotional states, and then identify how these emotional states are linked to student learning. This involves quantitative field observations in the classroom in which researchers record the behavior of students who are using intelligent tutors. We study the specific elements of learner's behavior and expression that could be observed by sensors. The long-term goal is to dynamically predict student performance, detect a need for intervention, and determine which interventions are most successful for individual students and the learning context (problem and emotional state).
| Original language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 29-39 |
| Number of pages | 11 |
| Volume | 5091 LNCS |
| DOIs | |
| State | Published - 2008 |
| Event | 9th International Conference on Intelligent Tutoring Systems, ITS 2008 - Montreal, QC, Canada Duration: Jun 23 2008 → Jun 27 2008 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 5091 LNCS |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 9th International Conference on Intelligent Tutoring Systems, ITS 2008 |
|---|---|
| Country | Canada |
| City | Montreal, QC |
| Period | 6/23/08 → 6/27/08 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
Viewing student affect and learning through classroom observation and physical sensors. / Dragon, Toby; Arroyo, Ivon; Woolf, Beverly P.; Burleson, Winslow; El Kaliouby, Rana; Eydgahi, Hoda.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5091 LNCS 2008. p. 29-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5091 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Viewing student affect and learning through classroom observation and physical sensors
AU - Dragon, Toby
AU - Arroyo, Ivon
AU - Woolf, Beverly P.
AU - Burleson, Winslow
AU - El Kaliouby, Rana
AU - Eydgahi, Hoda
PY - 2008
Y1 - 2008
N2 - We describe technology to dynamically collect information about students' emotional state, including human observation and real-time multi-modal sensors. Our goal is to identify physical behaviors that are linked to emotional states, and then identify how these emotional states are linked to student learning. This involves quantitative field observations in the classroom in which researchers record the behavior of students who are using intelligent tutors. We study the specific elements of learner's behavior and expression that could be observed by sensors. The long-term goal is to dynamically predict student performance, detect a need for intervention, and determine which interventions are most successful for individual students and the learning context (problem and emotional state).
AB - We describe technology to dynamically collect information about students' emotional state, including human observation and real-time multi-modal sensors. Our goal is to identify physical behaviors that are linked to emotional states, and then identify how these emotional states are linked to student learning. This involves quantitative field observations in the classroom in which researchers record the behavior of students who are using intelligent tutors. We study the specific elements of learner's behavior and expression that could be observed by sensors. The long-term goal is to dynamically predict student performance, detect a need for intervention, and determine which interventions are most successful for individual students and the learning context (problem and emotional state).
UR - http://www.scopus.com/inward/record.url?scp=70349805480&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349805480&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69132-7-8
DO - 10.1007/978-3-540-69132-7-8
M3 - Conference contribution
SN - 3540691308
SN - 9783540691303
VL - 5091 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 39
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -