Abstract
Research in the field of collaboration shows that students do not spontaneously collaborate with each other. A system that can measure collaboration in real time could be useful by, for example, helping the teacher locate a group requiring guidance. To address this challenge, my research focuses on building and comparing collaboration detectors for different types of classroom problem solving activities, such as card sorting and hand writing. I am also studying transfer: how collaboration detectors for one task can be used with a new task. Finally, we attempt to build a teachers dashboard that can describe reasoning behind the triggered alerts thereby helping the teachers with insights to aid the collaborative activity. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity was distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. Our preliminary results indicate that machine learned classifiers were reliable.
| Original language | English (US) |
|---|---|
| Title of host publication | Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings |
| Editors | Seiji Isotani, Peter Hastings, Amy Ogan, Bruce McLaren, Rose Luckin, Eva Millán |
| Publisher | Springer Verlag |
| Pages | 327-331 |
| Number of pages | 5 |
| ISBN (Print) | 9783030232061 |
| DOIs | |
| State | Published - Jan 1 2019 |
| Event | 20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States Duration: Jun 25 2019 → Jun 29 2019 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11626 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 20th International Conference on Artificial Intelligence in Education, AIED 2019 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 6/25/19 → 6/29/19 |
Fingerprint
Keywords
- Collaborative learning
- Learning analytics
- Machine learning
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Detection of collaboration : Relationship between log and speech-based classification. / Viswanathan, Sree Aurovindh; VanLehn, Kurt.
Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings. ed. / Seiji Isotani; Peter Hastings; Amy Ogan; Bruce McLaren; Rose Luckin; Eva Millán. Springer Verlag, 2019. p. 327-331 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11626 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detection of collaboration
T2 - Relationship between log and speech-based classification
AU - Viswanathan, Sree Aurovindh
AU - VanLehn, Kurt
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Research in the field of collaboration shows that students do not spontaneously collaborate with each other. A system that can measure collaboration in real time could be useful by, for example, helping the teacher locate a group requiring guidance. To address this challenge, my research focuses on building and comparing collaboration detectors for different types of classroom problem solving activities, such as card sorting and hand writing. I am also studying transfer: how collaboration detectors for one task can be used with a new task. Finally, we attempt to build a teachers dashboard that can describe reasoning behind the triggered alerts thereby helping the teachers with insights to aid the collaborative activity. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity was distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. Our preliminary results indicate that machine learned classifiers were reliable.
AB - Research in the field of collaboration shows that students do not spontaneously collaborate with each other. A system that can measure collaboration in real time could be useful by, for example, helping the teacher locate a group requiring guidance. To address this challenge, my research focuses on building and comparing collaboration detectors for different types of classroom problem solving activities, such as card sorting and hand writing. I am also studying transfer: how collaboration detectors for one task can be used with a new task. Finally, we attempt to build a teachers dashboard that can describe reasoning behind the triggered alerts thereby helping the teachers with insights to aid the collaborative activity. Data for building such detectors were collected in the form of verbal interaction and user action logs from students’ tablets. Three qualitative levels of interactivity was distinguished: Collaboration, Cooperation and Asymmetric Contribution. Machine learning was used to induce a classifier that can assign a code for every episode based on the set of features. Our preliminary results indicate that machine learned classifiers were reliable.
KW - Collaborative learning
KW - Learning analytics
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85068325563&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068325563&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-23207-8_60
DO - 10.1007/978-3-030-23207-8_60
M3 - Conference contribution
SN - 9783030232061
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 327
EP - 331
BT - Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
A2 - Isotani, Seiji
A2 - Hastings, Peter
A2 - Ogan, Amy
A2 - McLaren, Bruce
A2 - Luckin, Rose
A2 - Millán, Eva
PB - Springer Verlag
ER -