TY - GEN
T1 - Detection of collaboration
T2 - 20th International Conference on Artificial Intelligence in Education, AIED 2019
AU - Viswanathan, Sree Aurovindh
AU - Vanlehn, Kurt
N1 - Funding Information:
This research was funded by the Diane and Gary Tooker chair for effective education in Science Technology Engineering and Math, by NSF grant IIS-1628782, and by the Bill and Melinda Gates Foundation under Grant OP1061281.
Funding Information:
Acknowledgements. This research was funded by the Diane and Gary Tooker chair for effective education in Science Technology Engineering and Math, by NSF grant IIS-1628782, and by the Bill and Melinda Gates Foundation under Grant OP1061281.
Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
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
AN - SCOPUS:85068325563
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 - Millán, Eva
A2 - Ogan, Amy
A2 - McLaren, Bruce
A2 - Hastings, Peter
A2 - Luckin, Rose
PB - Springer Verlag
Y2 - 25 June 2019 through 29 June 2019
ER -