TY - GEN
T1 - Towards Speech-Based Collaboration Detection in a Noisy Classroom
AU - Shahrokhian, Bahar
AU - VanLehn, Kurt
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - An Intelligent Orchestration System, such as our FACT [1], should act like an automated teaching assistant that helps teachers provide relevant, timely help. To do so, it needs to know what the students are doing and thus who needs help more than the others. This is especially important when students work in small groups and the teacher’s ability to monitor every group frequently diminishes. This project is an attempt to investigate the feasibility and challenges of only using the students’ speech to predict each group’s collaboration status. We are using machine-learning techniques to build models that agree with our human annotator’s collaboration status judgments.
AB - An Intelligent Orchestration System, such as our FACT [1], should act like an automated teaching assistant that helps teachers provide relevant, timely help. To do so, it needs to know what the students are doing and thus who needs help more than the others. This is especially important when students work in small groups and the teacher’s ability to monitor every group frequently diminishes. This project is an attempt to investigate the feasibility and challenges of only using the students’ speech to predict each group’s collaboration status. We are using machine-learning techniques to build models that agree with our human annotator’s collaboration status judgments.
KW - Collaboration detection
KW - Educational data mining
KW - Intelligent orchestration systems
KW - Intelligent tutoring systems
UR - http://www.scopus.com/inward/record.url?scp=85135939217&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85135939217&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-11647-6_11
DO - 10.1007/978-3-031-11647-6_11
M3 - Conference contribution
AN - SCOPUS:85135939217
SN - 9783031116469
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 70
BT - Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium - 23rd International Conference, AIED 2022, Proceedings
A2 - Rodrigo, Maria Mercedes
A2 - Matsuda, Noburu
A2 - Cristea, Alexandra I.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022
Y2 - 27 July 2022 through 31 July 2022
ER -