Detection of collaboration: Relationship between log and speech-based classification

Sree Aurovindh Viswanathan, Kurt Vanlehn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


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 languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Bruce McLaren, Peter Hastings, Rose Luckin
PublisherSpringer Verlag
Number of pages5
ISBN (Print)9783030232061
StatePublished - 2019
Event20th International Conference on Artificial Intelligence in Education, AIED 2019 - Chicago, United States
Duration: Jun 25 2019Jun 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11626 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Artificial Intelligence in Education, AIED 2019
Country/TerritoryUnited States


  • Collaborative learning
  • Learning analytics
  • Machine learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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