Towards Collaborative Convergence

Towards Collaborative Convergence: Quantifying Collaboration Quality with Automated Co-located Collaboration Analytics

Praharaj, Sambit (Open University of the Netherlands)
Scheffel, Maren (Ruhr-University Bochum)
Schmitz, Marcel (Zuyd University of Applied Science)
Specht, M.M. (TU Delft Web Information Systems)
Drachsler, Hendrik (Open University of the Netherlands; Goethe University)

2022

Collaboration is one of the four important 21st-century skills. With the pervasive use of sensors, interest on co-located collaboration (CC) has increased lately. Most related literature used the audio modality to detect indicators of collaboration (such as total speaking time and turn taking). CC takes place in physical spaces where group members share their social (i.e., non-verbal audio indicators like speaking time, gestures) and epistemic space (i.e., verbal audio indicators like the content of the conversation). Past literature has mostly focused on the social space to detect the quality of collaboration. In this study, we focus on both social and epistemic space with an emphasis on the epistemic space to understand different evolving collaboration patterns and collaborative convergence and quantify collaboration quality. We conduct field trials by collecting audio recordings in 14 different sessions in a university setting while the university staff and students collaborate over playing a board game to design a learning activity. This collaboration task consists of different phases with each collaborating member having been assigned a pre-fixed role. We analyze the collected group speech data to do role-based profiling and visualize it with the help of a dashboard.

co-located collaboration
collaboration
collaboration analytics
multimodal learning analytics

http://resolver.tudelft.nl/uuid:93e2d5fe-8c23-4241-ba10-e5185c226f93

https://doi.org/10.1145/3506860.3506922

Association for Computing Machinery (ACM)

978-1-4503-9573-1

LAK 2022 - Conference Proceedings: Learning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge

12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022, 2022-03-21 → 2022-03-25, Virtual, Online, United States

ACM International Conference Proceeding Series

Institutional Repository

conference paper

© 2022 Sambit Praharaj, Maren Scheffel, Marcel Schmitz, M.M. Specht, Hendrik Drachsler