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

Sambit Praharaj*, Maren Scheffel, Marcel Schmitz, Marcus Specht, Hendrik Drachsler

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Citations (Scopus)
20 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationLAK 2022 - Conference Proceedings
Subtitle of host publicationLearning Analytics for Transition, Disruption and Social Change - 12th International Conference on Learning Analytics and Knowledge
PublisherAssociation for Computing Machinery (ACM)
Pages358-369
Number of pages12
ISBN (Electronic)978-1-4503-9573-1
DOIs
Publication statusPublished - 2022
Event12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022 - Virtual, Online, United States
Duration: 21 Mar 202225 Mar 2022

Publication series

NameACM International Conference Proceeding Series

Conference

Conference12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
Country/TerritoryUnited States
CityVirtual, Online
Period21/03/2225/03/22

Keywords

  • co-located collaboration
  • collaboration
  • collaboration analytics
  • multimodal learning analytics

Fingerprint

Dive into the research topics of 'Towards Collaborative Convergence: Quantifying Collaboration Quality with Automated Co-located Collaboration Analytics'. Together they form a unique fingerprint.

Cite this