TY - GEN
T1 - Towards Collaborative Convergence
T2 - 12th International Conference on Learning Analytics and Knowledge: Learning Analytics for Transition, Disruption and Social Change, LAK 2022
AU - Praharaj, Sambit
AU - Scheffel, Maren
AU - Schmitz, Marcel
AU - Specht, Marcus
AU - Drachsler, Hendrik
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - co-located collaboration
KW - collaboration
KW - collaboration analytics
KW - multimodal learning analytics
UR - http://www.scopus.com/inward/record.url?scp=85126267168&partnerID=8YFLogxK
U2 - 10.1145/3506860.3506922
DO - 10.1145/3506860.3506922
M3 - Conference contribution
AN - SCOPUS:85126267168
T3 - ACM International Conference Proceeding Series
SP - 358
EP - 369
BT - LAK 2022 - Conference Proceedings
PB - Association for Computing Machinery (ACM)
Y2 - 21 March 2022 through 25 March 2022
ER -