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Title: What Can Automated Speech Recognition Add to Qualitative Video Observations of Small Groups’ Collaborative Interactions?
Authors: Zabolotna, Kateryna
Spikol, Daniel
Malmberg, Jonna
Keywords: CSCL
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Zabolotna, K., Spikol, D., & Malmberg, J. (2023). What can automated speech recognition add to qualitative video observations of small groups’ collaborative interactions?. In Damșa, C., Borge, M., Koh, E., & Worsley, M. (Eds.), Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023 (pp. 382-383). International Society of the Learning Sciences.
Abstract: This study explores engaged and disengaged collaborative learning (CL) by applying qualitative video coding and automated speech recognition to video and audio data. We present a case study of 2 groups (N=6) of students building a robot together. Their CL interactions were 1) qualitatively coded for on-task behavior; 2) analyzed with speech recognition methods for voice activity and speaker identification. The initial results visualize how conversation patterns differ between groups and discuss the value of automated speech recognition combined with qualitative video coding for investigating engaged and disengaged CL in group interactions.
Description: Poster
Appears in Collections:ISLS Annual Meeting 2023

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