Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/9894
Title: Leveraging Computationally Generated Descriptions of Audio Features to Enrich Qualitative Examinations of Sustained Uncertainty
Authors: Krist, Christina
Dyer, Elizabeth B.
Rosenberg, Joshua
Palaguachi, Chris
Cox, Eugene
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Krist, C., Dyer, E. B., Rosenberg, J., Palaguachi, C., & Cox, E. (2023). Leveraging computationally generated descriptions of audio features to enrich qualitative examinations of sustained uncertainty. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1258-1261). International Society of the Learning Sciences.
Abstract: Prosodic features of speech, such as pitch and loudness, are important aspects of the social dimensions of learning. In particular, these features are likely related to sustained disciplinary uncertainty in collaborative STEM learning contexts. We present a case conducting an exploratory, descriptive analysis of sustained uncertainty in groupwork in a secondary mathematics lesson integrating computational and qualitative methods with audiovisual data. Results of computational audio feature extraction of loudness and pitch, combined with a transcript, were used to identify potential patterns between laughter and uncertainty.
Description: Short Paper
URI: https://doi.org/10.22318/icls2023.981192
https://repository.isls.org//handle/1/9894
Appears in Collections:ISLS Annual Meeting 2023

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