Leveraging Computationally Generated Descriptions of Audio Features to Enrich Qualitative Examinations of Sustained Uncertainty

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.