Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88334
Title: Automated classification of classroom climate by audio analysis
Authors: James, Anusha
Chua, Victoria Yi Han
Maszczyk, Tomasz
Núñez, Ana Moreno
Bull, Rebecca
Lee, Kerry
Dauwels, Justin
Keywords: Automated Classification
Engineering::Electrical and electronic engineering
Audio Analysis
Issue Date: 2018
Source: James, A., Chua, V. Y. H., Maszczyk, T., Núñez, A. M., Bull, R., Lee, K., & Dauwels, J. (2018). Automated classification of classroom climate by audio analysis. International Workshop on Spoken Dialog System Technology.
Abstract: While in training, teachers are often given feedback about their teaching style by experts who observe the classroom. Trained observer coding of classroom such as the Classroom Assessment Scoring System (CLASS) provides valuable feedback to teachers, but the turnover time for observing and coding makes it hard to generate instant feedback. We aim to design technological platforms that analyze real-life data in learning environments, and generate automatic objective assessments in real-time. To this end, we adopted state-of- the-art speech processing technologies and conducted trials in real-life teaching environments. Although much attention has been devoted to speech processing for numerous applications, few researchers have attempted to apply speech processing for analyzing activities in classrooms. To address this shortcoming, we developed speech processing algorithms that detect speakers and social behavior from audio recordings in classrooms. Specifically, we aim to infer the climate in the classroom from non-verbal speech cues. We extract non-verbal speech cues and lowlevel audio features from speech segments and we train classifiers based on those cues. We were able to distinguish between positive and negative CLASS climate scores with 70-80% accuracy (estimated by leave-one-out crossvalidation). The results indicate the potential of predicting classroom climate automatically from audio recordings.
URI: https://hdl.handle.net/10356/88334
http://hdl.handle.net/10220/49458
Rights: © 2018 The Author(s). All rights reserved. This paper was published by IWSDS 2018 in International Workshop on Spoken Dialog System Technology and is made available with permission of The Author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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