Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity

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https://link.springer.com/chapter/10.1007/978-3-030-52240-7_29

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https://doi.org/10.1007/978-3-030-52240-7_29
 http://hdl.handle.net/11603/29964

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https://orcid.org/0000-0003-1007-2553

Date

2020-06-30

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book chapters
conference papers and proceedings
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Citation of Original Publication

Lee, H., Mandalapu, V., Kleinsmith, A., Gong, J. (2020). Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity. In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science(), vol 12164. Springer, Cham. https://doi.org/10.1007/978-3-030-52240-7_29

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Abstract

Public Speaking Anxiety (PSA) and Foreign Language Anxiety (FLA) afflict most English Language Learners (ELLs) during a presentation. However, few tools are available to help multicultural learners clearly identify which type of anxiety they are feeling. In this paper, we present a field study conducted in real language classrooms. We developed machine learning models based on features of electrodermal activity (EDA) to predict non-verbal behaviors manifested as PSA and FLA. The students were labeled with the anxiety categories both PSA and FLA, PSA more, FLA more, or no anxiety. To classify the ELLs into their respective anxiety categories, prominent EDA features were employed that supported the predictions of anxiety sources. These results may encourage both ELLs and instructors to be aware of the origins of anxiety subtypes and develop a customized practice for public speaking in a foreign language.