Author: Lee, Heera; Mandalapu, Varun; Kleinsmith, Andrea; Gong, Jiaqi
Title: Distinguishing Anxiety Subtypes of English Language Learners Towards Augmented Emotional Clarity Cord-id: 7qk68eq3 Document date: 2020_6_10
ID: 7qk68eq3
Snippet: 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 la
Document: 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.
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