Supporting Instructors to Provide Emotional and Instructional Scaffolding for English Language Learners through Biosensor-based Feedback

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Abstract

Delivering a presentation has been reported as one of the most anxiety-provoking tasks faced by English Language Learners. Researchers suggest that instructors should be more aware of the learners' emotional states to provide appropriate emotional and instructional scaffolding to improve their performance when presenting. Despite the critical role of instructors in perceiving the emotional states among English language learners, it can be challenging to do this solely by observing the learners? facial expressions, behaviors, and their limited verbal expressions due to language and cultural barriers. To address the ambiguity and inconsistency in interpreting the emotional states of the students, this research focuses on identifying the potential of using biosensor-based feedback of learners to support instructors. A novel approach has been adopted to classify the intensity and characteristics of public speaking anxiety and foreign language anxiety among English language learners and to provide tailored feedback to instructors while supporting teaching and learning. As part of this work, two further studies were proposed. The first study was designed to identify educators' needs for solutions providing emotional and instructional support. The second study aims to evaluate a resulting prototype as a view of instructors to offer tailored emotional and instructional scaffolding to students. The contribution of these studies includes the development of guidance in using biosensor-based feedback that will assist English language instructors in teaching and identifying the students' anxiety levels and types while delivering a presentation.

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APA

Lee, H. (2020). Supporting Instructors to Provide Emotional and Instructional Scaffolding for English Language Learners through Biosensor-based Feedback. In ICMI 2020 - Proceedings of the 2020 International Conference on Multimodal Interaction (pp. 733–737). Association for Computing Machinery, Inc. https://doi.org/10.1145/3382507.3421159

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