Abstract
We describe the design of an AIED tool EmoInfer to accelerate process-based research on emotions in Learning Sciences and help educational stakeholders understand the interplay of cognition and affect in ecologically-valid learning situations. Through an iterative implementation pipeline, we have developed a user interface to streamline automatic annotation and analysis of videos with facial expressions of emotion. EmoInfer can be applied to quantify and visualize the frequency and the temporal dynamics of naturally occurring or induced emotions (both on-the-fly and posthoc after data collection). By offering an accessible toolkit with “low floor, high ceiling and wide walls”, we aim to initiate democratizing emotion research in Learning Sciences.
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Notes
- 1.
For example, in one of the emotion inference coding schemes [5], facial expressions of emotions appeared at above chance rates in five cultures with varying societal characteristics (China, India, Japan, Korea, United States), with no gender differences in the frequency of occurrence of these patterns across the cultures.
- 2.
Compound emotions [6] are those that can be distinctively expressed because of overlap in action unit patterns as well as unambiguously discriminated by observers.
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Sinha, T., Dhandhania, S. (2022). Democratizing Emotion Research in Learning Sciences. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham. https://doi.org/10.1007/978-3-031-11647-6_27
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