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Conference Papers Year : 2017

EMODA: a tutor oriented multimodal and contextual emotional dashboard

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Abstract

Learners' emotional state has proven to be a key factor for successful learning. Visualizing learners' emotions during synchronous on-line learning activities can help tutors in creating and maintaining socio-affective relationships with their learners. However, few dashboards offer emotional information on the learning activity. The current study focuses on synchronous interactions via a videoconferencing tool dedicated to foreign language training. We collected data on learners' emotions in real conditions during ten sessions (five sessions for two learners). We propose to adopt and combine different models of emotions (discrete and dimensional) and to use heterogeneous APIs for measuring learners' emotions from different data sources (audio, video, self-reporting and interaction traces). Based on a thorough data analysis, we propose an approach to combine different cues to infer information on learners' emotional states. We finally present the EMODA dashboard, an affective multimodal and contextual visual analytics dashboard, which allows the tutor to monitor learners' emotions and better understand their evolution during the synchronous learning activity.
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Dates and versions

hal-01497669 , version 1 (03-04-2017)

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Mohamed Ez-Zaouia, Elise Lavoué. EMODA: a tutor oriented multimodal and contextual emotional dashboard. Seventh International Learning Analytics & Knowledge Conference (LAK 2017), Mar 2017, Vancouver, Canada. pp.429-438, ⟨10.1145/3027385.3027434⟩. ⟨hal-01497669⟩
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