AffectiveSpotlight: Facilitating the Communication of Affective Responses from Audience Members during Online Presentations
This addresses the challenge for presenters in online settings to gauge audience reactions, though it is incremental as it builds on existing affect sensing technologies.
The paper tackled the problem of limited audience feedback in online presentations by developing AffectiveSpotlight, a bot that analyzes facial responses and head gestures to spotlight expressive audience members, resulting in presenters being significantly more aware of their audience, speaking longer, and aligning self-assessments with audience ratings.
The ability to monitor audience reactions is critical when delivering presentations. However, current videoconferencing platforms offer limited solutions to support this. This work leverages recent advances in affect sensing to capture and facilitate communication of relevant audience signals. Using an exploratory survey (N = 175), we assessed the most relevant audience responses such as confusion, engagement, and head-nods. We then implemented AffectiveSpotlight, a Microsoft Teams bot that analyzes facial responses and head gestures of audience members and dynamically spotlights the most expressive ones. In a within-subjects study with 14 groups (N = 117), we observed that the system made presenters significantly more aware of their audience, speak for a longer period of time, and self-assess the quality of their talk more similarly to the audience members, compared to two control conditions (randomly-selected spotlight and default platform UI). We provide design recommendations for future affective interfaces for online presentations based on feedback from the study.