  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.
