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Telepresence robots offer presence, embodiment, and mobility to remote users,making them promising options for homebound K-12 students. It is difficult,however, for robot operators to know how well they are being heard in remoteand noisy classroom environments. One solution is to estimate the operator'sspeech intelligibility to their listeners in order to provide feedback about itto the operator. This work contributes the first evaluation of a speechintelligibility feedback system for homebound K-12 students attending classremotely. In our four long-term, in-the-wild deployments we found that studentsspeak at different volumes instead of adjusting the robot's volume, and thatdetailed audio calibration and network latency feedback are needed. We alsocontribute the first findings about the types and frequencies of multimodalcomprehension cues given to homebound students by listeners in the classroom.By annotating and categorizing over 700 cues, we found that the most common cuemodalities were conversation turn timing and verbal content. Conversation turntiming cues occurred more frequently overall, whereas verbal content cuescontained more information and might be the most frequent modality for negativecues. Our work provides recommendations for telepresence systems that couldintervene to ensure that remote users are being heard.

Long-Term, in-the-Wild Study of Feedback about Speech Intelligibility for K-12 Students Attending Class via a Telepresence Robot