$1 Conversational Turn Detector: Measuring How Video Conversations Affect Student Learning in Online Classes

Abstract

Massive online classes can benefit from peer interactions such as discussion, critique, or tutoring. However, to scaffold productive peer interactions, systems must be able to detect student behavior in interactions at scale, which is challenging when interactions occur over rich media like video. This paper introduces an imprecise yet simple browser-based conversational turn detector for video conversations. Turns are detected without accessing video or audio data. We show how this turn detector can find dominance in video-based conversations. In a case study with 1,027 students using Talkabout, a video-based discussion system for online classes, we show how detected conversational turn behavior correlates with participants' subjective experience in discussions and their final course grade.