Abstract
Off-task discussions during collaborative learning offer benefits such as alleviating boredom and strengthening social relationships, and are therefore of interest to learning scientists. However, identifying moments of off-task speech requires researchers to navigate massive amounts of conversational data, which can be laborious. We lay the groundwork for automatically identifying off-task segments in a conversation, which can then be qualitatively analyzed and coded. We focus on in-person, real-time dialog and introduce an annotation scheme that examines two facets of dialog typical to in-person classrooms: whether utterances are pertinent to the lesson, and whether utterances are pertinent to the classroom, more broadly. We then present two computational models for identifying off-task utterances.
