This paper presents a dual dashboard early warning system which uses students' affective state as a measure of risk. Affective state has been shown to influence CS1 performance, and specific states such as frustration have been linked to attrition. The software administers affective surveys to students using a series of 2-dimensional grids. Students then complete a qualitative journal entry. Risk weights are assigned to students based on the journal response's sentiment analysis and whether student's 2dimensional grid responses fall within configurable 'danger zone' bounds. The early warning system automatically flags students as needing support if the responses' combined risk weights exceed configurable thresholds. Additionally, flags can be assigned manually, either by instructors or by students themselves.

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