Student affect is widely recognized as a major influence on learning gains and engagement, which has lead to the development of many automated detectors for affect. However, in order to effectively respond to student affect, we must know how students are interpreting it. This study proposes a novel automated detector, which models when students are attributing their epistemic emotion to task difficulty. The goal is to use detectors like this one to better understand how to respond to students’ affective states (in this case, boredom, confusion, frustration and nervousness). We then discuss the implications of this novel detector for real-time support in a game-based learning environments.