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Research into student affect detection has historically relied on ground truth measures of emotion that utilize one of three sources of data: (1) self-report data, (2) classroom observations, or (3) sensor data that is retrospectively labeled. Although a few studies have compared sensor- and observation-based ap-proaches to student affective modeling, less work has explored the relationship between self-report and classroom observa-tions. In this study, we use both recurring self-reports (SR) and classroom observation (BROMP) to measure student emotion during a study involving middle school students interacting with a game-based learning environment for microbiology educa-tion. We use supervised machine learning to develop two sets of affect detectors corresponding to SR and BROMP-based measures of student emotion, respectively. We compare the two sets of detectors in terms of their most relevant features, as well as correlations of their output with measures of student learning and interest. Results show that highly predictive features in the SR detectors are different from those selected for BROMP-based detectors. The associations with interest and motivation measures show that while SR detectors captured underlying motivations, the BROMP detectors seemed to capture more in-the-moment information about the student申fs experience. Evi-dence suggests that there is benefit of using both sources of data to model different components of student affect.