The past few years have seen a surge of interest in deep neural networks. The wide application of deep learning in other domains such as image classification has driven considerable recent interest and efforts in applying these methods in educational domains. However, there is still limited research comparing the predictive power of the deep learning approach with the traditional feature engineering approach for common student modeling problems such as sensor-free affect detection. This paper aims to address this gap by presenting a thorough comparison of several deep neural network approaches with a traditional feature engineering approach in the context of affect and behavior modeling. We built detectors of student affective states and behaviors as middle school students learned science in an open-ended learning environment called Betty’s Brain, using both approaches. Overall, we observed a tradeoff where the feature engineering models were better when considering a single optimized threshold (for intervention), whereas the deep learning models were better when taking model confidence fully into account (for discovery with models analyses).