"Sensor-free" detectors of student affect that use only student activity data and no physical or physiological sensors are cost-effective and have potential to be applied at large scale in real classrooms. These detectors are trained using student affect labels collected from human observers as they observe students learn within intelligent tutoring systems (ITSs) in real classrooms. Due to the inherent diversity of student activity and affect dynamics, observing the affective states of some students at certain times is likely to be more informative to the affect detectors than observing others. Therefore, a carefully-crafted observation schedule may lead to more meaningful observations and improved affect detectors. In this paper, we investigate whether active (machine) learning methods, a family of methods that adaptively select the next most informative observation, can improve the efficiency of the affect label collection process. We study several existing active learning methods and also propose a new method that is ideally suited for the problem setting in affect detection. We conduct a series of experiments using a real-world student affect dataset collected in real classrooms deploying the ASSISTments ITS. Results show that some active learning methods can lead to high-quality affect detectors using only a small number of highly informative observations. We also discuss how to deploy active learning methods in real classrooms to improve the affect label collection process and thus sensor-free affect detectors. [For the full proceedings, see ED599096.]