The goal of this research is to investigate the effect of emotion-aware interventions on students' behavioral and emotional states. To this end, we collected data from 12 students in the 9th grade in a high school in Turkey. The data collection took place in two sessions of an English Course. While the students were reading articles and solving relevant questions, our data collection application running in the background recorded the videos of the individual students through a camera and captured students' screens in a non-intrusive manner. In total, we had 12.5 h of student data. We employed the human expert labeling process (HELP) (Aslan et al. in Workshop proceedings at international conference on intelligent tutoring systems (ITS), pp 156-165, 2016) to have the data labeled (150 h of data labeling in total). The data collection application was designed in a way that it also collected emotional self-labels (i.e., emotional states as self-reported by students at any time of learning). We leveraged emotional self-label information to suggest various real-time interventions for the students. The results obtained using the final expert labels showed that the percentage of the students' "Satisfied" state was significantly higher after interventions. The results also demonstrated that although the interventions were triggered by the emotional states as self-labeled by the students and tailored to improve such states, there was a major positive impact of these interventions on students' behavioral states. This preliminary study showed that even with a limited set of emotion-aware interventions based on self-labels, students' states could be impacted positively. Conducting large-scale pilots leveraging more advanced interventions is a future direction for our research.