## 1 Introduction  
E-learning systems were introduced to facilitate and enhance the learning process [1; 2; 3]; a problem associated with their use is that the loss of face-to-face interaction makes it impossible to assess students' attention and response. This way, it is not possible to adapt courses to students' needs. Technologies enabling the monitoring and recognition of users' emotions and sentiments can address this problem [4; 5]. In this sense, the evaluation of students' engagement is the starting point to develop effective virtual learning environments. [PERSON] et al. reported that there are three types of engagement in learning: (1) behavioral engagement; (2) cognitive engagement; and (3) emotional engagement [6]. Behavioral engagement corresponds to effort, persistence, attention, and participation; cognitive engagement refers to psychological investment of the learner, such as being committed to understanding work. Lastly, emotional engagement focuses on students' feelings in learning processes such as being happy, sad, interested, bored, frustrated, or angry. The goal in increasing students' engagement led researchers to study in-depth methods to measure it [7; 8]. Notwithstanding the interest in developing methods to measure students' engagement, there are not many studies concerning the measure of engagement in e-learning environments; in this sense, [PERSON] et al. proposed a scale to measure engagement in this specific learning context [9]. Currently, the most popular tools for measuring engagement include (1) questioning methods; (2) physiological methods; and (3) observing methods. The first relies on questionnaires or self-reports in which students describe their own level of attention, engagement, distraction, excitement, or boredom [6; 7]. Self-reports are undoubtably useful and widespread since they do not require the use of any instrument to perform the evaluation; on the other hand, they have well-known limitations [8]. For example, some students may feel embarrassed in admitting they are not engaged, voiding the result of the questionnaire. Moreover, self-reports may be biased by primacy and recency memory effects, or they may differ dramatically in their own sense of what it means to be engaged. Concerning the physiological methods, engagement can be measured based on physiological and neurological sensor readings [8; 10]. In neuroscience, engagement is typically equated with the level of arousal or alertness. In this sense, physiological measures such as EEG (electroencephalogram), blood pressure, heart rate, and galvanic skin response are used to evaluate engagement and alertness [11; 12; 13; 14]. However, these measures require specialized sensors, and they are difficult to use in large scale studies. A third kind of automatic engagement recognition involves computer vision systems offering the prospect of unobtrusively estimating the engagement of a student by analyzing cues from the face, body posture, and hand gestures [4; 5; 15; 16]. A real-time students' engagement recognition system would have a wide range of applications, such as the following:  
* Human teachers in distance-learning environments could get real-time feedback on the level of engagement of their audience;
* Audience responses to educational videos could be used to automatically identify specific parts of the video causing disengagement, allowing the creator to change it adequately;
* Educational researchers could acquire large amounts of data to study the causes and the variables that affect students' engagement; these data would have a high temporal resolution when compared to self-reports and questionnaires;
* Educational institutions could monitor students' engagement and intervene. In the domain of the automatic evaluation of users' feedback, facial expression recognition (FER) is one of the possible methods. Interest in FER grew in the last few years; particularly, research is focusing on three-dimensional (3D) methods to refine the recognition accuracy and to widen the possibility of application [17].