![]() ![]() Results show that, globally, engagement prediction from students’ facial behavior was weakly correlated to their subjective answers. Then, the collected videos were analyzed automatically with a software that implements the model and provides an interface for the visual analysis of the model outcome. During the experiment we collected videos of students behavior and, at the end of each session, we asked students to answer a questionnaire for assessing their perceived engagement. The aim of the study was to compare the self-evaluation of the engagement perceived by the students with the one assessed by the model. In order to test its performance in learning contexts, an experiment, involving students attending an online lecture, was performed. The dataset used to learn the model is the one of the EmotiW 2019 challenge datasets. This article presents a research aiming to investigate how student engagement level can be assessed from facial behavior and proposes a model based on Long Short-Term Memory (LSTM) networks to predict the level of engagement from facial action units, gaze, and head poses. The automatic monitoring and assessment of the engagement level of learners in distance education may help in understanding problems and providing personalized support during the learning process. ![]()
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