  In recent years, the main problem in e-learning has shifted from analyzing
content to personalization of learning environment by Intelligence Tutoring
Systems (ITSs). Therefore, by designing personalized teaching models, learners
are able to have a successful and satisfying experience in achieving their
learning goals. Affective Tutoring Systems (ATSs) are some kinds of ITS that
can recognize and respond to affective states of learner. In this study, we
designed, implemented, and evaluated a system to personalize the learning
environment based on the facial emotions recognition, head pose estimation, and
cognitive style of learners. First, a unit called Intelligent Analyzer (AI)
created which was responsible for recognizing facial expression and head angles
of learners. Next, the ATS was built which mainly made of two units: ITS, IA.
Results indicated that with the ATS, participants needed less efforts to pass
the tests. In other words, we observed when the IA unit was activated, learners
could pass the final tests in fewer attempts than those for whom the IA unit
was deactivated. Additionally, they showed an improvement in terms of the mean
passing score and academic satisfaction.
