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Existing literature argues that emotions have a significant impact on the majority of human activities and functions. The learning process is one of the activities on which emotions have a direct influence. Thus, understanding the manner in which emotions change the students' learning process is not only very important but it can also allow to improve the existing learning models. Currently, in the majority of situations, the teacher serves as a facilitator between the student and the learning course, and through a constant analysis of the student's behaviour, emotions and achievements, he constantly performs adjustments to the teaching process in order to meet the students' needs and goals. Thus far, in online learning environments there is no easy way for teachers to analyse students' behaviour and emotions. A possible solution to this problem can be the development of mechanisms that enable computers to automatically detect students' emotions and adapt the learning process in order to meet students' real needs. An emotional learning model was described and a software prototype was developed and tested, in order to find out whether it performs live identification of the students' emotions, by using affective computing techniques, and whether it automatically performs adjustments to their individual learning process. Through a deeper analysis and multidisciplinary discussion of the achieved results it is possible to acknowledge that not only emotions impact students' learning, but also that an application that performs live emotion recognition and which integrates this feature with adjustable online learning environments will trigger improvements in students' learning.

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