The traditional dichotomy between reason and emotion in Western society, inherited from Descartes' dualistic view of mind and body, diminished the attention to the role of affectivity in learning and other cognitive activities in the last century. However, the work of psychologists and neurologists has highlighted the important role of motivation and affectivity in cognitive tasks. The field of Artificial Intelligence that works with emotion in computers is called Affective Computing, which studies mechanisms for recognizing human emotions or expressing emotions through machines in the human-computer interaction. This work proposes a learning model based on the student’s emotions bringing them to the center of the learning process, using their emotions, their profile, and learning style. The research was conducted using Design Science methodology for model proposition, construction and validation. Aiming at supporting the use of the model, as a proof of concept, a software tool (named CADAP) was developed in order to use affective computing concepts to perform the computational aid necessary to validate the work developed in this thesis. The model also indicates an evidence-based assessment methodology for developing knowledge, skills and abilities to be used to establish correlations between students' emotions and their learning. The feasibility of adopting the model was verified from a set of experiments, carried out in different professional and higher education institutions (from Latin America and Brazil), which showed that the student's emotions used individually contribute to the learning process and that it is possible to relate the student's performance with their emotional's reactions.
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