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Learning-centered emotions have a significant role in the pedagogical process of a student. For this reason, it is relevant that virtual learning environments take into account not only the cognitive aspects of the student but also the affective ones. Some approaches such as self-report, observational, and neuroimaging have been used extensively to measure emotions. However, today other methods of artificial intelligence such as the recognition of facial expressions and sentimental analysis have proven to be a better alternative in the automatic recognition of affect, and the results are superior when we combine and integrate several modes of recognition. In this work, we present three different methods to combine the recognition of facial expressions and textual expressions, applied to virtual learning environments. For the implementation of recognizers, we use our own corpora with classifiers that employ deep learning techniques. We evaluate the efficiency of the three fusion methods by comparing them against unimodal methods of emotion recognition. The improvement of one

Reconocimiento multimodal de emociones orientadas al aprendizaje