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We report current findings when considering video recordings of facial expressions and body movements to provide affective personalized support in an educational context from an enriched multimodal emotion detection approach. In particular, we describe an annotation methodology to tag facial expression and body movements that conform to changes in the affective states of learners while dealing with cognitive tasks in a learning process. The ultimate goal is to combine these annotations with additional affective information collected during experimental learning sessions from different sources such as qualitative, self-reported, physiological, and behavioral information. These data altogether are to train data mining algorithms that serve to automatically identify changes in the learners' affective states when dealing with cognitive tasks which help to provide emotional personalized support.

The emotional situation of the learner can influence the learning process. For this reason, we are researching how educational recommender systems can take advantage of affective computing to improve the recommendation support in educational scenarios. The paper reports works carried out involving 18 educators and 77 learners to elicit and design emotional feedback to be provided for learners in terms of personalized recommendations. To this end, user centered design methods and data mining techniques are used.

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