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This work presents the building and validating of a face expression database and a face expression recognizer. The face expression recognizer uses a geometric-based technique that measures distances between the central point on the face and other 68 facial landmark points. These measures are transformed into features to train a support vector machine. The database was built inside an educational context while students program in Java code. The tests validate the accuracy of the recognizer applying a ten-fold cross-validation.

This paper presents an implementation of Sentiment Analysis module that works in an Affective Intelligent Tutoring System. Later, we show the results of evaluating this module and some experiments we made with students. We consider that the main contribution of this work is that with this module, the system will be able to analyze the student's feedback using sentiment analysis techniques and provide useful information to the course administrators teachers for improving the teaching material.