It is no news to teachers that emotional upsets can interfere with mental life. Recent findings on emotions have shown that the mechanisms associated with emotions are not only tightly intertwined neurologically with the mechanisms responsible for cognition, but they are also play a central role in decision making, problem solving, communicating, negotiating, and adapting to unpredictable environments. One of the main concerns in education is how to induce that state of cognitive and emotional arousal which will make the instruction an interesting and engaging experience for the student. Therefore, it would be very interesting to build and evaluate different strategies of teaching pedagogy related to student awareness of affective/cognitive states. But how can we diagnose the interest, the attention or the frustration of a student? Emotion recognition can be achieved by a number of methods, like the completion of questionnaires, personal interviews after the end of an action, self report, expert system, sentic modulation using bio-sensors. Bio-sensors possess a number of advantages in comparison to other emotion recognition methods as they can made both unobtrusive and robust against a number of environmental conditions. On the other hand, it is very hard to uniquely map physiological patterns onto specific emotions. In order to extract valuable information from physiological data researchers uses advanced data analysis techniques with average accuracies in emotion recognition near to 80%. It seems that identifying emotions through physiological data analysis may be hard, but for educational applications it may not be necessary to know precise emotions: it may be sufficient to simply know when a change occurs in the physiological data. GSR measuring is a relatively simple and not invasive method and can be applied with little disturbance to subjects. With the advent of electronics it is possible to continuously measure the galvanic skin response, store it in a computer and apply signal processing to extract valuable information. At the same time the computer can be utilized to deliver the stimuli to the subjects thus allowing flexibility, uniformity, a variety of stimuli and combinations of them. It is found that there is a relationship between galvanic skin response and frustration. When a subject was frustrated his/her GSR increased. Furthermore galvanic skin response is a linear correlate to arousal and reflects both emotional responses as well cognitive activity. In this paper we focus on GSR physiological signals and on the analysis of these signals to elicit students’ frustration or interest during processes related to education. Our results suggest that there is great potential for using physiological metrics to model emotional experience and cognitive activity, specifically to access the educational process. Experimenting on a small number of subjects we have produced a package of stimuli and signal processing method to isolate the response to stimuli. The first results seem to agree with bibliography as concern the relationship between GSR and frustration. In addition it has been recorded a partial disagreement between students’ subjective evaluation of their emotions and what is obtained from physiological signals. We consider very interesting the finding that the degree of interest or frustration of a subject depends on previous stimuli and in his/her positive attitude towards what is being instructed.