This article proposes and applies a new systemic three-dimensional model and a methodology for empathy awareness and development, integrating different partial approaches found in the literature for developing empathy as a transversal competence. Background: Empathy is a competence linked to collaboration and teamwork. Perspective taking is an important component of empathy and it is key for professionals today. Even though empathy is valued in Computer Science Engineering courses, it is not yet fully addressed as an integral part of the training process. Intended outcomes: Both the model and the methodology are put into practice with a group of first year Computer Science Engineering students, highlighting the possibilities of the proposal for this course of studies. The experience presented here is an example of a classroom activity in which awareness and perspective taking are addressed, as key components, in relation to the collaborative work towards achieving empathy. Application design: The methodological proposal is applied to guide educators' decisions so that they can work on empathy in the classroom. Responses to several standardized and ad-hoc questionnaires by students from two universities are analyzed. Findings: The results revealed low to medium empathy levels in participating students, but a higher perception of their own empathic ability. The proposed methodology allows students to become aware of and develop some initial changes in relation to empathy, particularly in its perspective taking component, through classroom work.

Wearable technology is playing an increasing role in the development of user-centric applications. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training.

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