The number of e-learning platforms has grown in recent years due to advances in cloud computing, ease of access to technology, and the trend of people to constantly improve their knowledge and skills. For this reason, a great deal of research has been carried out to improve the use of these platforms. This work focuses on two research streams. First, Emotion-aware systems are based on the use of different tools to capture students’ emotions in order to adapt the lessons to their mood. Secondly, the main goal of Learning Analytics is to harness educational data sets to infer, create, and predict new information that helps to improve learning process. Once the basis of this project have been defined, its main development has consisted of the implementation of a system capable of detecting the mood of students in the course and during the performance of different activities. This system has been integrated into one of the most used e-learning platforms: Moodle. With the purpose of displaying the data collected by this system, it has been necessary to implement a set of visualizations in two dashboards, designed for teachers and students respectively. In the same manner, this data has been analyzed with Machine Learning techniques to infer relations, outliers, or trends. Finally, to take advantage of the capabilities of the implemented emotion detector, a new version of Ewetasker, a semantic task automation platform, has been developed. Through it, students are able to adapt the environment to their emotions, improving their comfort when performing tasks. To summarize, the aim of this project has been to improve students mood through the different developments carried out, and consequently, their academic performance.