Nowadays, we are constantly interacting with computers, mobiles and other wearable devices. These interactions leave behind the digital footprint of the user. This data is used with different goals in the so-called Big Data field to predict customer behaviour in marketing and health research. Learning Analytics tackles this challenge in the Technology Enhanced Learning field. George Siemens defines Learning Analytics as the measurement, collection, analysis and reporting of the data to understand and optimise learning. In this context, we find a variety of studies that process the data different. Some studies implement complex algorithms and display the outcome to the user. Others rely on simpler approaches to process the data but enabling the user to explore the data with understandable, comprehensive and usable visualisations. Users can draw conclusions by their own and, with this information, steer their own learning process. This thesis is contextualised in the latter and intends to help students to become autonomous and lead their own educational process. This dissertation presents the work in the scope of four research questions: 1) RQ1 - What characteristics of learning activities can be visualised usefully for learners?; 2) RQ2 - What characteristics of learning activities can be visualised usefully for teachers?; 3) RQ3 - What are the affordances of and user problems with tracking data automatically and manually?; and 4) RQ4 - What are the key components of a simple and flexible architecture to collect, store and manage learning activity?. The exploration of these research questions include the deployment of: 1) three different learning dashboard designs deployed in real courses with 128 students participating in the evaluations; 2) the analysis of two Massive Open Online Courses (MOOCs) with 56876 enrolled students; and 3) the deployment of an architecture in two real case studies, including a European project with more than 15 scheduled pilots. The results highlight the usefulness of learning dashboards for students to analyse their own and their peers' activity. Throughout the description of a case study, this work explains how teachers can visualise the learning traces in order to learn from their courses once finished. Manual and automatic trackers have benefits and drawbacks. For example, manual trackers respect the user privacy in blended learning courses but the data provided by the students is not trusted by their fellow students. Automatic trackers are more accurate, but they do not track the activity outside of the computer, and, therefore, do not provide the complete picture that students demand. This research also identifies three components to deploy a simple and flexible architecture to collect data in open learning environments: 1) a set of simple services to push and pull the learning traces; 2) a simple data schema to ensure completeness and findability of the data; and 3) independent components to collect the data from data providers.