Although most HE institutions have embraced the potential of e-learning methods and have invested in technology-enhanced learning environments and tools, we do not have a clear picture of students’ online learning habits. Moreover, e-learning so far has not received much attention within quality assessment procedures. The understanding of concrete learning behaviour and uses of electronic courseware and online resources is an important prerequisite to assess the quality of autonomous, lifelong learning. Another challenge are the high dropout rates associated with e-learning, not in the least where MOOCs are concerned. Among numerous other variables, an important factor is the lack of engagement and motivation, since students don’t know how they are progressing and what their peers’ achievements over time are. Students involved in e-learning often have a limited knowledge of their own learning habits and which rate of studying with the online material is required. To succeed in (semi-)autonomous learning, however, a higher level of self-regulation is needed. This project proposal addresses the Erasmus + challenge of raising the quality of education through the use of learning analytics. Learning analytics is a new and promising research field which can be defined as “the measurement, collection, analysis and reporting of data about learners in their context, for purposes of understanding and optimizing learning and the environment in which it occurs” (Siemens et. Al). The recent evolution of web-based learning and the possibility of tracking students’ online behaviour offers promising new ways of measuring actual self-study activities. This project aims to establish a clear image of how higher education students in different European countries learn online. The goal is to map existing learning patterns in 4 different types of online language learning and teaching and maths courses and to feed back this new knowledge to the most important educational actors themselves, being the students and their lecturers. We want to use a bottom-up learning analytics approach taking the perspective of the learning process, focusing on the courses used and the students’ learning trails through these courses, and intend to use process mining techniques for the analysis of the data. Therefore, a complimentary and cross-disciplinary consortium of teams from three universities and a private open source company was set up. Together, they will first implement tracking of learning data based on the new Experience API standard for interoperability with other learning environments (e.g. mobile apps, games) and reporting tools. After a piloting phase, the learning behavior of several student groups enrolled in distance learning or university programmes with an important self-study component will be tracked during one semester. The data will be collected in a central repository (Learning Record store). An important point of concern in the project will be the privacy of the students who will be monitored. Participants will be asked to give their consent to collect and use their data for the aims that will be clearly described. The data will be kept anonymous and the EU data protection directive will be taken into account. In a fourth phase we aim to analyse the processes of autonomous learning comparing them to the intended pedagogic objectives of the tools. Patterns of learning behaviour will be detected, based on which different user profiles can be identified and feedback about used learning resources can be obtained. Finally, data visualisation tools will be developed and implemented in order to create a learning dashboard application for students and for teachers. Special care will be given to ease of use of the dashboards for non-specialist users. These applications will allow both the teachers and the students to understand how they learn online but also to compare their profile to user patterns of their peers. Educators get dynamic and real-time overviews of how their students are progressing, which students might be at risk of dropping out or of failing for the course and which parts of the courses cause difficulties/require more feedback. The project aims at the development of a generic model for implementing learning analytics in interactive e-learning tools, which can be reused in different educational settings, countries, courses. The project outputs will be used by or presented to the student and instructor target groups but more generally also to all stakeholders in the field of educational innovation and research on a European level. All technologies, models, algorithms, reports, guidelines, recommendations will be put at their disposal under open licenses.