Big data is an emerging field of research and many organizations especially governments, business and health care currently use them to make informed decisions. Millions of generated data are unstructured and come in a variety of formats. Nowadays, data technologies make possible to collect and store large and complex amounts of data and then to turn them into meaningful patterns. In higher education, there is limited research into big data despite it would bring opportunities to inform decisions and to identify potential issues related to policies, programs, teaching, learning and research. When learners interact with a digital device (smartphone, tablet or laptop) data about their interactions are easily "logged" and so can be used for further analysis. The trails that students leave behind can be analyzed and could reveal social connections, behaviors, preferences and goals. A large field of research opens to identify patterns and deviations from patterns, addressing some of the key challenges facing higher education. Spanish Universities have deployed different platforms to support teaching and learning. An important part of the teachers still uses the digital environment as a sort of digital photocopy service. However, some teachers make an advanced use of the virtual campus with tasks, exams, wikis and forums and other activities. The Universidad Complutense of Madrid has started some actions to allow the analysis of the students' interactions and to examine patterns of students' performance. We describe in this paper one of these pilot experiences focused on Human Histology, a subject in the second year of the Medicine Degree. Three are the main aims of this experience. First of all, to propose a working methodology to gather the data, to define the metrics and to set up the indicators that may be useful to identify patterns in the undergraduate students' learning. Secondly, to evaluate the results in order to make better decisions that achieve better outcomes. Finally, to reflect on the metrics and indicators that could be useful to measure in other subjects and knowledge areas.
