A data-logging mechanism to support e-learning systems

User profiling and e-learning have received great attention in the last years. In a learning environment, user profiling provides historical data of the students´ performance on different learning subjects. e-Learning tools such as serious games can collect user´s data and build a user profile by appropriate storing these information. Thus, by combining above techniques and analysing each user´s data a teacher can provide personalized treatment to her/his students. In this paper we present our solution on storing and analysing user data on a centralized server. Our system stores data collected from users with dyslexia. We maintain one user profile per student from data taken from a) user responses on a screening test and b) from user´s performance on a set of serious games. The learning process then depends on the above data, so it is important to store and analyse our resources efficiently while respecting the user´s privacy. Our solution is based on the usage of the cube model in parallel with a traditional, table-based storage of log data. The services we present are part of the iLearnRW1 project, aiming to support gamified learning for children with dyslexia.