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.