Collecting and sharing attention information represents a main concern within the Technology Enhanced Learning community, as the number of works or projects related to this topic demonstrates it. Attention data are produced by various systems exploited by TEL actors during a learning session, thus raising a difficulty when it comes to collect traces translating all activities operated by learners, teachers or tutors. Thus, we propose an open framework based on a standardized and widely adopted approach to manage systems, networks and application. Our proposal is able to self-adapt to any attention information related to learning systems, resources and activities, and can meet the requirements of any web-based recommender systems. An implementation validates our approach by collecting and sharing attention data resulting from the manipulation of learning objects through heterogeneous applications.