Toward Egocentric Network-based Learner Profiling in Adaptive E-learning Systems: A concept paper

Adaptive E-learning systems, which provide personalized learning experiences based on learner's specific characteristics (e.g. knowledge, skills, and competencies), are essential for developing effective learning and teaching. This paper presents a conceptual proposition consisting in developing adaptive E-learning system by incorporating egocentric network-based user profiling, an existing contribution of our research team. Actually, this contribution has been proved in the online social network context with empirical results on different social networks data (Facebook, Delicious, Twitter and DBLP). We aim to apply the existing algorithms underlying this contribution in E-learning context and present in this work a conceptual model and the construction process of learner's user profile, called "learner profile". The learner profile can be exploited in adaptive E-learning systems to provide different personalized/adapted services (books recommendation, personalized courses search, ...) to the learner.