The success of Web 2.0 inspires e-learning to evolve into e-learning 2.0, which exploits collective intelligence to achieve user-centric learning. However, searching for suitable learning paths and content for achieving a learning goal is time consuming and troublesome on e-learning 2.0 platforms. Therefore, introducing formal learning in these platforms to provide learning guidance is important. Adaptive learning mechanisms are useful to provide learning guidance based on individual differences. However, most adaptive learning systems provide learning paths and content based on the views of a few designers or experts. To tackle these problems this research proposes a user-centric adaptive learning system (UALS) that uses sequential pattern mining to construct adaptive learning paths based on users' collective intelligence and employs Item Response Theory (IRT) with collaborative voting approach to estimate learners' abilities for recommending adaptive materials. The experimental results show that the effectiveness of user-centric adaptive learning is comparable to expert-designed learning and learners are more satisfied and learn efficiently. The guidelines to design e-learning 2.0 platforms are also proposed. (Contains 9 tables and 2 figures.)