The objective of any tutoring system is to provide resources to learners that are adapted to their current state of knowledge. With the availability of a large variety of on-line contents, and the disjunctive nature of results provided by traditional search engines, it becomes crucial to provide learners with adapted learning paths that propose a sequence of resources that match their learning objectives. In an ideal case, the sequence of documents provided to the learner should be such that each new document relies on concepts that have been already defined in previous documents. Thus, the problem of determining an effective learning path from a corpus of web documents depends on the accurate identification of outcome and prerequisite concepts in these documents and on their ordering according to this information. Until now, only few works have been proposed to distinguish between prerequisite and outcome concepts and to the best of our knowledge no method have been introduced so far to benefit from this information to produce meaningful learning path. To this aim, this paper first describes a concept annotation method that relies on machine learning techniques to predict the class of each concept: prerequisite or outcome, on the basis of contextual and local features. Then, this categorization is exploited to produce an automatic resources sequencing on the basis of different representations and scoring functions that transcribe the precedence relation between learning resources. Experiments conducted on a real data set built from on-line resources show that our concept annotation approach outperforms the baseline method and that the learning paths automatically generated are consistent with the ground truth provided by the author of the on-line contents.