There are several educational resources distributed in different repositories that address to a wide range of subjects and different educational goals. The proper choice of these educational resources is a challenge. Recommendation systems may help users in this task. In order to generate personalized recommendations, it is important to identify information that will help to define user profile and assist in identifying his/her interests. The constant and ever-increasing use of social networks allows the identification of different information about profile, interests, preferences, style and behavior from the spontaneous interaction. This paper presents an infrastructure able to extract users’ profile and educational context, from the Facebook social network and recommend educational resources. The proposal is supported by Information Extraction Techniques and Semantic Web technologies for extraction, enrichment and definition of user’s profile and interests. The recommendation approach is based on learning objects repositories, linked data and video repositories. It takes advantage of user’s spent time at the web. The proposal evaluation was made from the development of a prototype, three proofs of concept and a case study. The evaluation showed users’ acceptance of extracted information about their educational interests, automatically generated from social network and enriched to find implicit interests. It was also validated the possibility of people recommendation, enabling the establishment of interest network, based on a specific subject, showing good partners to study and research.

Dropping out of school comes from a long-term disengagement process with social and economic consequences. Being able to predict students' behavior earlier can minimize their failures and disengagement. This article presents the SASys architecture, based on a lexical approach and a polarized frame network. Its main goal is to define the author's sentiment in texts and increase the assertiveness of detecting the sentence's emotional state by adding authors' information and preferences. The author's emotional state begins with the phrase extraction from Virtual Learning Environments; then, pre-processing techniques are applied in the text, which is submitted to the complex frame network to identify words with polarity and the author's text sentiment. The flow ends with the identification of the author's emotional state. The proposal was evaluated by a case study, applying the Sentiment Analysis approach to the students' school dropout problem. The results point to the feasibility of the proposal for asserting the student's emotional state and detection of students' risks of dropout.