An Education Profile Model Applied in the Context of Recommender Systems

The systems used in education have evolved bringing new teaching approaches and impacting in traditional learning methods. Also, they present new scenarios to students aiming to improve activities and practices in online systems. Along with this evolution the growing availability of contents has promoting an overload of data. Nowadays, many online educational systems have too much information, content and activities available to students making choices difficult. Educational systems supported by recommender systems allow to offer contents toward the needs and profiles of the students. This work presents a student profile model based on students’ preferences and their interactions in online educational systems, with the purpose of allowing the recommendation of contents. To demonstrate the feasibility and evaluation of the proposed model two study scenarios were developed with information from an educational institution. In these scenarios recommendations were carried out using collaborative filtering and content based approaches. From the evaluation of these scenarios, the results demonstrate the viability of the model in producing recommendations that aim to aid the students’ learning process when interacting with online educational systems.