With the evolution of the application of Information and Communication Technologies (ICTs) in education was fostered the emergence of new methods, techniques and procedures that favor active learning, planning and management courses and support for overcoming difficulties in the educational process, be distance learning or presencial teaching. The Virtual Learning Environments (VLEs) have become fundamental to the conduct of educational processes, providing the democratization of education and enabling continuing education, as well as generating large volumes of data about the learning process. Have information about the learning process is of utmost importance for educators and students, as it allows to support decision making and reflection on the methodologies applied in education, used content and student performance. In this sense, this research proposes feature selection methodology for performance evaluation Massive Training Program students using data mining techniques. The proposed methodology considers identify attributes to be used for making inferences related to student performance and correlated with social aspects through qualitative and quantitative analysis of results. This methodology was developed considering the educational context and valuing diversity in the process. To demonstrate the feasibility of the proposed methodology was applied case study on hybrid environment of massive learning with proprietary databases from Telecentros.BR program provided by the managers of the program. In the case study was applied to feature selection methodology for Educational Data Mining, thus classification tasks were applied using the J48 algorithms, Random Forest and Random Tree to predict student grades; grouping tasks using the K-means algorithm to find profile of students based on the VLE usage logs and Self-Organized Maps (SOM) to find quality educational features from textual qualitative assessments. The results obtained through case study demonstrated the feasibility of the methodology considering the educational context and present new performance indicators to managers of Telecentros.BR program, such as profile use of AVA, evasion indicators, student profile.
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