Data Analysis Techniques Applied to the MathE Database

MathE is an international online platform that aims to provide a resource for in-class support as well as an alternative instrument to teach and study mathematics. This work focuses on the investigations of the students' behavior when answering the training questions available in the platform. In order to draw conclusions about the value of the platform, the ways in which the students use it and what are the most wanted mathematical topics, thus deepening the knowledge about the difficulties faced by the users and finding how to make the platform more efficient, the data collected since the it was launched (3 years ago) is analyzed through the use of data mining and machine learning techniques. In a first moment, a general analysis was performed in order to identify the students' behavior as well as the topics that require reorganization; it was followed by a second iteration, according to the students' country of origin, in order to identify the existence of differences in the behavior of students from distinct countries. The results point out that the advanced level of the platform's questions is not adequate and that the questions should be reorganized in order to ensure a more consistent support for the students' learning process. Besides, with this analysis it was possible to identify the topics that require more attention through the addition of more questions. Furthermore, it was not possible to identify significant disparities in the students behavior in what concerns the students' country of origin.