Usage of new technologies in educational scope raises several questions about the efficiency of these approaches and which beneĄts they provide to the academic Ąeld. Investigations in this area cover a line of research called Learning Analytics and, in the literature, many papers that analyze new technological proposals are only aimed at observing improvements that the use of tools can cause. Such researches do not analyze whether the sample size is robust to ensure reliability of results or whether the tool enhancement tends to maintain some inĆuence over stu-dentsŠ performance. Based on this, this thesis determined an optimal sample size of 25 students for the performance analysis of students who do not use teaching support technologies and of 20 students for classes in contact with educational platforms. An Experiments Manager was also developed to organize the visibility of Classroom eXperience (CX) platform functionalities and, using this Experiments Manager, a Factorial Analysis of Variance and a Correlation Analysis were performed. It was observed that studentsŠ performance was inĆuenced by the interaction between CX functionalities and the courses taken by students. In all undergraduate classes, there were signiĄcant increases in student performance in a comparison between the absence of CX and its use with the platform functionalities. Theoretical and mathematical undergraduate courses also presented moderate correlations between the platform usage level and studentsŠ performance. Thus, the platform usage positively inĆuenced the grades of undergraduate students and it was inferred that students who interacted more with CX also obtained the best grades in their classes. In graduate classes, there was no signiĄcant difference in students performance between CX levels of usage, nor the occurrence of correlations that indicated something similar to what happened with undergraduates, although there have also been increases in studentsŠ performance at this academic level.