Active learning and technology integration offer enhanced student engagement and adaptive learning, accommodating diverse preferences. This work uses fuzzy clustering method to analyze the data of students who answer questions on the MathE platform. To do this, the Fuzzy c-means algorithm was used, which allows flexibility and adaptability in the clustering partitioning, especially in situations where data elements may exhibit overlapping characteristics or belong to multiple categories. Thereby, two datasets are considered: the first is composed of 121 students who answered questions from the Vector Space subtopic, and the second dataset comprises the answers of 297 students who answered to any topic or subtopic of the platform. The results show that the fuzzy clustering method is appropriate for analyzing the student’s data since most students are highly associated with more than one cluster. Besides, the findings can support the formulation of intervention strategies to improve the student’s academic achievement.