The paper addresses the analysis of data on students’ educational activities that determine their involvement in the educational process, aiming to enhance their efficiency and improve their academic performance. The Fuzzy C-Means algorithm was used to cluster students characterized by different levels of engagement. It is a fuzzy clustering algorithm that allows to consider the possibility of students belonging to multiple clusters with varying degrees of membership, which is essential in an educational context. The clustering problem was solved using a dataset containing various parameters related to the learning activities of 150 students. The dataset contained values of parameters such as course IDs, time spent in courses, assignments completed, participation scores, and levels of interest. The Fuzzy C-Means algorithm identified students categorized into clusters of high engagement, moderate engagement, and low engagement. It calculated the degree of membership for each student in each cluster. The visual representation of the degrees of membership facilitates a visual analysis of student engagement information and provides a more nuanced understanding of their engagement patterns. The results demonstrate the flexibility of fuzzy clustering, in particular Fuzzy C-Means, in analyzing educational data where students exhibit a variety of behaviors. The proposed approach offers significant insights into student engagement, facilitating targeted interventions to improve their academic performance.