The student performance has been affected for different factors, many of them are unobvious. The habits or daily activities undoubtly have a deep effects on the student performance. In this work, the study of student daily activities, and the relationship with his academic performance, using Data Mining techniques was done. In the attribute selection phase, 5-13 attributes from the 35 total were selected. The students were classified in four classes related with their academic performance: low, regular, good and high; the classification accuracy was near to 90%, using algorithms like MLP, KNN and tree algorithms like Random Forest, Random Tree and J48. The activities and factors presented for low and high performance students, also the tendency of activities and factors in the four classes, are reported.