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Monitoring is understood as a teaching modality that enhances the learning of university students. This work aims to analyze the average of the final grades of students of the Cartographic and Surveying Engineering undergraduate course in four cases: with three monitoring schedules, with two monitoring, with one monitoring and without monitoring; thus, verifying the occurrence of improvement in students' learning according to their final average grade. The students' means were analyzed by analysis of variance (ANOVA) and were compared using the Tukey´s test. 5% was considered as the level of significance. The statistical computational system R was used for all analyzes. We found that monitoring is of essential importance in the lives of academics, its inclusion provided an increase in the average grade of students in the Statistics and Probability component in the Cartographic and Surveying Engineering undergraduate course. We can say that monitoring is a factor that influences the average of students.

Depending on the speed, the wind has beneficial potential in the pollination of plants, in the generation of energy, in the maintenance of temperature, but when it exceeds a certain level it becomes dangerous and destructive, and can cause damage to buildings, plantations and navigation. The theory of extreme values plays a key role in modeling events associated with very small probabilities or rare events. Probabilistic models based on this theory aim to predict, from a set of maximum values of an environmental process recorded in a relatively short period of time, the maximum values expected in a longer period of time, which for the specific case of wind, is of great utility, for example, in the choice of the cultivar to be sown. This work consisted in adjusting the generalized distribution of extreme values (GVE) to the maximum monthly wind speed data recorded over a period of 54 years (1961 to 1983 and 1989 to 2015) in Uruguaiana, State of Rio Grande do Sul. The adjustment to the data was evaluated through the Kolmogorov-Smirnov test. The generalized distribution of extreme values with their parameters estimated by the maximum likelihood method presented a satisfactory adjustment to the data.