The generation of individualized exams can contribute to a more reliable assessment of the students. Manually performing this procedure may not be feasible, even more on a large scale. An alternative to deal with it is the automatic generation of questions. This paper discusses an innovative solution to simplify test generation and correction through parameterized questions in the context of a four-month Introduction to Programming course under a blended- learning (IP-BL) approach. It combines the open-source tool MCTest with Moodle and VPL plugin to generate and also automatically evaluate parameterized programming language questions. We applied an intervention based on this solution in two IP-BL groups (a total of 171 enrolled students) using Java.

This paper presents the development of a computer vision application based on the YOLOv8 network, designed to assist the navigation of autonomous vehicles on rural roads, particularly those found in sugarcane fields. The application employs instance segmentation to differentiate between navigable and non-navigable areas and detect obstacles such as pedestrians, vehicles, and other potential hazards. This information is used to generate an occupancy map that helps the navigation planner identify the safest and most efficient routes. The system was trained on a dataset containing 1.018 images, and the results demonstrate that instance segmentation significantly enhances the precision and safety of autonomous navigation in complex rural environments. The proposed approach is compatible with the ROS2 framework, using its structure for data integration and enabling real-time decision making.

In many areas of knowledge it has always been a challenge to evaluate students efficiently. Considering that we are all undergoing a pandemic period, efficient evaluations are necessary and urgent. In our paper we followed the main objective of adapting MCTest. Namely, a web platform devoted to generate and correct individualized exams automatically. We have addressed the problem of distance student evaluation by profiting MCTest. As a result it provides a solution that is free of charge and enables creating parametric questions with LaTeX and Python. The automatic correction is carried out with Google Forms and Sheets, namely our original contribution. The adapted solution was successfully applied to a Calculus class with 100 students.