Gamification is a viable strategy used to enhance motivation and engagement in programming classes among students. However, automated evaluation capabilities, which are crucial for giving students fast and correct feedback, are frequently lacking in currently available gamification technologies. This study proposes a novel web-based application that combines automated programming assessment features with gamification concepts; the aim is to provide students taking a programming course with an engaging learning environment. A survey conducted with 215 undergraduate students assessed how the tool affected the motivation and engagement of students. The findings show that the tool had a beneficial impact on students’ willingness to participate in class, study, increase their self-confidence, engage in healthy competition with peers, and learn from their mistakes. The qualitative feedback that students offered regarding the features of the tool that they liked best is also covered in the study. This paper contributes to the field of programming education by presenting a detailed gamified tool, incorporating automated evaluation and gamification in a web-based platform.

The dropout rate of engineering students is a concerning problem at the present time in many countries, resulting in difficulties to follow the demand of professionals in certain technological sectors as well as an associated negative economic impact. This study identifies a number of factors affecting the motivation degree of current electrical and computer engineering students, as well as the main reasons behind the dropout. A survey was completed by 624 students belonging to 8 different Spanish universities, rating the factors that could influence them to abandon their studies and those affecting their motivation to continue studying. Non-parametric analyses were performed in order to test the association of motivation as well as self-reported likelihood of dropping out with several variables. According to the research results, 23 of the 40 analyzed factors are correlated with the degree of motivation and 14 factors are correlated with the self-reported probability of dropping out. About 46% of the students declared to have thought about dropping out at some point in the past. Difficulty, followed by bad academic performance and negative relationships with professors are the main reasons for dropping out given by the students. Lack of vocation and distance to their home address were less frequent reasons.

The field of image analysis with artificial intelligence has grown exponentially thanks to the development of neural networks. One of its most promising areas is medical diagnosis through lung X-rays, which are crucial for diseases like pneumonia, which can be mistaken for other conditions. Despite medical expertise, precise diagnosis is challenging, and this is where well-trained algorithms can assist. However, working with medical images presents challenges, especially when datasets are limited and unbalanced. Strategies to balance these classes have been explored, but understanding their local impact and how they affect model evaluation is still lacking. This work aims to analyze how a class imbalance in a dataset can significantly influence the informativeness of metrics used to evaluate predictions. It demonstrates that class separation in a dataset impacts trained models and is a strategy deserving more attention in future research. To achieve these goals, classification models using artificial and deep neural networks implemented in the R environment are developed. These models are trained using a set of publicly available images related to lung pathologies. All results are validated using metrics obtained from the confusion matrix to verify the impact of data imbalance on the performance of medical diagnostic models. The results raise questions about the procedures used to group classes in many studies, aiming to achieve class balance in imbalanced data and open new avenues for future research to investigate the impact of class separation in datasets with clinical pathologies.

This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.

A compact dual-band equatorial helical antenna for TTC (telemetry, tracking, and control) applications in satellites is presented. The network feeding system of the antenna was also designed, analyzed, and measured to achieve circular polarization. A Moment Method code was used to evaluate the performance of both the helical antenna and the feed-circuit network. The whole system was optimized to fulfill rather strict specifications, while maintaining quite small size and weight. A prototype of the helical antenna was fabricated and measured. The design process of the geometrical model, and comparisons between the predicted and measured values, are presented. The results led to the conclusion that it is a very suitable antenna for operating at S band.