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Currently, the improvement of core skills appears as one of the most significant educational challenges of this century. However, assessing the development of such skills is still a challenge in real classroom environments. In this context, Multimodal Learning Analysis techniques appear as an attractive alternative to complement the development and evaluation of core skills. This article presents an exploratory study that analyzes the collaboration and communication of students in a Software Engineering course, who perform a learning activity simulating Scrum with Lego® bricks. Data from the Scrum process was captured, and multidirectional microphones were used in the retrospective ceremonies. Social network analysis techniques were applied, and a correlational analysis was carried out with all the registered information. The results obtained allowed the detection of important relationships and characteristics of the collaborative and Non-Collaborative groups, with productivity, effort, and predominant personality styles in the groups. From all the above, we can conclude that the Multimodal Learning Analysis techniques offer considerable feasibilities to support the process of skills development in students.

Currently, one of the main challenges for information systems in healthcare is focused on support for health professionals regarding disease classifications. This work presents an innovative method for a recommendation system for the diagnosis of breast cancer using patient medical histories. In this proposal, techniques of natural language processing (NLP) were implemented on real datasets: one comprised 160, 560 medical histories of anonymous patients from a hospital in Chile for the following categories: breast cancer, cysts and nodules, other cancer, breast cancer surgeries and other diagnoses; and the other dataset was obtained from the MIMIC III dataset. With the application of word-embedding techniques, such as word2vec's skip-gram and BERT, and machine learning techniques, a recommendation system as a tool to support the physician's decision-making was implemented. The obtained results demonstrate that using word embeddings can define a good-quality recommendation system. The results of 20 experiments with 5-fold cross-validation for anamnesis written in Spanish yielded an F1 of 0.980 ± 0.0014 on the classification of 'cancer' versus 'not cancer' and 0.986 ± 0.0014 for 'breast cancer' versus 'other cancer'. Similar results were obtained with the MIMIC III dataset.

Smart Grid constitutes the next generation of electricity delivery systems that intend to enhance reliability, efficiency, and security of the power grid. A fundamental component of this intelligent network is the Advanced Metering Infrastructure (AMI), which provides a two-way communication network between Utilities and a collection of smart meters located at the customers side. The interconnected meters form Neighborhood Area Networks that provide a platform for the deployment of customized AMI applications. In this paper, we study the operation of AMI in Colombia. While several Utilities are implementing first approximations to AMI that mostly focus on automated consumption readings, there is no certainty that traffic of different natures, corresponding to future AMI applications, can be supported by these initial deployments. As there is a lack of study of these issues, we carry out an extensive performance evaluation through simulations of current technologies delivering traffic from future AMI applications. Our simulations are based on an extensive review of the communication technologies that are currently employed for AMI in Colombia, and on a characterization of the future AMI applications. With our study we pursuit a better understanding of the primary challenges that will have to be faced for the development and enhancement of AMI networks in the country.

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