A Learning Ecosystem for Linemen Training based on Big Data Components and Learning Analytics

Linemen training is mandatory, complex, and hazardous. Electronic technologies, such as virtual reality or learning management systems, have been used to improve such training, however these lack of interoperability, scalability, and do not exploit trace data generated by users in these systems. In this paper we present our ongoing work on developing a Learning Ecosystem for Training Linemen in Maintenance Maneuvers using the Experience API standard, Big Data components, and Learning Analytics. The paper describes the architecture of the ecosystem, elaborates on collecting learning experiences and emotional states, and applies analytics for the exploitation of both, legacy and new data. In the former, we exploit legacy e-Learning data for building a Domain model using Text Mining and unsupervised clustering algorithms. In the latter we explore self-reports capabilities for gathering educational support content, and assessing students emotional states. Results show that, a suitable domain model for personalizing maneuvers linemen training path can be built from legacy text data straightforwardly. Regarding self reports, promising results were obtained for tracking emotional states and collecting educational support material, nevertheless, more work around linemen training is required

Maternal Consumption of Non-Nutritive Sweeteners during Pregnancy Is Associated with Alterations in the Colostrum Microbiota

Non-nutritive sweeteners (NNSs) provide a sweet taste to foods and beverages without significantly adding calories. Still, their consumption has been linked to modifications in adult’s and children’s gut microbiota and the disruption of blood glucose control. Human milk microbiota are paramount in establishing infants’ gut microbiota, but very little is known about whether the consumption of sweeteners can alter it. To address this question, we sequenced DNA extracted colostrum samples from a group of mothers, who had different levels of NNS consumption, using the Ion Torrent Platform. Our results show that the “core” of colostrum microbiota, composed of the genera Bifidobacterium, Blautia, Cutibacteium, Staphylococcus, and Streptococcus, remains practically unchanged with the consumption of NNS during pregnancy, but specific genera display significant alterations, such as Staphylococcus and Streptococcus. A significant increase in the unclassified archaea Methanobrevibacter spp. was observed as the consumption frequency of NNS increased. The increase in the abundance of this archaea has been previously linked to obesity in Mexican children. NNS consumption during pregnancy could be related to changes in colostrum microbiota and may affect infants’ gut microbiota seeding and their future health