This paper presents data for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. The data contains 17 attributes and 2111 records, the records are labeled with the class variable NObesity (Obesity Level), that allows classification of the data using the values of Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III. 77% of the data was generated synthetically using the Weka tool and the SMOTE filter, 23% of the data was collected directly from users through a web platform. This data can be used to generate intelligent computational tools to identify the obesity level of an individual and to build recommender systems that monitor obesity levels. For discussion and more information of the dataset creation, please refer to the full-length article “Obesity Level Estimation Software based on Decision Trees” (De-La-Hoz-Correa et al., 2019).

A set of new types of assessment is required for learning management systems (LMSs), and there is a need for a way to assess lifelong adaptive competencies. Proposed solutions to these problems need to preserve the interoperability, reusability, efficiency and abstract modeling already present in LMSs. This paper introduces a set of software tools for an author assessment package on the LMS Moodle being developed as part of adaptive e-learning engine architecture (AEEA). The principal features of this set are: 1) The set avoid editing items for a 360-degree feedback evaluation, 2) Whole items and tests are linked to levels of competencies acquisition, 3) The competency-based eassessment data model are based on e-learning specification and complemented with XML data on the appraised competencies, 4) Items and tests are storage in repositories, and 5) The tools are integrated within Moodle to facilitate the design of an assessment plan.

Using sensors and mobile devices integrated with hardware and software tools for Human Recognition Activities (HAR), is a growing scientific field, the analysis based on this information have promising benefits to detect regular and irregular behaviors in individuals during their daily activities. In this study, the Van Kasteren dataset was used for the experimental stage, and it all data was processed using the data mining classification methods: Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB). These methods were applied during the training and validation processes with the proposed methodology, and the results obtained showed that all these three methods were successful to identify the cluster associated to the activities contained in the Van Kasteren dataset. The Support Vector Machines (SVM) method showed the best results with the evaluation metrics: True Positive Rate (TPR) 99.2%, False Positive Rate (FPR) 0.6%, precision (99.2%), coverage (99.2%) and F-Measure (98.8%).