The Internet of Things enables experts of given domains to create smart user experiences for interacting with the environment. However, development of such experiences requires strong programming skills, which are challenging to develop for non-technical users. This paper presents several extensions to the block-based programming language used in App Inventor to make the creation of mobile apps for smart learning experiences less challenging. Such apps are used to process and graphically represent data streams from sensors by applying map-reduce operations. A workshop with students without previous experience with Internet of Things (IoT) and mobile app programming was conducted to evaluate the propositions. As a result, students were able to create small IoT apps that ingest, process and visually represent data in a simpler form as using App Inventor’s standard features. Besides, an experimental study was carried out in a mobile app development course with academics of diverse disciplines. Results showed it was faster and easier for novice programmers to develop the proposed app using new stream processing blocks.

Systematic reviews are powerful methods used to determine the state-of-the-art in a given field from existing studies and literature. They are critical but time-consuming in research and decision making for various disciplines. When conducting a review, a large volume of data is usually generated from relevant studies. Computer-based tools are often used to manage such data and to support the systematic review process. This paper describes a comprehensive analysis to gather the required features of a systematic review tool, in order to support the complete evidence synthesis process. We propose a framework, elaborated by consulting experts in different knowledge areas, to evaluate significant features and thus reinforce existing tool capabilities. The framework will be used to enhance the currently available functionality of CloudSERA, a cloud-based systematic review tool focused on Computer Science, to implement evidence-based systematic review processes in other disciplines.

Los serious games o videojuegos educativos son videojuegos con propósitos de aprendizaje. Los jugadores realizan múltiples interacciones para aplicar las competencias desarrolladas. Información relevante sobre estas interacciones puede ser registrada como secuencias de eventos. Desafortunadamente, la evaluación en serious games es un procedimiento más complejo que el de otros instrumentos e-Learning debido a una serie de factores y particularidades, como el contexto del videojuego o su género. Además, los métodos de evaluación manuales presentan limitaciones para llevar a cabo un análisis detallado de grandes conjuntos de datos. La Minería de procesos (Process Mining) es un conjunto de técnicas de Análisis de Secuencias que permite extraer conocimiento de registros de eventos de manera automática. En esta publicación, proponemos aplicar técnicas de Process Mining para evaluar las interacciones realizadas dentro de un serious game. En el juego propuesto se trabajan competencias desarrolladas en la asignatura de Bases de Datos del Grado en Ingeniería Informática. Las interacciones del jugador son almacenadas en un registro de eventos el cual es usado por las técnicas de Process Mining para descubrir modelos. Finalmente, mostramos el análisis de los resultados obtenidos sobre el comportamiento de los jugadores, que muestra evidencias de diversas competencias del jugador. Caballero-Hernández, Juan Antonio; Palomo-Duarte, Manuel; Dodero, Juan Manuel; Person, Tatiana

The appropriate categorisation of written information by health professionals is very important to guarantee its accessibility. Unfortunately, the information technology tools that support professionals on that task imply a heavy workload, so that the responsibility for categorising the written content is often delegated to administrative staff. Well-known health ontologies such as SNOMED-CT or MeSH provide a representation of the clinical contents to be used by the information systems. This research proposes a computer based method to automatically extract and code the diagnostics, procedures and treatments according to health ontologies. A Knowledge Management System based on an extended version of Drupal is used to implement and evaluate this proposal. Results provide a positive evidence on the application of the method to support medical professionals.