Mathematics Game e-Library for Elementary School, study case: Mexico

One of the most relevant subjects for the intellectual formation of elementary school students is Mathematics where its importance goes back to ancient civilizations and which its importance is underestimated nowadays. This phenomenon occurs in Mexico, where 63.1% of the total population of elementary school students between the third and sixth grade have insufficient/elemental level of mathematics knowledge. This has resulted in the need to use a new mechanism to complement student’s classroom learning. With the rapid growth of wireless and mobile technologies, the mobile learning has been gradually considered as a novel and effective form of learning due to it inherits all the advantages of e-learning as well as breaks the limitations of learning time and space occurring in the traditional classroom teaching. This project proposes the use of a Mathematics Game e-Library integrated by a set of games for mobile devices and a distribution/management tool. The games are developed for running on mobile devices and for cover the six competencies related with the mathematics learning approach established in Mexico. The distribution/management tool allows students to reach contents according to their needs; this is achieved through a core engine that infers, from an initial profile, the games that cover the user’s knowledge gaps.

Análisis de la satisfacción del alumnado de L2 con respecto a la adquisición de terminología especializada por medio de videojuegos: estudio de caso

Los videojuegos permiten enseñar contenidos y destrezas de forma eficiente, posibilitando un aprendizaje duradero (Rama et al., 2012), y aumentan la motivación y la implicación del alumnado (Martens et al., 2004). En esta línea, el presente estudio pretende medir tanto el grado de satisfacción de dos grupos de estudiantes de L2 de la Universidad de Alicante con respecto a la adquisición de terminología especializada por medio de un videojuego como la percepción sobre el propio grado de aprendizaje. Tras un periodo de práctica, se ha medido y analizado tanto el grado de aprendizaje alcanzado como la satisfacción con la herramienta empleada y, muy especialmente, las diferencias en el grado de aprendizaje percibido por cada uno de estos grupos.

Lecciones aprendidas gamificando cuando aún no se llamaba gamificación

El término gamificación está de moda. Los gurús la sitúan como una tecnología emergente y disruptiva, que cambiará muchas de nuestras experiencias en campos tan alejados de los juegos como el empresarial, el marketing y la relación con los clientes. Y el entorno educativo no escapará a ello. En este artículo presentamos la experiencia de un grupo de profesores preocupados por la docencia, que llevamos años experimentando con los videojuegos y las experiencias lúdicas, y que de repente nos hemos encontrado con el término gamificación. Estas son las lecciones que hemos aprendido, que podemos enmarcar en el campo de la gamificación en educación, pero que derivan de una experiencia práctica, de un análisis desmenuzado y de una reflexión concienzuda. Pretendemos mostrar qué es lo realmente importante y qué puntos debemos tener en cuenta los profesores antes de lanzarnos al diseño gamificado de nuestra propuesta docente.

Contributions to 3D Data Registration and Representation

Nowadays, new computers generation provides a high performance that enables to build computationally expensive computer vision applications applied to mobile robotics. Building a map of the environment is a common task of a robot and is an essential part to allow the robots to move through these environments. Traditionally, mobile robots used a combination of several sensors from different technologies. Lasers, sonars and contact sensors have been typically used in any mobile robotic architecture, however color cameras are an important sensor due to we want the robots to use the same information that humans to sense and move through the different environments. Color cameras are cheap and flexible but a lot of work need to be done to give robots enough visual understanding of the scenes. Computer vision algorithms are computational complex problems but nowadays robots have access to different and powerful architectures that can be used for mobile robotics purposes. The advent of low-cost RGB-D sensors like Microsoft Kinect which provide 3D colored point clouds at high frame rates made the computer vision even more relevant in the mobile robotics field. The combination of visual and 3D data allows the systems to use both computer vision and 3D processing and therefore to be aware of more details of the surrounding environment. The research described in this thesis was motivated by the need of scene mapping. Being aware of the surrounding environment is a key feature in many mobile robotics applications from simple robotic navigation to complex surveillance applications. In addition, the acquisition of a 3D model of the scenes is useful in many areas as video games scene modeling where well-known places are reconstructed and added to game systems or advertising where once you get the 3D model of one room the system can add furniture pieces using augmented reality techniques. In this thesis we perform an experimental study of the state-of-the-art registration methods to find which one fits better to our scene mapping purposes. Different methods are tested and analyzed on different scene distributions of visual and geometry appearance. In addition, this thesis proposes two methods for 3d data compression and representation of 3D maps. Our 3D representation proposal is based on the use of Growing Neural Gas (GNG) method. This Self-Organizing Maps (SOMs) has been successfully used for clustering, pattern recognition and topology representation of various kind of data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models without considering time constraints. Self-organising neural models have the ability to provide a good representation of the input space. In particular, the Growing Neural Gas (GNG) is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation. However, this type of learning is time consuming, specially for high-dimensional input data. Since real applications often work under time constraints, it is necessary to adapt the learning process in order to complete it in a predefined time. This thesis proposes a hardware implementation leveraging the computing power of modern GPUs which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). Our proposed geometrical 3D compression method seeks to reduce the 3D information using plane detection as basic structure to compress the data. This is due to our target environments are man-made and therefore there are a lot of points that belong to a plane surface. Our proposed method is able to get good compression results in those man-made scenarios. The detected and compressed planes can be also used in other applications as surface reconstruction or plane-based registration algorithms. Finally, we have also demonstrated the goodness of the GPU technologies getting a high performance implementation of a CAD/CAM common technique called Virtual Digitizing.