Recomendaciones para propiciar el aprendizaje colaborativo de estudiantes latinoamericanos de informática considerando estilos de aprendizaje dominante

This paper shows a case study with students of Computer Science related undergraduate programs that involved more than 300 university students from Argentina, Colombia and Mexico. For this purpose, a learning styles model of four dimension (Processing, Perception, Input ang Understanding) was applied by an online. The analysis shows that visual-active-sensing-sequential was the style dominant presented in the students of three countries. The analysis also revealed that there are not significant statistical differences for the four dimensions in students from Colombia and Mexico. However, learning styles of students from Argentina and Mexico show only significant differences in Understanding dimension. In contrast, there are significant statistical differences in Input and Understanding dimensions between students from Colombia and Argentina. Finally, this work provides recommendations so teachers and professors in these countries can adapt their practices to the dominant style identified and some suggestions for designers and developers of collaborative educational applications.El presente artículo describe un estudio de caso con más de 300 estudiantes de pregrado relacionados con informática, de Argentina, Colombia y México. Se aplicó un modelo de estilos de aprendizaje de cuatro dimensiones (Procesamiento, Percepción, Entrada y Comprensión) a través de un cuestionario en línea. El análisis identificó que el estilo dominante, en los tres países, fue visual-activo-sensitivo-secuencial. También se evidenció que no se presentan diferencias estadísticas significativas para las cuatro dimensiones entre estudiantes de Colombia y México. Sin embargo, para los estudiantes de Argentina y México se encontraron diferencias significativas en la dimensión Comprensión, mientras que entre Colombia y Argentina, se identificaron diferencias significativas en las dimensiones Entrada y Comprensión. Finalmente, se ofrecen recomendaciones para que los profesores puedan adaptar sus prácticas a ese estilo de aprendizaje dominante y se brindan sugerencias para los desarrolladores de aplicaciones educativas

Guidelines Based on Need-Findings Study and Communication Types to Design Interactions for MOOCs

Experts affirm that interaction in learning settings represent a necessary process for knowledge acquisition and cognitive development. In this vein, is crucial to ensure effective interaction and communication through the user interface of MOOCs. This work proposes a set of design guidelines as starting point for developers to integrate a set of interactive elements into the MOOCs' user interface oriented to foster the four basic types for communication in distance education. The design guidelines were conformed through a need-findings process (observing people-interviewing), in which 35 participants provided their user experience perceptions after using MOOCs from edX; Coursera; and Udacity. Obtained results suggest a particular set of interactive communication elements that should be incorporated in every MOOC's user interface

Usability Evaluation Trends in Ibero-American Countries

Usability evaluation is a major area of humancomputer interaction (HCI) in both academia and industry. According to Nielsen, on the Web, usability is a necessary condition for survival. If a website is difficult to use, people leave. If a homepage fails to clearly state what a company offers and what users can do on the site, people leave. If users get lost on a website, they leave. If a website’s information is hard to read or doesn’t answer users’ key questions, they leave. We conducted a survey in diverse Ibero-American countries of both academic institutions and software development companies to determine how usability evaluation is performed, taking into account methods, techniques, software tools, and usability laboratories

Driver eXperience (DX): Una aproximación a la interacción en el vehículo

User eXperience (UX) has been a growing area in different fields in the automotive industry. Vehicles have become a world full of opportunities that allow drivers to immerse themselves in an environment of connectivity, communication, information, and entertainment. Thus, it is important to start giving the user a more contextualized study, where he/she is not treated as a general individual, but instead for what he/she is, a driver, with specific features and actions. This paper presents a subjective definition of Driver eXperience (DX) and the factors to take into account to enhance the vehicle-driver relationship and therefore bring the driver a more comprehensive approach when performing in-vehicle tasks. Additionally, this paper presents a literature mapping of UX in the automotive field, intending to understand the relevance that could have the introduction of the DX term.La Experiencia de Usuario (UX) ha tenido un crecimiento en diferentes campos de la industria automotriz. Los vehículos se han convertido en un mundo lleno de oportunidades que le permiten al conductor sumergirse en un entorno de conectividad, comunicación, información y entretenimiento. Por lo tanto, es importante comenzar a darle al usuario un estudio más contextualizado, en donde no sea tratado como un individuo en general, sino más bien, por lo que realmente es, un conductor, con atributos y acciones propias de su rol en el vehículo. Este artículo presenta una definición subjetiva del término Driver eXperience (DX), y un conjunto de factores para tener en cuenta a fin de mejorar la relación vehículo-conductor, y así, brindarle al usuario una aproximación más comprensiva. Adicionalmente, se presenta un mapeo de la literatura de UX en la industria automotriz para entender la relevancia que puede tener introducir el término de DX

Hacia el desarrollo de un framework para el diseño de Sistemas Infotainment Automotrices: Primeras aproximaciones contextuales

The objective of this article was to establish the first contextual approaches for the development of a framework focused on the design of automotive infotainment systems. Therefore, a method was developed to identify a group of characteristics of the infotainment system interfaces that ensure a good user experience by the user, according to previous studies. Besides, the establishment of usability is proposed as a basis for the development of the tool.El objetivo de este artículo fue establecer las primeras aproximaciones contextuales para el desarrollo de un framework enfocado en el diseño de sistemas Infotainment automotrices. Por lo cual, se desarrolló un método para la identificación de un grupo de características de las interfaces de sistemas Infotainment que aseguren una buena experiencia de uso por parte del usuario, de acuerdo con estudios previos. Además, se plantea la usabilidad como base para el desarrollo de la herramienta

Depression Episodes Detection: A Neural Netand Deep Neural Net Comparison.

Depression is a frequent mental disorder. It is estimated thatit affects more than 300 million people in the world. In this investiga-tion, a motor activity database was used, from which the readings of 55patients (32 control patients and 23 patients with the condition) wereselected, during one week in one minute intervals, obtaining a total of385 observations (participants) and 1440 characteristics (time intervals)from which the most representative one minute intervals were extractedapplying genetic algorithms that reduced the number of data to process,with this strategy it is guaranteed that the most representative genes(characteristics) in the chromosome population is included in a singlemachine learning model of which applied deep neural nets and neuralnets with the aim of creating a comparative between the models gener-ated and determining which model offers better performance to detectingepisodes of depression. The deep neural networks obtained the best per-formance with 0.8086 which is equivalent to 80.86 % of precision, thisdeep neural network was trained with 270 of the participants which isequivalent to 70 % of the observations and was tested with 30 % Remain-ing data which is equal to 115 participants of which 53 were diagnosedas healthy and 40 with depression correctly. Based on these results, itcan be concluded that the implementation of these models in smart de-vices or in some assisted diagnostic tool, it is possible to perform theautomated detection of episodes of depression reliably.La depresión es un trastorno mental frecuente. Se estima que afecta a más de 300 millones de personas en el mundo. En esta investigación se utilizó una base de datos de actividad motora, de la cual se seleccionaron las lecturas de 55 pacientes (32 pacientes control y 23 pacientes con la condición), durante una semana en intervalos de un minuto, obteniendo un total de 385 observaciones (participantes) y 1440 características (intervalos de tiempo) de los cuales se extrajeron los intervalos de un minuto más representativos aplicando algoritmos genéticos que redujeron el número de datos a procesar, con esta estrategia se garantiza que los genes (características) más representativos de la población cromosómica se incluyan en un aprendizaje de una sola máquina modelo del cual se aplicó redes neuronales profundas y redes neuronales con el objetivo de crear una comparativa entre los modelos generados y determinar qué modelo ofrece mejor desempeño para detectar episodios de depresión. Las redes neuronales profundas obtuvieron el mejor desempeño con 0.8086 lo que equivale al 80.86% de precisión, esta red neuronal profunda fue entrenada con 270 de los participantes que es equivalente al 70% de las observaciones y se probó con el 30% de los datos restantes que es igual a 115 participantes de los cuales 53 fueron diagnosticados como sanos y 40 con depresión correctamente. En base a estos resultados, se puede concluir que la implementación de estos modelos en dispositivos inteligentes o en alguna herramienta de diagnóstico asistido, es posible realizar la detección automatizada de episodios de depresión de manera confiable

Removal of Te and Se anions in alkaline media in presence of cyanide by quaternary ammonium salts

Precious metals are currently associated with selenium (naumannite, Ag2Se) and tellurium (calaverite, AuTe2; sylvanite, (Au,Ag)2Te4) to form species refractory to cyanidation. The aim of this preliminary work was to study the use of the solvent extraction technique to recover tellurium and selenium ions from a synthetic solution similar to the cyanidation effluents to recycle the free cyanide back to the process. For the extraction of the Se and Te anions, the use of quaternary amines as extractants was evaluated (tallow trimethyl ammonium chloride, Quartamin TPR; hexadecyl trimethyl ammonium chloride, Amine F; and trioctyl methyl ammonium chloride, Aliquat 336) employing nonylphenol as a modifier in the organic phase (iso-octane). The results obtained showed that the extraction was strongly affected by the pH and that it was possible to recover up to 83% of Se and 10% of Te with Quartamin TPR from two synthetic solutions containing 23 mg/L of Te and 20 mg/L of Se with a molar cyanide:metal ratio of 1:4 at pH 11, a ratio of aqueous/organic (A/O) = 1 (V/V) and an extractant concentration of 0.022 mol/L. A maximum distribution coefficient (D) of 4.97 was obtained at pH 11. The McCabe-Thiele diagram indicates that two theoretical extraction stages are necessary to obtain a good extraction of Se complexes using Quartamin TPR.Precious metals are currently associated with selenium (naumannite, Ag2Se) and tellurium (calaverite, AuTe2; sylvanite, (Au,Ag)2Te4) to form species refractory to cyanidation. The aim of this preliminary work was to study the use of the solvent extraction technique to recover tellurium and selenium ions from a synthetic solution similar to the cyanidation effluents to recycle the free cyanide back to the process. For the extraction of the Se and Te anions, the use of quaternary amines as extractants was evaluated (tallow trimethyl ammonium chloride, Quartamin TPR; hexadecyl trimethyl ammonium chloride, Amine F; and trioctyl methyl ammonium chloride, Aliquat 336) employing nonylphenol as a modifier in the organic phase (iso-octane). The results obtained showed that the extraction was strongly affected by the pH and that it was possible to recover up to 83% of Se and 10% of Te with Quartamin TPR from two synthetic solutions containing 23 mg/L of Te and 20 mg/L of Se with a molar cyanide:metal ratio of 1:4 at pH 11, a ratio of aqueous/organic (A/O) = 1 (V/V) and an extractant concentration of 0.022 mol/L. A maximum distribution coefficient (D) of 4.97 was obtained at pH 11. The McCabe-Thiele diagram indicates that two theoretical extraction stages are necessary to obtain a good extraction of Se complexes using Quartamin TPR

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (ie, wifi, bluetooth, rfid, sound, movement, etc.). In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user’s location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set

Feature Selection Using Genetic Algorithms for the Generation of a Recognition and Classification of Children Activities Model Using Environmental Sound

In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy.In the area of recognition and classification of children activities, numerous works have been proposed that make use of different data sources. In most of them, sensors embedded in children’s garments are used. In this work, the use of environmental sound data is proposed to generate a recognition and classification of children activities model through automatic learning techniques, optimized for application on mobile devices. Initially, the use of a genetic algorithm for a feature selection is presented, reducing the original size of the dataset used, an important aspect when working with the limited resources of a mobile device. For the evaluation of this process, five different classification methods are applied, k-nearest neighbor (k-NN), nearest centroid (NC), artificial neural networks (ANNs), random forest (RF), and recursive partitioning trees (Rpart). Finally, a comparison of the models obtained, based on the accuracy, is performed, in order to identify the classification method that presents the best performance in the development of a model that allows the identification of children activity based on audio signals. According to the results, the best performance is presented by the five-feature model developed through RF, obtaining an accuracy of 0.92, which allows to conclude that it is possible to automatically classify children activity based on a reduced set of features with significant accuracy