Potenciando el aprendizaje proactivo con ILIAS&WebQuest: aprendiendo a paralelizar algoritmos con GPUs

Arquitectura de Computadores es una asignatura troncal de segundo ciclo de la titulación de Ingeniería de Telecomunicación (P.E. 2004) de la Universidad de Jaén, que desde el curso académico 2009/10 cuenta con una metodología de aprendizaje proactivo para motivar al alumno en la realización de las prácticas. En concreto, se ha abordado la enseñanza de la materia de paralelización de algoritmos haciendo uso de GPUs de tarjetas gráficas convencionales. Además, se ha dado soporte telemático al profesorado y alumnado de la asignatura mediante el uso de plataformas web de e-learning como ILIAS y otras como WebQuest. Por último, en este trabajo se presentan algunos de los resultados alcanzados con esta experiencia.Peer Reviewe

Desarrollo y empleo de juegos educativos on-line destinados al auto-entrenamiento y auto-evaluación

La implantación del EEES, está originando cambios en el modelo docente, cada vez más orientado al trabajo autónomo del alumno. Los contenidos electrónicos que apoyan el proceso de aprendizaje también deben, por tanto, orientarse a facilitar el aprendizaje autónomo, como por ejemplo los procesos de autoevaluación y auto-entrenamiento por parte del estudiante. Por ello, parece adecuado desarrollar contenidos electrónicos que motiven y a la vez entretengan de forma amena al alumnado durante su aprendizaje autónomo. En esta contribución, proponemos una serie de juegos educativos electrónicos como herramienta que haga de la auto-evaluación y del autoentrenamiento un proceso ameno y entretenido.Peer Reviewe

Decision Analysis Linguistic Framework

Everyday human beings are faced with situations they should choose among different alternatives by means of reasoning and mental processes when solving a problem. Many of these decision problems are under uncertain environments including vague, imprecise and subjective information that is usually modeled by linguistic information due to the use of natural language and its relation to mental reasoning processes of the experts when expressing their judgments. In a decision process multiple criteria can be evaluated which involving multiple experts with different degrees of knowledge. Such process can be modeled by using Multi-granular Linguistic Information (MGLI) and Computing with Words (CW) processes to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish this process in an accurate and interpretable way. In this paper we propose a useful Decision Analysis Framework to manage this kind of problems by using the Extended Linguistic Hierarchy (ELH), 2-tuples linguistic representation model and its computational method. The developed Framework has many advantages when dealing with a complex problem in a simple way and its capability of having easy and useful reasonably results.Sociedad Argentina de Informática e Investigación Operativ

Activity Recognition for IoT Devices Using Fuzzy Spatio-Temporal Features as Environmental Sensor Fusion

The IoT describes a development field where new approaches and trends are in constant change. In this scenario, new devices and sensors are offering higher precision in everyday life in an increasingly less invasive way. In this work, we propose the use of spatial-temporal features by means of fuzzy logic as a general descriptor for heterogeneous sensors. This fuzzy sensor representation is highly efficient and enables devices with low computing power to develop learning and evaluation tasks in activity recognition using light and efficient classifiers. To show the methodology's potential in real applications, we deploy an intelligent environment where new UWB location devices, inertial objects, wearable devices, and binary sensors are connected with each other and describe daily human activities. We then apply the proposed fuzzy logic-based methodology to obtain spatial-temporal features to fuse the data from the heterogeneous sensor devices. A case study developed in the UJAmISmart Lab of the University of Jaen (Jaen, Spain) shows the encouraging performance of the methodology when recognizing the activity of an inhabitant using efficient classifiers

Decision Analysis Linguistic Framework

Everyday human beings are faced with situations they should choose among different alternatives by means of reasoning and mental processes when solving a problem. Many of these decision problems are under uncertain environments including vague, imprecise and subjective information that is usually modeled by linguistic information due to the use of natural language and its relation to mental reasoning processes of the experts when expressing their judgments. In a decision process multiple criteria can be evaluated which involving multiple experts with different degrees of knowledge. Such process can be modeled by using Multi-granular Linguistic Information (MGLI) and Computing with Words (CW) processes to solve the related decision problems. Different methodologies and approaches have been proposed to accomplish this process in an accurate and interpretable way. In this paper we propose a useful Decision Analysis Framework to manage this kind of problems by using the Extended Linguistic Hierarchy (ELH), 2-tuples linguistic representation model and its computational method. The developed Framework has many advantages when dealing with a complex problem in a simple way and its capability of having easy and useful reasonably results.Sociedad Argentina de Informática e Investigación Operativ

Collection of a Diverse, Realistic and Annotated Dataset for Wearable Activity Recognition

This paper discusses the opportunities and challenges associated with the collection of a large scale, diverse dataset for Activity Recognition. The dataset was collected by 141 undergraduate students, in a controlled environment. Students collected triaxial accelerometer data from a wearable accelerometer whilst each carrying out 3 of the 18 investigated activities, categorized into 6 scenarios of daily living. This data was subsequently labelled, anonymized and uploaded to a shared repository. This paper presents an analysis of data quality, through outlier detection and assesses the suitability of the dataset for the creation and validation of Activity Recognition models. This is achieved through the application of a range of common data driven machine learning approaches. Finally, the paper describes challenges identified during the data collection process and discusses how these could be addressed. Issues surrounding data quality, in particular, identifying and addressing poor calibration of the data were identified. Results highlight the potential of harnessing these diverse data for Activity Recognition. Based on a comparison of six classification approaches, a Random Forest provided the best classification (F-measure: 0.88). In future data collection cycles, participants will be encouraged to collect a set of “common” activities, to support generation of a larger homogeneous dataset. Future work will seek to refine the methodology further and to evaluate model on new unseen data.</p

A Method Based on AHP to Define the Quality Model of QuEF

QuEF is a framework to analyze and evaluate the quality of ap proaches based on Model-Driven Web Engineering (MDWE). In this frame work, the evaluation of an approach is calculated in terms of a set of informa tion needs and a set of quality characteristics. The information needs are requirements demanded by users of approaches. On the other hand, the quality characteristics are specific aspects that the approaches provide to their users. In these lines, there is a gap in the importance of each quality characteristic in the QuEF and the degree of coverage of each information need regarding the quali ty characteristics. In this contribution, we propose a method to define the Quali ty Model within QuEF. This method is based on the Analytic Hierarchy Process in order to establish the importance of the quality characteristics and the degree of coverage of each requirement of the information needs regarding the set of quality characteristics. Furthermore, a software application that develops the proposed method is presented.Ministerio de Ciencia e Innovación TIN2009-08286Junta de Andalucía P08-TIC-3548Ministerio de Ciencia y Tecnología TIN2010-20057-C03-02Ministerio de Educación y Ciencia TIN2010-12312-EJunta de Andalucía TIC-578

Auto-entrenamiento y auto-evaluación a través de juegos educativos

La implantaci&oacute;n del EEES, est&aacute; originando cambios en el modelo docente, cada vez m&aacute;s orientado al trabajo aut&oacute;nomo del alumno. Los contenidos electr&oacute;nicos que apoyan el proceso de aprendizaje (e-Learning) tambi&eacute;n deben, por tanto, orientarse a facilitar el aprendizaje aut&oacute;nomo, como por ejemplo los procesos de auto-evaluaci&oacute;n y auto-entrenamiento por parte del estudiante. Por ello, parece adecuado desarrollar contenidos electr&oacute;nicos que motiven y a la vez entretengan de forma amena al alumnado durante su aprendizaje aut&oacute;nomo. En esta contribuci&oacute;n, proponemos una serie de juegos educativos electr&oacute;nicos como herramienta que haga de la auto-evaluaci&oacute;n y del auto-entrenamiento un proceso ameno y entretenido, incluy&eacute;ndolos en paquetes SCORM que faciliten su transferencia entre diferentes plataformas electr&oacute;nicas de aprendizaje