An online e-Learning authoring tool to create interactive multi-device learning objects using e-Infrastructure resources

Education can take advantage of e-Infrastructures to provide teachers with new opportunities to increase students' motivation and engagement while they learn. Nevertheless, teachers need to find, integrate and customize the resources provided by e-Infrastructures in an easy way. This paper presents ViSH Editor, an innovative web-based e-Learning authoring tool that aims to allow teachers to create new learning objects using e-Infrastructure resources. These new learning objects are called Virtual Excursions and are created as reusable, granular and interoperable learning objects. This way they can be reused to build new ones and they can be integrated in websites or Learning Management Systems. Details about the design, development and the tool itself are explained in this paper as well as the concept, structure and metadata of the new learning objects. Lastly, some real examples of how to enrich learning using Virtual Excursions are exposed.

Facilitating the creation of interactive multi-device Learning Objects using an online authoring tool

Learning Objects facilitate reuse leading to cost and time savings as well as to the enhancement of the quality of educational resources. However, teachers find it difficult to create or to find high quality Learning Objects, and the ones they find need to be customized. Teachers can overcome this problem using suitable authoring systems that enable them to create high quality Learning Objects with little effort. This paper presents an open source online e-Learning authoring tool called ViSH Editor together with four novel interactive Learning Objects that can be created with it: Flashcards, Virtual Tours, Enriched Videos and Interactive Presentations. All these Learning Objects are created as web applications, which can be accessed via mobile devices. Besides, they can be exported to SCORM including their metadata in IEEE LOM format. All of them are described in the paper including an example of each. This approach for creating Learning Objects was validated through two evaluations: a survey among authors and a formal quality evaluation of 209 Learning Objects created with the tool. The results show that ViSH Editor facilitates educators the creation of high quality Learning Objects.

Enhanced recommendations for e-learning authoring tools based on a proactive context-aware recommender

Authoring tools are powerful systems in the area of e-Learning that make easier for teachers to create new learning objects by reusing or editing existing educational resources coming from learning repositories or content providers. However, due to the overwhelming number of resources these tools can access, sometimes it is difficult for teachers to find the most suitable resources taking into account their needs in terms of content (e.g. topic) or pedagogical aspects (e.g. target level associated to their students). Recommender systems can take an important role trying to mitigate this problem. In this paper we propose a new model to generate proactive context-aware recommendations on resources during the creation process of a new learning object that a teacher carries out by using an authoring tool. The common use cases covered by the model for having recommendations in online authoring tools and details about the recommender model itself are presented.

Incorporating proactivity to context-aware recommender systems for e-learning

Recommender systems in e-learning have proved to be powerful tools to find suitable educational material during the learning experience. But traditional user request-response patterns are still being used to generate these recommendations. By including contextual information derived from the use of ubiquitous learning environments, the possibility of incorporating proactivity to the recommendation process has arisen. In this paper we describe methods to push proactive recommendations to e-learning systems users when the situation is appropriate without being needed their explicit request. As a result, interesting learning objects can be recommended attending to the user?s needs in every situation. The impact of this proactive recommendations generated have been evaluated among teachers and scientists in a real e-learning social network called Virtual Science Hub related to the GLOBAL excursion European project. Outcomes indicate that the methods proposed are valid to generate such kind of recommendations in e-learning scenarios. The results also show that the users' perceived appropriateness of having proactive recommendations is high.

A model for integrating learning object repository resources into web videoconference services

Reusing Learning Objects saves time and reduce development costs. Hence, achieving their interoperability in multiple contexts is essential when creating a Learning Object Repository. On the other hand, novel web videoconference services are available due to technological advancements. Several benefits can be gained by integrating Learning Objects into these services. For instance, they can allow sharing, co-viewing and synchronized co-browsing of these resources at the same time that provide real time communication. However, several efforts need to be undertaken to achieve the interoperability with these systems. In this paper, we propose a model to integrate the resources of the Learning Object Repositories into web videoconference services. The experience of applying this model in a real e-Learning scenario achieving interoperability with two different web videoconference services is also described.

An architecture for integrating learning object repository resources into web videoconference services

Reusing Learning Objects saves time and reduce development costs. Hence, achieving their interoperability in multiple contexts is essential when creating a Learning Object Repository. On the other hand, novel web videoconference services are available due to technological advancements. Several benefits can be gained by integrating Learning Objects into these services. For instance, they can allow sharing, co-viewing and synchronized co-browsing of these resources at the same time that provide real time communication. However, several efforts need to be undertaken to achieve the interoperability with these systems. In this paper, we propose a model to integrate the resources of the Learning Object Repositories into web videoconference services. The experience of applying this model in a real e-Learning scenario achieving interoperability with two different web videoconference services is also described.

A flexible open source web platform to facilitate Learning Object evaluation

Systematic evaluation of Learning Objects is essential to make high quality Web-based education possible. For this reason, several educational repositories and e-Learning systems have developed their own evaluation models and tools. However, the differences of the context in which Learning Objects are produced and consumed suggest that no single evaluation model is sufficient for all scenarios. Besides, no much effort has been put in developing open tools to facilitate Learning Object evaluation and use the quality information for the benefit of end users. This paper presents LOEP, an open source web platform that aims to facilitate Learning Object evaluation in different scenarios and educational settings by supporting and integrating several evaluation models and quality metrics. The work exposed in this paper shows that LOEP is capable of providing Learning Object evaluation to e-Learning systems in an open, low cost, reliable and effective way. Possible scenarios where LOEP could be used to implement quality control policies and to enhance search engines are also described. Finally, we report the results of a survey conducted among reviewers that used LOEP, showing that they perceived LOEP as a powerful and easy to use tool for evaluating Learning Objects.

Towards a Learning Object pedagogical quality metric based on the LORI evaluation model

Evaluating and measuring the pedagogical quality of Learning Objects is essential for achieving a successful web-based education. On one hand, teachers need some assurance of quality of the teaching resources before making them part of the curriculum. On the other hand, Learning Object Repositories need to include quality information into the ranking metrics used by the search engines in order to save users time when searching. For these reasons, several models such as LORI (Learning Object Review Instrument) have been proposed to evaluate Learning Object quality from a pedagogical perspective. However, no much effort has been put in defining and evaluating quality metrics based on those models. This paper proposes and evaluates a set of pedagogical quality metrics based on LORI. The work exposed in this paper shows that these metrics can be effectively and reliably used to provide quality-based sorting of search results. Besides, it strongly evidences that the evaluation of Learning Objects from a pedagogical perspective can notably enhance Learning Object search if suitable evaluations models and quality metrics are used. An evaluation of the LORI model is also described. Finally, all the presented metrics are compared and a discussion on their weaknesses and strengths is provided.

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

Analyzing training dependencies and posterior fusion in discriminant classification of apnoea patients based on sustained and connected speech

We present a novel approach using both sustained vowels and connected speech, to detect obstructive sleep apnea (OSA) cases within a homogeneous group of speakers. The proposed scheme is based on state-of-the-art GMM-based classifiers, and acknowledges specifically the way in which acoustic models are trained on standard databases, as well as the complexity of the resulting models and their adaptation to specific data. Our experimental database contains a suitable number of utterances and sustained speech from healthy (i.e control) and OSA Spanish speakers. Finally, a 25.1% relative reduction in classification error is achieved when fusing continuous and sustained speech classifiers. Index Terms: obstructive sleep apnea (OSA), gaussian mixture models (GMMs), background model (BM), classifier fusion.

Integration of SCORM packages into web games

This paper presents a model that enables the integration of SCORM packages into web games. It is based on the fact that SCORM packages are prepared to be integrated into Learning Management Systems and to communicate with them. Hence in a similar way they can also be integrated into web games. The application of this model results in the linkage between the Learning Objects inside the package and specific actions or conditions in the game. The educational content will be shown to the players when they perform these actions or the conditions are met. For example, when they need a special weapon they will have to consume the Learning Object to get it. Based on this model we have developed an open source web platform which main aim is to facilitate teachers the creation of educational games. They can select existing SCORM packages or upload their own ones and then select a game template in which the Learning Objects will be integrated. The resulting educational game will be available online. Details about the model and the developed platform are explained in this paper. Also links to the platform and an example of a generated game will be provided.

Using lecture recordings to support curriculum changes in the European convergence process

The aim of this paper is to present the experience of using lecture recordings to support curriculum changes within the framework of the European convergence process, mainly courses that need to be promoted or discontinued. We will explain an integrated solution for recording lectures consisting of a web portal, a videoconferencing tool and an economical and easily transportable kit. The validation process was performed recording three different courses at the Universidad Politécnica of Madrid (UPM) and using different diffusion channels, such as Moodle, an open source web portal called GlobalPlaza that supports streaming and recordings and the YouTube UPM channel. To assess the efficiency of our solution, a formal evaluation was conducted and will be also presented in this paper. The results show that lecture recordings allow teachers to support discontinued and new courses and enable students from remote areas to participate in international educational programmes, also the resulting recordings will be used as learning objects for future virtual courses.

Diseño de una red multinivel de conocimiento certificado y su aplicación a la enseñanza universitaria

En la investigación en e-Learning existe un interés especial en la adaptación de los objetos de aprendizaje al estudiante, que se puede realizar por distintos caminos: considerando el perfil del estudiante, los estilos de aprendizaje, estableciendo rutas de aprendizaje, a través de la tutoría individualizada o utilizando sistemas de recomendación. Aunque se han realizado avances en estas facetas de la adaptación, los enfoques existentes aportan soluciones para un entorno específico, sin que exista una orientación que resuelva la adaptación con una perspectiva más genérica, en el contexto de los objetos de aprendizaje y de la enseñanza. Esta tesis, con la propuesta de una “red multinivel de conocimiento certificado” aborda la adaptación a los perfiles de los estudiantes, asume la reutilización de los objetos de aprendizaje e introduce la certificación de los contenidos, sentando las bases de lo que podría ser una solución global al aprendizaje. La propuesta se basa en reestructurar los contenidos en forma de red, en establecer distintos niveles de detalle para los contenidos de cada nodo de la red, para facilitar la adaptación a los conocimientos previos del estudiante, y en certificar los contenidos con expertos. La “red multinivel” se implementa en una asignatura universitaria de grado, integrándola en los apuntes, y se aplica a la enseñanza. La validación de la propuesta se realiza desde cuatro perspectivas: en las dos primeras, se realiza un análisis estadístico para calcular la tasa de aceptación y se aplica un modelo TAM, extrayendo los datos para realizar el análisis de una encuesta que cumplimentan los alumnos; en las otras dos, se analizan las calificaciones académicas y las encuestas de opinión sobre la docencia. Se obtiene una tasa de aceptación del 81% y se confirman el 90% de las hipótesis del modelo TAM, se mejoran las calificaciones en un 21% y las encuestas de opinión en un 9%, lo que valida la propuesta y su aplicación a la enseñanza. ABSTRACT E-Learning research holds a special interest in the adaptation of learning objects to the student, which can be performed in different ways: taking into account the student profile or learning styles, by establishing learning paths, through individualized tutoring or using recommender systems. Although progress has been made in these types of adaptation, existing approaches provide solutions for a specific environment without an approach that addresses the adaptation from a more general perspective, that is, in the context of learning objects and teaching. This thesis, with the proposal of a “certified knowledge multilevel network”, focuses on adapting to the student profile, it is based on the reuse of learning objects and introduces the certification of the contents, laying the foundations for what could be a global solution to learning. The proposal is based on restructuring the contents on a network setting different levels of depth in the contents of each node of the network to facilitate adaptation to the student’s background, and certify the contents with experts. The multilevel network is implemented in a university degree course, integrating it into the notes, and applied to teaching. The validation of the proposal is made from four perspectives: the first two, a statistical analysis is performed to calculate the rate of acceptance and the TAM model is applied, extracting data for analysis of a questionnaire-based survey completed by the students; the other two, academic qualifications and surveys about teaching are analyzed. The acceptance rate is 81%, 90% of TAM model assumptions are confirmed, academic qualifications are improved 21% and opinion survey 9%, which validates the proposal and its application to teaching.

Aplicación Android para el aprendizaje de inglés nivel B2 : extensión del ILLLab

Desde el inicio de la globalización, el aprendizaje de la lengua inglesa se ha instaurado como una necesidad. Hoy en día, con la adopción del Espacio Europeo de Educación Superior este lenguaje no sólo se impone como un requisito para los estudiantes sino que se exige un nivel B2, lo cual significa un esfuerzo mayor tanto como para el alumno como para el profesor a la hora de hacer de este ejercicio un hábito y lograr la evaluación continua de los mismos. Este proyecto intenta extender las funcionalidades de una aplicación existente llamada Illlab con ejercicios que se adapten al nivel B2 y permitan la interacción entre alumnos durante la realización de estos ejercicios. El objetivo de esta aplicación es el de desarrollar ejercicios extra en la aplicación Illlab que añadan complejidad para el aprendizaje de inglés de un nivel B2 y que además se puedan realizar actividades entre los alumnos. La idea es hacer una aplicación de preguntas y respuestas “multiple choice” con cuatro opciones por pregunta. El fuerte de este juego está en presentar material variado sobre uso de la lengua y además permitir el juego entre varios alumnos. La extensión de ILLLab se plantea como un proyecto para desarrollar interfaces y funcionalidades adicionales en la antigua aplicación. La principal funcionalidad que se añade es un juego de preguntas y respuestas con opciones múltiples para un nivel B2 y las interfaces responden a necesidades de intercambio y manejo de contenido por Internet mediante estándares aceptados en el mundo del aprendizaje digital tales como Common Cartridge o SCORM. Este proyecto simplemente adapta la aplicación para su uso en un entorno de evaluación de actividades en el cual el profesor tiene acceso a las actividades que realizan los alumnos de un curso para su posterior evaluación. Antiguamente ILLLab sólo contenía ejercicios que se llevaban a cabo en el dispositivo móvil por lo que el control de estas actividades no era posible. La mejora se presenta como una interfaz Common Cartridge para el manejo del contenido, una interfaz de comunicación sobre servicios web tipo REST y el manejo de base de datos mediante Hibernate que agrupa una serie de librerías Java para la persistencia de objetos de la base de datos. ABSTRACT. Since the onset of globalization, the learning of the English language has become as a necessity. Today, with the adoption of the European Higher Education Area this language is not only imposed as a requirement for students but a B2 level is required, which means a greater effort both to the student and teacher when it comes to make the learning exercise a habit and achieve continuous evaluation of students. This project aims to extend the functionality of an existing application called Illlab with an exercise that suits the B2 level and allow interaction between students while performing these exercises. The purpose of this application is to develop an additional exercise in the application Illlab that adds complexity for learning English at B2 level and also enables the interaction among students. The main idea is to make an application in multiple choices style with four options. The strength of this game is to present varied material on use of Enlgish and also allow play between two students. ILLLab extension is conceived as a project to develop interfaces and additional functionalities in the old application. The main functionalities added are a game of questions and answers with multiple choices for a B2 level and interfaces that meet information exchange requirements and content management over the Internet using standards adopted in the world of digital learning such as Common Cartridge or SCORM. This project simply adapts the application for its use in an activities evaluation environment in which the teacher has access to the activities performed by students in a course for further evaluation. The former versión of ILLLab contained only exercises that were carried out on the mobile device so that the evaluation of these activities was not possible. The improvement comes as a Common Cartridge interface for content management, a communication interface with REST web services and a database access using Hibernate which groups a number of Java libraries for object persistence in the database.

Conductivity test in seeds of different passion flower species

The objective of this work was to evaluate the use of the conductivity test as a means of predicting seed viability in seven Passiflora species: P. alata, P. cincinnata, P. edulis f. edulis, P. edulis f. flavicarpa, P. morifolia, P. mucronata, and P. nitida. Conductivity of non?desiccated (control), desiccated, and non?desiccated cryopreserved seeds was determined and related to their germination percentage. The obtained results suggest that the electrical conductivity test has potential as a germination predictor for P. edulis f. flavicarpa seed lots, but not for the other tested species. Index terms: Passiflora, seed cryopreservation, seed desiccation, seed viability.