OpenACS and - Galileo Educational System - Universidad Galileo

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Departamento de Investigación y Desarrollo Universidad Galileo

MEMORIAS 2ª. Conferencia Internacional E-Learning Integral 2.0 Y 6ª. Conferencia Internacional de OpenACS y .LRN

Guatemala, 12 al 15 de febrero de 2008

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ISBN 978-99922-2-434-2 Comité del Programa 2a. Conferencia Internacional E-Learning Integral 2.0            

Rocael Hernández, Universidad Galileo, Guatemala. (Director de Comité del Programa) Carl Blesius, M.D., MGH Lab of Computer Science / Harvard, U.S.A. Jesús G. Boticario, Ph.D., aDeNu Research Group, UNED, España. Carlos Delgado Kloos, Ph.D., UC3M, España. Gustaf Neumann, Ph.D., University of Economics and Business Administration, Vienna, Austria. Abelardo Pardo, Ph.D., UC3M, España. Rafael Pastor, Ph.D., Innova, UNED, España. Olga C. Santos, aDeNu Research Group, UNED, España Dr. Cyrano Ruiz, Ph.D., Universidad Galileo. Dr. Bernardo Morales, Ph.D., Universidad Galileo. Inga. Stephany Orozco, Universidad Galileo. Ing. Rodrigo Baessa, Universidad Galileo.

Program Committee OpenACS and .LRN Conference           

Carl Blesius, MGH Lab of Computer Science (MIT / Harvard), U.S.A. Jesús G. Boticario, aDeNu Research Group, UNED, Spain. Carlos Delgado Kloos, UC3M, Spain. Alfred Essa, U.S.A. Rocael Hernández, Galileo University, Guatemala. Raúl Morales, Innova, UNED, Spain. Gustaf Neumann, University of Economics and Business Administration, Vienna, Austria. Abelardo Pardo, UC3M, Spain. Rafael Pastor, Innova, UNED, Spain. Emmanuelle Raffenne, aDeNu Research Group, UNED, Spain. Olga C. Santos, aDeNu Research Group, UNED, Spain.

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INDICE El rol del e-learning en el proceso electoral 2007 Guatemala Experiencia Tribunal Supremo Electoral – Corporate Learning………… E-Learning en Galileo, Acceso e Innovación para todos……………….. Calidad en elearning: Criterios de calidad en el aprendizaje virtual – la experiencia de la URL………………………………………………… Scrutinising Competencias: Retraceable Clouds of Learning Goals In the APOSDLE System……………………………………………….. Open Source Collaborative eLearning………………….………………. Educación Nacional Apoyada por Ciencia y Tecnología……………….. Usability in e-Learning Platforms: heuristicscomparison between Moodle, Sakai and dotLRN…………………………………………….. Monitoring of Learning Performance: From Eye-Tracking Support to Explicit Feedback………………………………………………………. Playing Games with Business: History, Theory and Examples…………… A Web Application Mashup Approach for E-Learning ………………….. Web Content Creation Tool for dotLRN…………………………………... Automatic Limited- Choice and Completion Test Cration, Assessmente and feeback in modern Learning processes………………… Transnational Educational Technology ………………….……………… Ajax, Listbuilder, and Dynamic Types…………………………………… Web Storage Website: AJAX & OpenACS ……………………………… Ajax interfaces inside OpenACS…………………………………………. Xowiki as CMS?........................................................................................... A General Tracking and Auditing Architecture for the OpenACS Framework…………………………………………………………………. Management of standard-based User Models and Device Profiles in OpenACS…………………………………………………………………… Dynamic support in OpenACS/dotLRN: Technological Infrastructure for providing dynamic recommendations for all in open and standard-based LMS ………………………………… Galileo´s Infrastructure……………………………………....................... Diseño y Evolución del Clúster de E-Learning (.LRN) en la Universitat de València…………………………………………………….. Proyectos de e-Learning desarrollados en Universidad Galileo 2006-2007… Arquitectura .LRN…………………………………………………………..

8 17 28 34 47 59 76 87 98 106 114 126 134 142 148 162 172 180 190

198 209 212 214 216

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ISBN 978-99922-2-434-2

INDICE DE AUTOR Aguilar, Maria José Usability in e-Learnig Platforms: heuristics comparison between Moodle, Sakai and dotLRN

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Aguilar, Vivian Arquitectura de .LRN 216 Aust, Ronald Open Source Collaborative eLearning 47 Transnational Educational Technology 134 Bauer, David Ajax, Listbuilder, and Dynamic Types 142 Beham, Günter Scrutinising Competencias: Retraceable Clouds of Learning Aposdle Goals in the System 34 Boticário, Jesús G. Usability in e-Learnig Platforms: heuristics comparison between Moodle, Sakai and dotLRN

A General Tracking and Auditing Architecture for the OpenACS framework Management of standard-based User Models Dynamic support in OpenACS/dotLRN: Technological infrastructure for providing dynamic recommendations Clavería, César Web Storage Website: AJAX & OpenACS Couchet, Jorge A General Tracking and Auditing Architecture for the OpenACS framework Cuartero, Adrián Management of standard-based User Models De La Roca, Mónica Modelo Educativo de e-Learning implementado en Universidad Galileo Finkel, Meir

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180 190 198 148

180 190

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EDUCACIÓN NACIONAL APOYADA POR CIENCIA Y TECNOLOGÍA

Furman, Rich Transnational Educational Technology García Izaguirre, Sonia Modelo Educativo de e-Learning implementado en Universidad Galileo 214 García-Barrios, Victor Manuel Scrutinising Competencias: Retraceable Clouds of Learning Aposdle Goals in the System

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134

34

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Monitoring of Learning Performace: From Eye- Tracking Support Explicit Feedback

A Web Application Mashup Approach for eLearning Giorgis de Orozco, Nidia

to 87

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Calidad en E- Learning: Criterios de Calidad en el Aprendizaje Virtual

Granado, Jorge Management of standard-based User Models Dynamic support in OpenACS/dotLRN: Technological infrastructure for providing dynamic recommendations Guerra, Victor M. Ajax interfaces inside OpenACS Web Content Creation Tool for dotLRN Gütl, Christian Automatic Limited-Choice and Completion Test Cration, Assessment and Feedback in modern Learning Processes Hernández, Rocael

28 190 198 162

114

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E learning en Galileo Acceso e Innovación Para Todos

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Web Content Creation Tool for dotLRN 114 Web Storage Website: AJAX & OpenACS 148 Ajax Interfaces inside OpenACS 162 XOWIKI AS CMS 172 Galileo Infraestructure 209 Kump, Bárbara Scrutinising Competencias: Retraceable Clouds of Learning Aposdle Goals in the System 34 Linares, Byron H. Web Content Creation Tool for dotLRN 114 Manrique, Daniel A General Tracking and Auditing Architecture for the OpenACS framework 180 Martin, Ludivine Usability in e-Learnig Platforms: heuristics comparison between Moodle, Sakai and dotLRN

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Meeks, Carolina Playing Games with Business: History, Theory, and Example Mödritscher, Felix A Web Application Mashup Approach for eLearning 106 Morales, Miguel

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E learning en Galileo Acceso e Innovación Para Todos

Modelo Educativo de e-Learning implementado en Universidad Galileo Neumann, Gustaf A Web Application Mashup Approach for eLearning Quesada, Allen Open Source Collaborative eLearning Transnational Educational Technology Raffenne, Emmanuelle A General Tracking and Auditing Architecture for

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214 107 48 135

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the OpenACS framework Management of standard-based User Models Dynamic support in OpenACS/dotLRN: Technological infrastructure for providing dynamic recommendations Revilla, Olga

181 191 199

Usability in e-Learnig Platforms: heuristics comparison between Moodle, Sakai and dotLRN

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Rodríguez, Alvaro XOWIKI AS CMS Roldán Martinez, David

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Usability in e-Learnig Platforms: heuristics comparison between Moodle, Sakai and dotLRN

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Santos, Edgar El Rol de E- Learning en el Proceso Electoral 2007 Guatemala Santos, Olga C. Usability in e-Learnig Platforms: heuristics comparison between Moodle, Sakai and dotLRN

A General Tracking and Auditing Architecture for the OpenACS framework Management of standard-based User Models Dynamic support in OpenACS/dotLRN: Technological infrastructure for providing dynamic recommendations for all in open and standard-based LMS Wild, Fridolin A Web Application Mashup Approach for eLearning

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181 191 199

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MENSAJE DEL ORGANIZADOR Poner en marcha un evento, de carácter internacional es un reto, pero a su vez un deber dentro de nuestro contexto educativo en Guatemala. Un especial agradecimiento a todas las autoridades de la Universidad Galileo que nos apoyaron e hicieron posible este evento, de igual forma extiendo cordiales agradecimientos a los colegas de diversas instituciones Nacionales, en Estados Unidos, Europa y Latino América que también colaboraron en la realización del mismo. Por último, pero igualmente importante, es de reconocer y agradecer el esfuerzo, entusiasmo y apoyo de todo el Departamento de Investigación y Desarrollo, GES, cuya labor ha sido fundamental para el evento. En esta segunda edición de nuestra conferencia de E-learning Integral (la cual se transformó de foro a conferencia) decidimos adoptar un programa amplio en el cual se cuenta con una sesión plenaria de ponencias, una sesión de stands y pósters, y 2 tutoriales, todo relacionado al tema de la Conferencia: Acceso e Innovación en el Elearning. Se decidió adoptar este título - tema para la conferencia porque es importante mostrar las innovaciones en el campo, y a su vez hablar del acceso al Elearning en general, especialmente dentro de nuestro contexto latinoamericano a nivel académico, gubernamental, etc.

El mayor reto fue evaluar las propuestas de trabajos de investigación que se recibieron, tan diversos e interesantes, para luego producir este libro electrónico que tiene un número ISBN que le permite ser referenciado internacionalmente y provee un elemento de prestigio adicional para las conferencias. Todo esto nos ha dejado una enorme experiencia que iremos incorporando en las siguientes ediciones de la Conferencia.

Por otra parte, ha sido una gran responsabilidad ser la sede de la 6a. Conferencia de .LRN & OpenACS. Fue idóneo organizar ambos eventos de forma continua, y ambas Conferencias se vieron beneficiadas por esta sinergía.

Se proyecta continuar con más ediciones del evento de E-learning Integral para seguir creando un espacio y ambiente en el cual a través de los diversos participantes se conozca, comparta, discuta y exponga lo más reciente del E-learning en Latinoamérica y el mundo, creando así, un impacto positivo en los asistentes, y en general para nuestro país, especialmente en este momento, donde la Sociedad del Conocimiento es una realidad.

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Todos debemos ser participes de los cambios que la tecnología esta provocando en los procesos educativos, transformar a nuestros estudiantes en ciudadanos de este mundo plano y digital, con las competencias necesarias para triunfar profesionalmente.

Deseo que este material sea de mucho provecho para sus actividades educativas y profesionales.

Ing. Rocael Hernández Rizzardini Organizador del Evento Director Desarrollo, Depto. de Investigación y Desarrollo. Director Galileo Educational System (GES), E-campus, E-learning & Diseño Instruccional. Universidad Galileo Torre Galileo, oficina 413 Guatemala.

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El rol del e-learning en el proceso electoral 2007 Guatemala Experiencia Tribunal Supremo Electoral – Corporate Learning Edgar Santos

Tema: Experiencias y Mejores Prácticas Ponencia.

1. ANTECEDENTES El Tribunal Supremo Electoral, instaló más de 600 puntos de votación a nivel nacional y se llegó a lugares recónditos del país con el objetivo de que toda la población emitiera su voto, múltiples municipios del país contaron con mesas de votación. Para el conteo de las boletas de votación se tenía el objetivo de emplear 2,000 personas encargadas de digitar las boletas mediante un sistema implementado por el Tribunal Supremo Electoral, la selección y entrenamiento se debía realizar en el mes de agosto y contar con personal capacitado en el puesto, que conociera bien los procedimientos de digitación y conteo así como el envió respectivo de los reportes, además de saber de la responsabilidad que cubre al tomar este puesto tanto el compromiso adquirido con la institución como para el país, ósea una reflexión de valores ciudadanos.

2. EL RETO Llevar la capacitación a más de 2,000 personas para digitar las boletas de digitación. El TSE realizó una convocatoria donde tuvieron una afluencia de más de 10,000.00 personas que en un término de una semana deberían ser evaluados con equipo de computo en un salón de Parque de La industria y 2 centros en el interior del país, con el fin de elegir a cada persona que esté interesada en participar en la jornada de trabajo que el TSE convoca, que cumpla los requisitos y complete satisfactoriamente la capacitación y así mismo las evaluaciones correspondientes.

3. LA SOLUCIÓN El Tribunal Supremo Electoral en conjunto con Corporate Learning, empresa especializada en soluciones de e-learning, realizaron un contenido e-learning de

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aproximadamente una hora de capacitación con practicas interactivas tanto del uso de el programa de Ingreso de datos o boletas así como un contenido de Valores, además de un examen. La utilización de la metodología Corporate Learning para la realización de contenido, Exámenes y perfil de participantes permitió capacitar y clasificar entre 10,000 personas las 2000 personas que apoyaron el proceso electoral 2007 en Guatemala. Fig. 1 Pantalla inicial del contenido del curso e-learning para la capacitación en las elecciones generales 2007

Tribunal Supremo Electoral Integrando La Tecnología En Su Capacitación.

g. Edgar Santos, Gerente General Fig 2. Pantalla del contenido del curso e-learnning, Opciones del sistema para ingresar un acta.

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Tribunal Supremo Electoral Integrando La Tecnología En Su Capacitación.

4. CONTEXTO GENERAL El Tribunal Supremo Electoral como en cada proceso de elecciones tiene grandes retos entre ellos el realizar el conteo de los votos en el menor tiempo posible y con certeza, para esto se valió de un sistema de información que le permitiera captar y transmitir los datos de distintos centros de votación distribuidos en el país. Al involucrar un sistema de información trae varios retos: 1. Reclutar a 2,000 personas con habilidades de digitación y usos de sistemas de información. 2. Seleccionar entre más de 10,000 personas 3. Garantizar que el personal que se selecciona pueda rápidamente adecuarse al sistema. 4. El personal está distribuido geográficamente en el país y no todos los candidatos tienen acceso a Internet. 5. Coordinar actividades de capacitación en conjunto con toda la logística del proceso electoral.

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5. CAPACITACIÓN TRADICIONAL VRS. CAPACITACIÓN ELEARNING En el caso del TSE si se hubiera querido hacer un proceso de capacitación con 2,000 personas se hubiese necesitado logística como: salones de clase, instructores, instalación de equipos, manejo de horarios, traslados, suponiendo que en una capacitación tradicional un salón de 12 personas es adecuado para el manejo de 1 instructor, en este caso se hubieran necesitado 167 grupos de alumnos de clase para dar capacitación en 4 horas, además que la restricción que se tenía era en 2 semanas o 10 días hábiles para realizarlo esto da un total de 17 salones. Una de las ventajas del e-learning, es la combinación de diferentes elementos multimedia, como sonido, videos y simulaciones, permite enfocar en los alumnos en decirle como se hace, luego el alumno hace el mismo ejemplo de manera guiada, esto logro reducir la capacitación a 1 hora. Aprovechando este ahorro de tiempo también se decidió agregar una evaluación con el objetivo de medir la eficiencia y eficacia del candidato, es decir se creó un simulador del software, al candidato se le entrega una copia de actas reales llenas con datos, estas debían ser digitadas dentro del simulador, al finalizar de introducirla el sistema hacía un cálculo automático de nota donde evaluaba tiempo de introducirla vrs. Los errores cometidos, es decir eficiencia y eficacia. Esto permitió que el tribunal pudiera seleccionar a las mejoras notas de los candidatos. Como se puede ver en la tabla 2 de selección de personal, se tuvieron más de 10,000 candidatos a la plaza temporal, en la capacitación tradicional se hubiera tenido que hacer una revisión de los curriculums y seleccionar a los 2,000 basado en documentos. Si se hubiera querido capacitar y luego examinar se hubiera requerido aproximadamente 83 salones de 12 personas. En el caso real se tuvieron salones con mayor número de personas hasta 150 personas, y se requirieron 3 salones únicamente en 3 puntos del país.

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TABLA 1

TABLA 2

6. METODOLOGÍA IMPLEMENTADA CORPORATE LEARNING: Se utilizó la metodología ―Mentored Learning‖ en la capacitación del sistema de ―EscrutinioElecciones Generales 2007‖. Esta metodología combina las clases presénciales con cursos de auto estudio. Para lograr capacitar y seleccionar a los 10,000 candidatos, se realizaron 3 pasos:

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1) Anuncio de Prensa 2) Asistencia al curso 3) Selección de Candidatos Cada uno de los pasos fue crucial para lograr reclutar y capacitar a los diferentes aspirantes. Anuncio en Prensa: Aproximadamente 1 mes antes de la capacitación se convocó en los diarios de mayor circulación del país el anuncio en prensa en el cual se convocaba a la capacitación, además de darlos detalles correspondientes al puesto de trabajo. Asistencia a Curso: El curso de ―Escrutinio Elecciones Generales 2007‖, se impartió con una metodología de ―Mentored Learning‖. Esta metodología combinó los principales beneficios de las clases presénciales y los cursos de auto estudio. Entre los beneficios obtenidos en esta capacitación podemos mencionar: Enfoque en el estudiante: cada estudiante recibió su capacitación a su propio ritmo, lo cual le permitió enfocarse en aquellos temas que más demandaron su atención. Instrucción Uno-Uno: siempre se contó con por lo menos un Mentor dentro del salón de clases, el cual era un experto en cada uno de los temas del curso. Su función principal fue guiar al estudiante y aclarar cualquier tipo de duda.

La capacitación de cada aspirante, se puede dividir en 5 etapas: Asistencia a los salones de clase Se contó con 3 salones de clases distribuidos de la siguiente forma:

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Principios y Valores El compromiso con Guatemala que cada aspirante tendría al ser contratado por el Tribunal Supremo Electoral era muy alto, ya que el trabajo que tendría seria de ingresar los resultados de las votaciones en el sistema central de cómputo. Por tal motivo al inicio de su capacitación se hizo conciencia de los principios y valores que rigen a todos los colaboradores del Tribunal Supremo Electoral, así como, hacerle ver la importancia de su labor dentro del proceso electoral 2007. Lección El curso fue dosificado en 16 lecciones, las cuales enseñaron al estudiante todos los lineamientos y forma de utilización del sistema de ―Escrutinio Elecciones Generales 2007‖. Este curso se realizo con contenidos multimedia interactivos que permitieron al estudiante escuchar la voz de su instructor, al mismo tiempo que iba realizando todos los procedimientos que el instructor le pedía hacer. El estudiante siempre tuvo visible el sistema de ―Escrutinio Elecciones Generales 2007‖, con el objetivo de estar familiarizado con la interfaz de este. Practica Luego que el estudiante recibió la lección y realizó todos los pasos que el instructor le pidió, pasó a una fase de práctica, donde se encontró con una simulación real del sistema de ―Escrutinio Elecciones Generales 2007‖. Aquí se le pedía al estudiante

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que realizara las tareas enseñadas con el fin de asegurarse del entendimiento de la lección. ng. Edgar Santos, Gerente General El estudiante tuvo la oportunidad de repetir las veces que fueran necesaria cada lección antes de pasar a la fase del examen. Durante esta fase el estudiante podía consultar al Mentor cualquier duda o inquietud. Examen Al finalizar la capacitación por parte del estudiante, este pasó a la fase del examen en donde se simuló un ingreso de 4 juegos de actas al sistema de ―Escrutinio Elecciones Generales 2007‖. Las actas se le proporcionaron en papel y debía utilizar el sistema para ingresar dichas actas. En este examen se valuaron 2 aspectos: - Eficiencia, Tiempo para el ingreso de las actas - Efectividad en los datos ingresados, número de errores ingresados

7. SELECCIÓN DE CANDIDATOS Todos los candidatos, antes de iniciar el examen, llenaron un formulario con la información necesaria para que el Tribunal Supremo Electoral pudiera contactarlos en caso de ser seleccionados. Todas las notas de los estudiantes quedaron almacenadas en una base de datos centralizada en la cual se filtro para seleccionar a los mejores 2,000 punteos. Estos fueron contratados por el Tribunal Supremo Electoral.

5.1 Autores: Edgar Santos, Ingeniero en Ciencias y Sistemas Universidad de San Carlos de Guatemala, Master in Business Administration de la Universidad Francisco Marroquín, Gerente General Corporate Learning. Director del proyecto de e-learning del sistema de escrutinios para el Tribunal Supremo Electoral 2007. Sofia Posada, Licenciada en Diseño Gráfico con especialización en multimedia, Master en Nuevas tecnologías orientadas a la educación titulada por las universidades de Alicante de Madrid, Carlos Tercero Barcelona de España, Especialista en metodología y multimedia del contenido del proyecto de e-learning del sistema de escrutinios para el Tribunal Supremo Electoral 2007.

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E-Learning en Galileo, Acceso e Innovación para todos

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Rocael Hernández, Miguel Morales Universidad Galileo (roc, amorales)@galileo.edu

RESUMEN Se explorara el trabajo realizado por parte del Departamento de Investigación y Desarrollo en el área de e-Learning, el mismo realizado de manera integral dentro de la Universidad Galileo, partiendo de elementos básicos como la transmisión sistemática de conocimientos acerca de producción e implantación de cursos eLearning, la simplificación continua del sistema, la introducción de nuevas tecnologías y herramientas, para finalizar en las líneas de trabajo de cara al futuro.

1. INTRODUCCIÓN Desde hace más de 8 años la universidad Galileo cuenta con soporte en Internet para sus cursos, a través del sistema conocido como GES (Galileo Educational System), y durante los últimos años se ha expandido el alcance de este sistema y sus procesos a casi toda la universidad, contando con más de 100,000 usuarios registrados en el sistema. Parte de los objetivos actuales son simplificar los procesos e interfaz del LMS (Learning Management System), hacerlos de fácil acceso para los catedráticos y estudiantes. De igual forma es necesario incorporar métodos y prácticas de como utilizar mejor la tecnología en la educación y para ello es fundamental crear procesos para poder duplicar estos conocimientos y buenas prácticas. A continuación describimos el caso GES / Galileo que implementa un e-Learning integral (que involucra tanto tecnología, como metodologías, mejores prácticas y procesos de entrenamiento en estas áreas) con el que se pretende brindar mayor acceso al e-Learning, y a su vez realizar innovaciones durante dicho proceso.

2. ACCESO E INNOVACIÓN EN LA TECNOLOGÍA En la universidad Galileo se utiliza como sistema de administración de aprendizaje (LMS por sus siglas en íngles, Learning Management System) llamado .LRN (pronunciado dot-learn), que puede ser obtenido gratuitamente en www.dotlrn.org. El sistema es conocido como GES (Galileo Educational System) el cual es una instalación y adaptación del LMS .LRN, dicha instalación ha sido adaptada a las necesidades de la Universidad Galileo siendo además de un LMS, un sistema que integra diversos servicios que son de importancia para el estudiante y catedrático.

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Se ha abordado el trabajo para simplificar, unificar y mejorar la interfaz de usuario, para lo cual se ha trabajado en diversas fases, tales como la realización de una nueva imagen gráfica, unificación de aplicaciones, simplificación y automatización de procesos, y otros. Para ello se ha utilizado los principios de Diseño de Interacción de Usuario [1] con el objetivo de reducir la fricción cognitiva que todo sistema informático conlleva para el usuario [2], en donde se elabora un escenario en base a las metas que el usuario tiene hacia la utilización del sistema. Como complemento necesario en varias partes del sistema se utilizó la tecnología llamada AJAX que ayuda a hacer la interfaces mas interactivas para ambientes web, los siguientes son los principales trabajos realizados. 2.1. Nueva interfaz gráfica: Se diseño una nueva interfaz gráfica en la cual se trabajaron 2 áreas, una es la visualización gráfica, de colores e iconografía. Se realizo un diseño gráfico mas ajustado al web 2.0, donde se uso tonalidades apropiadas para las diversas áreas del sistema, efectos visuales minimalistas tales como sombras, 3D, líneas divisorias. Por otra parte, en cuanto a la navegación, se crearon 4 pestañas superiores, que en todo momento indican la posición en donde se encuentra dentro del sistema. Las áreas son: mi portal (refleja el mismo contenido que siempre se ha tenido para el área de portal personal, pero con la diferencia de que esta accesible siempre), cursos (un elemento directo para acceder al listado de cursos desde cualquier parte del sistema), Mi cuenta (acciones administrativas para mi curso), y la pestaña para cada curso (donde se encuentra las secciones para navegar en los recursos del curso). 2.2. Sistema de copiado de elementos entre clases: Se creó un proceso integrado para copiar elementos de un curso a otro(s). El proceso se simplifico a 3 pasos: 1) seleccionar de que curso deseo copiar elementos, 2) seleccionar elementos que deseo copiar (tales como tareas, examenes, documentos, graficas, etc.), y 3) seleccionar curso o cursos destino de la copia. Se utilizó AJAX para las interfaces gráficas proveyendo una forma fácil de navegal entre los cursos y contenido, de una forma interactiva.

2.3. Estructuración de interfaz administrativa y de reportes del subsistema de evaluación:

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El ordenamiento, organización y simplificación de acceso a la información es necesario en todo sistema, en el caso del LMS .LRN y su versión GES de Galileo contamos con un sistema para administración de un determinado curso por parte de profesor, este sistema permite la creación de diversos categorías de asignaciones, tales como exámenes, tareas, laboratorios, etc, y adicionalmente contar con elementos en cada uno, por ejemplo tareas tendría tarea 1, tarea 2, etc. Cada uno de estos elementos debe de ser evaluado por el profesor. Se agrupo la interfaz administrativa para cada asignación a través de secciones (tabs): Información, Evaluados, No Evaluados, Evaluar con Excel. Adicionalmente para la entrega de asignación por Internet existe un tab adicional: Sin entregar. Anteriormente todas estas secciones estaban integradas en una sola página. Ahora se unen en un sistema de tabs que combina AJAX e historial de navegación en el browser [3].

De igual forma se trabajo con los reportes de asignaciones, se agruparon por secciones (tabs) y se les incluyo AJAX para una navegación mas sencilla, ya que no hay necesidad de recargar la página para servir el contenido, eso permite la consistencia visual de los tabs y reduce costo cognitivo de utilizar la interfaz ya que es una interfaz similar utilizada en diversas partes del sistema y que es muy parecida a la utilizada en diversos sitios web. 2.4. Herramienta de contenidos: Es una herramienta sencilla que con un solo clic crea páginas que serán desplegadas en .LRN, conteniendo las mismas, texto, imágenes, flash, videos, audio, o cualquier otro recurso que se pueda visualizar en un navegador. Provee una plantilla básica que ayuda a agrupar la información por capítulos o unidades didácticas, temas y subtemas (representados por tabs). Adicionalmente provee una navegación lineal entre páginas de un mismo tema. Como navegación opcional provee un árbol dinámico que refleja toda la estructura del contenido.

3. ACCESO E INNOVACIÓN EN EL E-LEARNING Desde hace ya un tiempo, la explosión del Internet y las tecnologías de la información y la comunicación (TIC) han generado intensas transformaciones sociales y culturales. Para el GES (Galileo Educational System) todos estos avances tecnológicos implican grandes desafíos y oportunidades. El principal desafío es la exigencia de nuevas competencias para los estudiantes, competencias cuyo aprendizaje y desarrollo deben ser satisfechos por nuestro sistema. El e-learning es una tendencia mundial en el campo de la educación, tanto dentro del contexto académico como empresarial. El enfoque más fuerte ha sido en el tema de tecnología dejando de lado los aspectos igualmente importantes como lo son los modelos y metodologías educativas a utilizar. El e-learning representa de fondo una

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innovación en la forma de enseñanza, donde ahora el curso gira alrededor del estudiante que aprende y no del que enseña. 3.1. Producción de Cursos: Se creó un proceso integral para el desarrollo de cursos, el cual involucra varios roles, el del experto, encargado de crear todo el material, el rol del ensamblador, encargado de trasladar todo el material creado por el experto al formato web utilizando la herramienta de creación de contenido, también se cuenta con el diseñador grafico y el diseñador instruccional, el primero se encarga de crear todo el material audiovisual para el curso y el segundo es el asesor pedagógico, encargado de velar porque el material este diseñado de tal manera que el estudiante pueda adquirir conocimientos, desarrollar aptitudes y competencias.

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3.2. Acceso a contenidos Dentro del campo de los cursos virtuales se han desarrollado cursos de diferentes niveles, clasificados por su aplicación, implementación de elementos interactivos, uso de metodologías de moderación en línea, etc. En este marco hay que resaltar la importancia de crear materiales accesibles, no hay que olvidar que lo principal es que el alumno aprenda mediante la tecnología de Internet y no forzosamente aprenda la tecnología Internet. El que se utilicen herramientas externas para generar contenido que posteriormente se insertaran en la plataforma (por ejemplo antes de contar con la herramienta de edición de contenido, se trabaja con HTML) lleva a que se pierda la uniformidad de los contenidos (tipos de letra, colores, tamaños, etc). Otro aspecto a considerar es el uso de términos informáticos, especialmente porque no todos los usuarios están acostumbrados a ellos y podrían significar cualquier cosa. Los usuarios deben de saber perfectamente cuál es el camino a seguir en cada unidad didáctica y donde encontrar todos los recursos. Es necesario indicar de una forma muy clara el camino a seguir y no dar lugar a ningún error. En general esos son algunos de los factores que consideramos a la hora de preparar un curso online. 3.3. Cursos Desarrollados A continuación se listan algunos de los cursos desarrollados: 3.3.1. Maestría en Confiabilidad/ Curso Pensamiento Sistémico Escenario: Estudiantes de toda la República de Guatemala. Tipo de Educación: Totalmente en Línea. Fecha y Duración (de elaboración): Enero-Marzo 2008 Cantidad de Alumnos: 12 Estudiantes en Promedio por Edición. Cantidad de moderadores: 1 por Edición. Este curso se desarrollo para la maestría en Confiabilidad, con una duración de 10 semanas, se identifica como curso de categoría uno, por el uso del témplate Web, adicionalmente es uno de los primeros cursos donde se incluye moderación en línea, bajo el modelo constructivista obteniendo así un mejor resultado en la implementación de dicho curso. 3.3.2. Diplomado de ENRED 2005, 2006, 2007 Escenario: Estudiantes de toda la República de Guatemala. Tipo de Educación: Totalmente en Línea. Duración: 10 Semanas, organizado por módulos Cantidad de Alumnos: 1150 Estudiantes en Promedio por Edición.

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Cantidad de Cursos desarrollados: 1 Cantidad de moderadores: 20 en Promedio por Edición. Diplomado dirigido a estudiantes preuniversitarios a nivel nacional, con el objetivo de desarrollar en los participantes las habilidades y destrezas necesarias para generar y administrar sitios Web, el proyecto se desarrolla en 10 semanas, y se premian con becas estudiantiles a los 5 mejores proyectos elaborados, se cuenta con la participación de moderadores quienes colaboran a lo largo del diplomado. Este curso incluye elementos interactivos, audio, videos que permiten al participante desarrollar las capacidades necesarias para el desarrollo de sus sitios Web. Una característica importante a resaltar es el uso frecuente que se le da a los foros, donde se llevan a cabo un sinfín de actividades. 3.3.3. Curso de Actualización para Peritos Contadores Escenario: Estudiantes de toda la República de Guatemala. Tipo de Educación: Totalmente en Línea. Duración: 5 Meses, organizado por módulos Este curso se caracteriza por la calidad que se logro obtener al utilizar una plantilla tipo Web, donde se incluía por cada modulo, la guía de estudio (Programa del Curso), Actividades(Ejercicios, Tareas, Foros), Lecturas Complementarias, Glosario, Anexos con compendios de leyes en materia tributaria, una de las características principales de este curso, debido al perfil del curso, es que cuenta con una serie de ejercicios interactivos y casos prácticos, desarrollados en su totalidad para que se aprenda de una forma autodirigida. Adicionalmente se incluyo el programa, objetivos y descripción del curso dentro del mismo modulo de estudio, con lo cual se obtuvo mas versatilidad y facilidad de uso para el usuario final. Este curso fue realizado, como parte de una serie de proyectos canalizados a la actualización académica de los peritos contadores, desarrollado por especialistas en materia tributaria y con la completa colaboración del Departamento GES, prestando asesoría en e-Learning, capacitación, diseño gráfico, creación de elementos interactivos, ensamblaje de cursos, etc. El curso cuenta con 7 módulos de estudio y un modulo introductorio donde se explican aspectos generales acerca de la metodología, estructura del curso, elementos de evaluación, etc. Este curso ya se encuentra disponible en www.galileo.edu, colocando únicamente en usuario y contraseña: usuariosat. 3.3.4. Curso de Desarrollo y organización personal – FISICC Escenario: Estudiantes de toda la República de Guatemala. Tipo de Educación: Blended Learning (Mixto, Presencial/Virtual)

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Cantidad de Alumnos: 50 Estudiantes en Promedio por Edición. Cantidad de moderadores: 2 por Edición. Duración: 6 Meses, organizado por módulos Descripción del proyecto: Curso en modalidad Blended Learning (Un periodo Presencial y dos en línea a la semana) con una duración de 16 semanas de clases efectivas mas 3 semanas de evaluación, creado con la aplicación de creación de contenido, incluyendo material audiovisual, animaciones interactivas e implementando el modelo de moderación en línea (Construcción de más de 20 actividades que se desarrollaran en línea). Actualmente se está implementado. 3.3.5. Maestría en Planificación y Gestión de Políticas y Programas de Alimentación Infantil – ESCISA Escenario: Estudiantes de América Latina. Tipo de Educación: Totalmente en Línea Cantidad de Alumnos: 15 Estudiantes en Promedio por Edición. Cantidad de moderadores: 2 por Edición. Duración: 2 años, organizado por módulos Descripción del proyecto: Es una alternativa innovadora en materia de pediatría, la Maestría pretende contribuir a mejorar la salud de mujeres y niñas y niños en la Región de América Latina y el Caribe a través de la optimización de las prácticas de alimentación infantil. Tipo de enseñanza y metodología e-Learning La Maestría será dictada íntegramente en el sistema ―e-Learning‖ debido a dos motivaciones principales:  

Llegar a toda la Región de América Latina y el Caribe, y aún España y el resto del mundo Poder contar con un grupo de expertos de excelencia en un tema muy especializado, ya que no existen en el mundo desde nuestro conocimiento antecedentes de una maestría de características similares

Los cursos en línea (cursos virtuales) son un concepto educativo que integra soporte tecnológico, didáctico y administrativo para extender y transferir el conocimiento en cualquier rama del saber. Este tipo de cursos están basados en la aplicación de las nuevas Tecnologías de la Información y las Comunicaciones, que permiten el

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aprendizaje sin las limitaciones del lugar, tiempo, ocupación o edad de los estudiantes. E-aprendizaje NO ES e-lectura. Así:      

El alumno deja de ser un ente pasivo- para ser el protagonista del proceso Lo importante pasa a ser cómo aprenden los alumnos y no cómo enseña el profesor El tutor desempeña el rol de guía No es apto para todos los niveles educativos porque requiere de mucha disciplina, mayor madurez y mayor compromiso El aprendizaje debe acercar al estudiante a su realidad Aplicación inmediata

3.4. Programa Académico de Certificación en E-Learning Objetivos Generales •

Capacitar a los participantes en la gestión de ambientes de aprendizaje virtual mediante el uso de un LMS (Plataforma GES)



Brindar a los participantes conocimiento de los conceptos, modalidades y teorías de aprendizaje que fundamentan e-Learning.



Preparar a los participantes en diseño instruccional para el desarrollo correcto de cursos virtuales.



Formar a los participantes para actuar como moderadores en ambientes de aprendizaje virtual.



Incursionar en la creación de material bajo estándares internacionales.



Desarrollar en los participantes un conjunto de competencias que le capaciten para enfrentar las exigentes demandas de educación en la actualidad, específicamente en el campo de e-Learning.

El programa está estructurado en 5 módulos, con una duración de 175 horas. Cada uno de los módulos esta diseñado de tal manera que le proporcionara al participante las mejores prácticas en metodología y tecnología para la implementación de cursos e-Learning.



Módulo I: Manejo de un LMS (GES)

La finalidad de este módulo es enseñar a utilizar de manera eficaz las distintas herramientas que posee un LMS, específicamente las que proporciona la

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herramienta GES y entienda el propósito de cada una así como la importancia de las mismas dentro del desarrollo de su curso. •

Módulo II: Fundamentos del E-learning

Este módulo pretende profundizar en los fundamentos de la Educación virtual, a través de una aproximación teórica y práctica, analizando las diversas teorías acerca de esta modalidad, sus características, elementos y la importancia de los estándares •

Módulo III: E-Moderación

Este módulo pretende desarrollar las destrezas necesarias en los encargados de impartir un curso virtual, que le permita llevar a cabo las funciones de moderador y gestor del conocimiento, a través de la implementación de actividades de trabajo colaborativo, y la correcta aplicación de un modelo desarrollado específicamente para entornos virtuales [4]. •

Módulo IV: E-Actividades

El módulo analiza la importancia de las actividades en el aprendizaje virtual, la preparación y gestión de actividades en línea con éxito y finalmente, propone un modelo concreto de enseñanza - aprendizaje en línea [5]. •

Módulo V: Diseño Instruccional

Éste modulo pretende desarrollar en el participante las competencias necesarias para el diseño, implementación y ejecución de cursos e-Learning en cualquier disciplina, a través del planteamiento de una metodología de diseño curricular, desarrollo de contenidos y evaluación enfocados al e-Learning.

4. CONCLUSIONES Y FUTURO En Galileo vemos la necesidad de seguir trabajando en la unificación y simplificación de diversas interfaces dentro de .LRN con vistas a reducir la fricción cognitiva implicita. La incorporación paulatina del uso de AJAX ayudará a interactuar más fácilmente con la herramienta. La educación virtual (e-leraning) es un complemento perfecto para ahorrar tiempo presencial, rebajar costos, o dar libertad total de horarios, pero para la mayoría de las

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ocasiones resulta complicado trabajar únicamente con esta opción. Para el futuro, algunas herramientas que podrían suplir estas carencias podrían ser: 1. Videoconferencias de Grupo 2. Escritorios remotos y demostraciones al momento. 3. Equipos de trabajo distribuidos 4. Exámenes y evaluaciones con un control más directo Vemos como futuro inmediato el trabajar en integrar con herramientas y servicios en Internet disponibles hoy en día, los cuales son populares entre los usuarios finales. Tales como google search, flickr, second life y otros tantos. Es necesario integrar con tecnologías especializadas que brindan servicios que de otra forma sería costoso en tiempo y recursos desarrollar. Por tanto, las metodologías didácticas serán más favorables a la lógica de lo abierto en la medida en que potencien las colaboraciones, apoyos, agrupaciones de y entre estudiantes, con los contenidos en la Web y el docente apoyando el proceso como expertos en organización, síntesis y resolución de problemas basados en informaciones abiertas.

REFERENCIAS [1] Alan Cooper, Robert Reimann, About FACE 2.0, 2003 [2] Alan Cooper, The Inmutes Are Running the Asylum, 1999. [3] The Yahoo! User Interface Library (YUI), http://developer.yahoo.com/yui/ [4] Gilly Salmon, E-moderating, second edition, 2004 [5] Gilly Salmon, E-tivities, 2002

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Calidad en elearning: Criterios de calidad en el aprendizaje virtual – la experiencia de la URL Nidia Giorgis de Orozco Directora del Departamento de Educación Virtual Universidad Rafael Landívar Vista Hermosa III, Campus Central, Zona 16 Oficina H-330 Teléfono: 2426- 2626 ext. 212 [email protected] o [email protected]

La definición de calidad en la educación superior causa polémica y discusiones. Esto es aún mayor en ambientes virtuales y de aprendizaje en línea. A pesar de ello, algo tiene que hacerse para medir la calidad del elearning en nuestras instituciones. Al final, el usuario es quién decide y mide la calidad de la educación que obtiene. En esta ponencia se expondrán algunas ideas acerca de cómo evaluar la calidad de los servicios de elearning que ofrecen nuestras instituciones y cómo garantizar a los alumnos que recibirán lo que esperan. Algunos puntos a tratar son:   

Establecimiento de criterios acerca de la calidad Instrumentos de Evaluación Cómo investigar y determinar niveles de calidad

No se trata de replicar los errores en el aula presencial (potenciados) en las aulas virtuales. Es de aprovechar esta oportunidad para reflexionar qué estamos haciendo bien y qué estamos haciendo mal en las aulas presénciales, determinar las diferencias entre un aula presencial y una virtual, y potenciar lo que se hace bien. Además, aprovechar nuestras fortalezas para atacar o contrarrestar lo que se está haciendo mal. Como bien expone Frydenberg, Jia (2002), no se trata de inventar el agua azucarada. Al investigar el entorno mundial, Frydenberg, Jia (2002) determinó 9 elementos a tomar en consideración para evaluar la calidad de los programas virtuales: 1. Compromiso institucional 2. Infraestructura tecnológica 3. Servicios estudiantiles 4. Diseño instruccional y el desarrollo de los cursos 5. Enseñanza y servicios al profesor 6. Entrega o ―delivery‖ 7. Finanzas 8. Reglamentación y cumplimiento con leyes 9. Evaluación

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A continuación, se estudiarán los criterios propuestos por la autora de esta ponencia para evaluar la calidad de estos programas. Estos, no están escritos en piedra y pueden enriquecerse con su discusión ante la comunidad experta en el tema.

ETAPA

CRITERIOS Diagnóstico Identificación de Filosofía o marco

Diseño Identificación de objetivos Curricular Cohesión entre objetivos y filosofía o marco filosófico Aspectos técnicos considerados Completo Cohesión con diseño curricular

Estructura del diseño

Aspectos técnicos considerados Divido en módulos Diseño incluye trabajar con conceptos fundamentales (no solo a través de lecturas) Facilidad de acceso a actividades y materiales para participantes Sistemática Participativa

Diseño de actividades

Flexible Coherente con objetivos (competencias ) Rol del docente y alumno plenamente identificado Tiempo suficiente (temporalización) Promueven el pensamiento crítico, evaluación del estudiante Promueven la construcción del conocimiento

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ETAPA

CRITERIOS Orientan el aprendizaje del estudiante Se contempla horario de apoyo didáctico de tutores Productos de actividades claramente definidos Presentación uniforme Relevantes Materiales apoyo suficientes y pertinentes Guías y ayudas suficientes Comprensibles Motivantes

Materiales Disponibles oportunamente / Recursos Accesibles (el alumno cuenta con herramientas (software) necesario) Variado ( Video, audio, imágenes, VC, guías, laboratorios, simulaciones, etc.) considerando el aspecto tecnológico de usuarios Comprobación de que enlaces están activos y que los materiales son accesibles Respeto del derecho de autor Sistemática Objetiva Participativa Evaluación

Flexible Criterios claramente establecidos Relación directa con objetivos (consistente y coherente) Motivadora Continua

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ETAPA

CRITERIOS

Reglas para retroalimentación constante y clara por parte de tutores

Calendario de evaluación Esquema de evaluación

CONCLUSIONES 1.

No existe acuerdo respecto al concepto de calidad en los entornos virtuales, sin embargo, se han realizado avances respecto a la selección y descripción de criterios a considerar para su evaluación. 2. Es posible y necesario medir la calidad de los programas virtuales 3. Es importante, aprovechar que esta modalidad está relativamente empezando, para repensar qué estamos haciendo bien y qué estamos haciendo mal en las aulas presenciales, determinar las diferencias entre un aula presencial y una virtual, y potenciar lo que se hace bien. Se incluyen algunas preguntas de reflexión al respecto: 1.1. ¿Quiénes serán nuestros alumnos virtuales? 1.2. ¿Tendrán el autocontrol y la disciplina necesaria para salir adelante en su tarea de aprendizaje? 1.3. ¿El recurso humano docente estará capacitado para enfrentar esta nueva modalidad que absorberá nuestro ambiente educativo?

REFERENCIAS Duart, J. y Martínez, M. (2001). Evaluación de la calidad docente en entornos virtuales de aprendizaje. Encontrado en: [mayo, 2006] Frydenberg, Jia (2002). Quality Standards in e-Learning: A matrix of analysis. Irvine Distance Learning Center University of California. Visitado en Red el 30 de noviembre del 2007 en: http://www.irrodl.org/index.php/irrodl/article/viewArticle/109/189

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Santoveña, S. (2005) Criterios de Calidad para la Evaluación de los Cursos Virtuales. Unidad de Virtualización Académica. Universidad Nacional de Educación a Distancia (UNED). Publicación en línea. España: Granada. Año II, Nº4 Encontrado en: ttp://www.ugr.es/~sevimeco/revistaeticanet/numero4/Articulos/Formateados/calidad.p df#search=%22Sonia%20M%C2%AA%20Santove%C3%B1a%20Casal%2B%20CR ITERIOS%20DE%20CALIDAD%20PARA%20LA%22 Salas, I. (2006). Condiciones que favorecen la calidad de los cursos en Línea. Universidad Estatal a Distancia. San José.

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Scrutinising Competencies: Retraceable Clouds of Learning Goals in the APOSDLE System Victor Manuel García-Barrios1, Günter Beham2, Barbara Kump3 1

Institute for Information Systems and Computer Media, Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria [email protected] Know-Center, Knowledge Management Institute, Graz University of Technology, Inffeldgasse 21a/II, 8010 Graz, Austria [email protected], [email protected] 2

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Abstract. The APOSDLE research project aims at developing an integrated framework of tools to support work-place learning. The resulting system will assist workers during their tasks by connecting and optimising their working, learning and collaboration activities. For this purpose, the system needs complex models for the representation of workflows, competencies, knowledge domains as well as user profiles and interactions, among others. In that context, this paper focuses on the critical aspects that arise from conveying to system users how their individual task history, competence-based learning goals and learning activities are interrelated. As a result, a solution approach is presented, which places learning goals at the centre of a user profile visualisation tool. The solution approach follows the principles of dynamic lists and tag-clouds in order to improve the scrutability of individual user profiles and to overcome the difficulty of conveying in a human-readable form the usage of complex models. Keywords: Work-integrated Learning, Cloud-based Visualisation, Learning Goals, Competence Management, Scrutability, User Profiling.

1. INTRODUCTION In general terms, the development of personalisation-pertinent systems is conducted under the premise of one size does not fit all. The end-users of such systems are aware of the increasing amount of well‐tailored information they may access for their particular needs or goals. Moreover, they are aware of the fact that in the majority of cases, they have to pay the price or hazard the consequences of delivering personal data to ensure those services. According to the results of several surveys, most of the users are willing to do so; e.g. as stated in [1], 76% of the survey subjects expressed strong interest in receiving personalised content, and 45% were more likely to visit Web sites that provide personalised recommendations than sites without them. The high relevance of these systems can be identified in various application areas, such as e-commerce, recommender systems and adaptive e-learning ([2] [3]). Thus, rather than a trend, there exists a need for personalisation‐pertinent systems in distinct

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situations of modern life [4], e.g. in work-place learning in order to increase or optimise the individual competence level of corporate workers. But apart from the fact that personalisation continuously gains interest within the research and end-user communities, the success and efficiency of a personalised service depends strongly on its technological implementation. The core components of such systems have to deal with high accuracy when assuming to know their users’ interests and goals, i.e. the applied reasoning methods, the needed model representations as well as the acquired (or inferred) information in the individual user profiles are critical issues from the point of view of system developers. Against this background, one of the biggest challenges met by developers of personalisation-pertinent systems is making it easy comprehensible to users how and why the system has delivered a personalised service as well as which information in the individual user profile has been used, but at the same time, hiding the level of computational complexity behind the user interfaces. Thus, the information in the user individual profiles should be easily scrutable [5]. In general terms, the difficulty of conveying to end-users the structure, state, meaning and usage of an abstract model increases with e.g. the complexity of its internal representation and computation, the degree of its evolution, the lack of its human-readable descriptors, as well as the loss of the usability of its visualisation. Within the context of personalised services of work-integrated learning systems, this paper presents a visualisation solution approach that deals with the high and dynamic complexity of competence-related models in the first prototype solutions of the APOSDLE system. The work and ideas presented in this paper are the outcome of the APOSDLE research project (Advanced Process-Oriented Self-Directed Learning Environment). As stated in [6], the APOSDLE system offers personalised learning support to their users while working with existing corporate-associated information and contributing with new information to the corporate knowledge repository. Within the scope of the research project, these persons are called knowledge workers and include engineers, researchers, software developers, consultants and designers. The APOSDLE project follows a Learn@Work approach, i.e. learning takes place in the immediate working environment and context of the system user. In contrast with traditional e-learning systems, the system offers integrated support for the three roles of a knowledge worker at work-place: worker, learner and expert. The topics driving the reminder of this paper can be summarised as follows. The practical scope of the APOSDLE project is defined by the integrated support of the main activities of knowledge workers: working, learning and collaborating. Thus, the (semi)automatic identification of their task-based learning goals plays a central role in the system. In order to fill a competence gap in a working situation, the APOSDLE system personalises its services for the user and recommends documents, learning events or collaboration possibilities with experts. Due to the complexity of the models behind this functionality and taking into account that the users of the system need a view on the personalisation-pertinent behaviour of the system, a simple and efficient visualisation of the relationships among the interacting models is required. Chapter 3 deals with this problem and gives an overview on the proposed solution approach. The paper concludes making references to related work as well as underlining open issues and future work.

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2. WORKING, LEARNING, COLLABORATING This chapter gives an overview on the research ideas and goals of the APOSDLE project. It introduces into relevant terminology as well as presents the general aspects of the overall system architecture and of its first prototype implementation (section 2.1). The focus in this chapter is set on the notion of a learning goal. An overview on the competence-based model of learning goals is introduced, and based on that, main requirements for the visualisation of an individual chronology of applied & acquired learning goals are defined. This central point of attention is described in section 2.2. 2.1 Process Oriented Self-Directed Learning Within the scope of the APOSDLE project, and according to [8], the activities of a knowledge worker are mainly defined by overall goals and expected results instead of predefined task procedures. Thus, a knowledge worker may organise the structure of her activities with certain autonomy in terms of their timing and sequencing. As a consequence, a knowledge worker may switch to different tasks or domains in her workflows. This switching reflects the dynamics in a so called user context, wherein a knowledge worker switches to different roles in her working situations (e.g. from worker to learner, or from learner to expert). In APOSDLE, the notion of learning refers to the advancement of knowledge and skills of knowledge workers, and includes the following characteristics: (a) workplace learning is integrated in the current working tasks of knowledge workers and utilises existing resources; (b) work-place learning activities aim at enhancing the performance of working tasks; (c) from the point of view of knowledge workers, work-place learning may occur spontaneously or unintentionally; (d) the learning needs and goals of APOSDLE users are derived from the tasks they currently perform; (e) learning activities emerge either from making use of available knowledge sources or in the creation of new knowledge (e.g. during collaboration events); (f) the results of learning activities (i.e. acquired knowledge and skills) may be directly transferable to the worker‘s working situation. [8] The general system architecture of APOSDLE‘s first prototype, shown in figure 1 (top side), depicts its central software parts and their interrelations (APOSDLE Tools, APOSDLE Platform and Backend Systems). The architecture is based on a clientserver software system, whereby the implementation follows a SOA paradigm (Service-Oriented Architecture) [6]. The Tools provide an interface to the user of the System, while the Platform provides the server-side functionality of the System. The bottom side of figure 1 illustrates the two sets of tools at disposal: the Modelling Tools (for experts to create formal models of user environments) and the Workplace Tools (for knowledge workers to use during work-integrated learning). Thus, the APOSDLE Tools represent the degree of operational complexity of the system. The computational (or functional) complexity of the system is given by the APOSDLE Platform (see [6] for details), which is mainly in charge of providing (a) foundation functionality to the whole System (Tools and Platform) through its Classification Service, Homogenous Access component, Semantic Service and Structure Repository

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Manager, and (b) a way of recommending resources for work-integrated learning through its Associative Network component and User Profile Service. The latter issue is based on the context of the learner, thus, on the one hand the Associative Network searches and retrieves context-dependent learning sources, and on the other hand the User Profile Service manages fine-grained information about the learners and their individual contexts.

Fig. 1. General Overview on APOSDLE System and on its Tools (Modelling & Workplace) [6]

In short, the current learning context of a knowledge worker is calculated from the current states of those internal models of the system that reflect her current individual task and her advancement in terms of competencies. The result for a system user is then personalised set of recommended learning sources, which, after consumption, might cover an identified context-dependent competency gap. But this result is the outcome of complex calculations in and among the distinct components of the APOSDLE Platform, Tools and Backend Systems. And what if a user wants to understand how all this technical and formal ―stuff‖ has taken place? A scrutable user interface is needed. 2.2 Learning Goals in the Aposdle System The APOSDLE Sidebar (shown on the left side of figure 2) represents the main user‟s view on the result of the calculations of the APOSDLE System. For a working task being currently executed by a user, the needed (task-related) competencies are listed by the system. In addition, according to these competencies, a set of learning resources (documents and events) as well as expert contacts are also shown. Thus, the

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Sidebar expresses a just-in-time personal learning offer for the current working task of a user [7]. The system delivers this most-suitable learning offer based on the current user context of the interacting knowledge worker; a user context [9] is described by connecting (at least) competence, domain and task models, as illustrated by the meta-model of user contexts shown on the right side of figure 2.

Fig. 2. Left: APOSDLE Sidebar, the User‟s View on the APOSDLE System. Right: MetaModel for User Contexts, as used by the User Profile Service. [6]

An individual user context is a dynamic entity continuously derived from the analysis of individual working tasks and individually consumed learning sources. Therefore, an individual work-place environment reflects distinct individual learning needs at distinct points in time depending on the task at hand. Within this context, the main goal of the APOSDLE research project is to improve the analysis of learning needs by comparing the tasks already executed by users with those tasks to be faced in the future. For that purpose, user-computer interactions are tracked on real-time to obtain and optimise a fine-grained user model, which in turn builds the computing base for personalised, user-context-based learning recommendations (see as shown in the Sidebar). [6] From the point of view of the system‘s usability and user interface design, it is highly important (and challenging) to convey to users a comprehensible explanation of why (and how) these recommendations have been delivered at a certain point in time (past or present). Thus, an intuitive user interface is needed for APOSDLE users in order to scrutinise on the one hand the adaptive behaviour of the system, and on the other hand the history of the tracked observations and inferences of the system in the individual user model. The diagrams on the left side of figure 3 show the relationship of a task model element (Task) with a competence model element (Learning Goal) as well as the connection of a learning goal with tracked Learning Activities, which can be of the

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type Learning Event, Expert Contacted or Document Opened, depending on the learning resources consumed or experts contacted trough the APOSDLE Sidebar.

Fig. 3. Left: Relations Task - Learning Goal & Learning Goal - Learning Activities. Right: An Example of the States of the Models for a Knowledge Worker on the Task “t4”.

Given the assumption that the number of model elements behind the user contexts of a medium- to large-size company may be many hundreds, a node diagram in a user interface reflecting an individual user context history might not be clear and comprehensible enough (see ―(A)‖ on right side of figure 3, showing an example of a small part of such a diagram). The diagram ―(B)‖ on figure 3 reflects the adaptive behaviour of the APOSDLE system for a user that performed ―Task 4‖ in the past, whereby the APOSDLE system did not show in its Sidebar all learning goals (―lg3‖ to ―lg6‖) corresponding to the task (―t4‖), rather just those representing her knowledge gap (―lg3‖ and ―lg4‖). Furthermore, after tracking the interactions of this user within that task, an additional diagram is needed to show her that she executed some learning activities connected to e.g. ―lg3‖ (see ―(C)‖ in figure 3). In sum, the learning activities executed in ―(C)‖ mean a competence advancement regarding ―lg3‖ while working on ―t4‖. In turn, due to further connections of ―lg3‖ with other tasks, the system will consider this advancement in the future and eventually suppress the appearance of ―lg3‖ in the Sidebar. In particular this adaptive behaviour of the system should be conveyed to the user in a simple and intuitive way. In APOSDLE, this behaviour is reflected within its Web-based User Profile Management Tool using dynamic lists that show the working-learning-collaborating history of individual users.

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3. RETRACING A WORK-INTEGRATED LEARNING CONTEXT APOSDLE‟s User Profile Management Tool (UPMT) is a Web-based user interface that shows users the contents of their individual user profiles. This chapter focuses on those parts of UPMT presenting to users the states of their competence advancement. The UPMT of the first and second prototypes of the APOSDLE system comprises five sections: Business Card (to show personal data, such as name or email address), Preferences (including a sub-section for choosing a desired privacy level, and one for selecting preferences about collaboration tools), Working (to visualise task-based activities), Learning (to visualise issues regarding knowledge acquired), and Collaborating (to show details on individual collaboration-related events, such as chats, emails or ratings of experts). The implementation of the APOSDLE system is based on the Java Spring framework [10]. Its Web-based server side is represented by the Tomcat Apache server [11]. To avoid platform dependencies and to enable an AJAX-based client solution [12], the UPMT is built on GWT (Java-based Google Widget Toolkit [13]). The next two subchapters introduce main aspects of the UPMT sections Working and Learning to give users the possibility to retrace their activity history in the context of their competence advancement (based on the models and functionalities shown in the previous chapter). 3.1 Tasks vs. Learning Goals: Knowledge Applied The section Working in UPMT presents APODLE users two visualisation possibilities (Lists and Clouds, see respectively left and right side of figure 4) to provide a view on the relationships among their performed tasks and the corresponding learning goals. This view is called Knowledge Applied and enables users to scrutinise and retrace their competence advancement history as computed and tracked by the system.

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Fig. 4. View “Knowledge Applied” in APOSDLE‟s UPMT (left: as Lists; right: as clouds).

The main area of the Knowledge Applied view shows a user the set of already performed working tasks (―Main List: Task History‖ in Lists mode or ―Main Cloud: Tasks‖ in Clouds mode). By selecting one task, a second area is dynamically filled with the corresponding learning goals (―Sub-Lists: Learning Goals‖ or ―Sub-clouds: Learning Goals‖). The main problem of making such a visualisation scrutable for users relies on the fact that the explanations needed to convey the APOSDLE-context meaning of such Task-LearningGoal relationships consist of a large set of descriptive data including temporal, environmental, personal and computational conditions. For example, knowledge workers perform usually the same task several times trough their workflows and thus, depending on their overall task history, they consume continuously distinct learning resources and collaborate with distinct experts in distinct working contexts. This continuous advancement of competencies through their working activities increases at the same time the complexity of the cross-linked relationships among tasks, detected learning goals and recommended learning sources. Furthermore, a consumption of learning events, an opened document or an established collaboration (all of them being tracked by the APOSDLE system) is an indicator for a learning activity, and thus, influences also the degree in which competence advancement is calculated. For the APOSDLE system, all these factors represent some of the triggers to adapt the elements in the personal Sidebar of a knowledge worker. Thus, the system contains stored numerical values that can be used as key elements to express the frequency of occurrence of tasks, learning goals and learning activities. Consequently, these numbers convey intrinsically individual competence advancement. This is the reason of utilising the notion of Tag Clouds within the UPMT ([14] [15]). For example, as

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shown in figure 5, how a learning goal (―LG137‖) has contributed to competence advancement can be visually retraced through the representative font-size within the clouds.

Fig. 5. Segment of the “Clouds” View on “Knowledge Applied” in APOSDLE‟s UPMT.

On the on hand, ―LG137‖ seems to have had little impact within the task ―T 048‖, but on the other hand, its overall impact on ―T 048‖ and ―T 045‖ is very relevant compared with the other learning goals. Thus, not only from the personal but also from the point of view of the company, filling this gap was essential in the competence advancement of this specific knowledge worker. 3.2 Learning Goals vs. Learning Activities: Knowledge Acquired The figures in the previous sub-chapter show a way to provide users a possibility of retracing their task history, and simultaneously, a way to scrutinise the relationships of task-related learning goals. APOSDLE‟s UPMT extends for its users the visibility of personal competence advancement by showing in its section Learning the relationships among learning goals and learning activities (see figure 6).

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Fig. 6. Segment of the “Clouds” View on “Knowledge Acquired” in APOSDLE‟s UPMT..

Within APOSDLE, the learning activities of knowledge workers represent distinct ways of acquiring knowledge. On the one hand, the system recommends personal learning sources through the Sidebar depending on the current working task (see figure 2), and on the other side, it tracks which of these recommendations have been accepted and consumed by the users. Thus, the system collects data about consumed learning events, documents opened as well as about the transcripts of collaboration events. As knowledge workers may e.g. read a certain document or chat with a certain expert several times within distinct tasks, these events contribute in a distinct manner to competence advancement. Through the usage of dynamic font-resizing as in Tag Clouds, the frequency of occurrence of learning activities (i.e. times a learning source has been consumed) conveys to users the impact of their repetitive learning on the knowledge acquired for a certain competence (learning goal). Further, a colour-based visual differentiation of the types of learning activities is also given. For example, the cloud for the learning goal ―LG 051‖ in figure 6 shows that this user has contacted ―Ana‖ (a recommended expert in the knowledge domain of this learning goal) so many times that these collaborations have contributed more to acquiring the needed knowledge than the other learning activities. Further, regarding the learning goals visible in figure 6, this user seems to prefer collaborative events with experts rather than reading documents or consuming learning events.

4. CONCLUSIONS AND FURTHER WORK The APOSDLE system enables an integrative view on the working environment of knowledge workers through the connection of learning, knowledge and work spaces. This paper has given an overview on how the system deals with the learning context

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of knowledge workers by means of their competence advancement. Because of the complexity of computing recommendations out of the states of the models in the system, and due to the highly-crossed relationships among model elements, this paper proposes the usage of intuitive cloud-based views on individual states of user models in order to enhance and simplify their visualisation. APOSDLE‘S UPMT views Knowledge Applied and Knowledge Acquired are provided to users to scrutinise the chronology, relationships and descriptions of their performed tasks, achieved learning goals and executed learning activities. The concept proposed in this paper will be evaluated for the development of the third prototype of the APOSDLE system. Future work will be also the attempt to place learning goals at the centre of a single visualisation with adjacent dynamic relations to tasks and learning activities in order to provide an integrated view on the entire competence advancement of knowledge workers in the APOSDLE context. As the current implementation of the UPMT is a Web-based (in concrete, Ajax-based) solution, it is assumed that (and should be tested if) this cloud-based tool can be reused for integration into other e-learning solutions.

4.1 Acknowledgements The APOSDLE research project (http://www.aposdle.org) is partially funded by the European Community (http://europa.eu.int) under the IST priority (Information Society Technologies, http://cordis.europa.eu/ist) of FP 6 (Sixth Framework Programme for R&D), contract number IST-027023. The support of the following institutions is gratefully acknowledged: Know-Center Graz (http://www.knowcenter.at), funded by the Austrian Competence Center program Kplus under the auspices of the Austrian Ministry of Transport, Innovation and Technology (http://www.ffg.at) and by the State of Styria, Austria; and Institute for Information Systems and Computer Media (IICM, http://www.iicm.edu), Faculty of Computer Science at Graz University of Technology, Austria.

REFERENCES 1. ChoiceStream: ChoiceStream Personalization Survey, Consumer Trends and Perceptions, (2007), URL http://www.choicestream.com Last visit: 2008-13-01. 2. Kobsa, A.: Generic User Modeling Systems; in User Modeling and User-Adapted Interaction, Volume 11: Kluwer Academic Publishers, The Netherlands; pp. 49-63, (2001). 3. Hof, R., Green, H., Himmelstein, L.: Now it‟s YOUR WEB; in Business Week, Issue October 5th 1998; pp. 68-75, (1998). 4. García-Barrios, V.M.: Personalisation in Adaptive E-Learning Systems - A Service-Oriented Solution Approach for Multi-Purpose User Modelling Systems; Dissertation at Institute of Information Systems and Computer Media, Faculty of Computer Science, Graz University of Technology, (2007).

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5. Kay, J.: Scrutable adaptation: because we can and must; in Proceedings of 4th International Conference for Adaptive Hypermedia and Adaptive Web‐Based Systems, AH 2006, in Dublin, Ireland; Wade, V., Ashman, H., Smyth, B. (eds.), Springer (pub.); pp. 11‐19, (2006). 6. Consortium of APOSDLE: First Prototype APOSDLE - Deliverables D1.2, D2.2, D3.2, D4.2, D5.2.; (2007), URL http://www.aposdle.org/results Last visit: 200813-01. 7. Bonestroo, W., Ley, T., Kump, B., Lindstaedt, S.: Learn@Work: Competency Advancement with Learning Templates; in Proceedings of LOKMOL-2007, Crete, Greece; pp. 17-20, (2007). 8. Consortium of APOSDLE: APOSDLE Scope and Boundaries - Deliverable D6.1; (2006), URL http://www.aposdle.tugraz.at/media/multimedia/files/aposdle_scope_and_boundari es Last visit 2008-13-01. 9. Ulbrich, A., Scheir, P., Lindstaedt, S.N., Görtz, M.: A Context-Model for Supporting Work-Integrated Learning; in Nejdl, W., Tochtermann, K. (eds.), Innovative Approaches for Learning and Knowledge Sharing, LNCS Vol. 4227, Springer Verlag (pub.), Heidelberg; pp. 525-530, (2006). 10. Spring; Open Source, full-stack Java/JEE application framework; Web-site URL http://www.springframework.org - Last Visit 2007-12-18. 11. Apache Tomcat; Open Source, Java Servlet container; Web-site URL http://tomcat.apache.org - Last Visit 2007-12-15. 12. Garrett J.J.: Ajax: A New Approach to Web Applications; Adaptive Path, LLC, 2005-02-18; Web-site URL http://www.adaptivepath.com/ideas/essays/archives/000385.php - Last visit 200712-07. 13. GWT: Google Widget Toolkit; Open Source, Java software development framework for AJAX applications; URL http://code.google.com/webtoolkit - Last Visit 2007-12-05. 14. Hassan-Montero, Y., Herrero-Solana, V.: Improving Tag-Clouds as Visual Information Retrieval Interfaces; In Proceedings of the 1 st International Conference on Multidisciplinary Information Sciences and Technologies (InSciT2006), Workshop on Information Visualization; Mérida, Spanien (2006). 15. Rivadeneira, A.W., Gruen, D.M., Muller, M.J., Millen, D.R.: Getting Our Head in the Clouds: Toward Evaluation Studies of Tagclouds; In Proceeding of the 25 th International Computer/Human Interaction Conference (CHI2007); San Jose, CA, USA; pp. 995-998, (2007).

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Open Source Collaborative eLearning Ronald Aust1, Allen Quesada2 1 Educational Leadership and Policy Studies, University of Kansas, Lawrence. [email protected] 2 Facultad de Letras, Universidad de Costa Rica, San Pedro. [email protected]

Abstract. Open source collaborative eLearning draws on a constructivist perspective with learners actively engaged in exchanging ideas, negotiating meanings and creating knowledge resources that are freely and openly shared. Collaborative eLearning is valuable in transnational university partnerships where faculty, students and programs benefit mutually from diverse perspectives. This investigation of a teaching and research partnership, with a United States and a Central American university, involved faculty in reviewing curricula, collaborative teaching and engaging students in collaborative knowledge construction. Surveys and interviews with 52 participants over two semesters revealed significantly higher ratings after refining strategies and adopting a six event Collaborative Knowledge Construction model. Results support an “Any Place Same Time” approach and the continued growth of transnational partnerships to advance open source eLearning. Keywords: open source, collaborations, eLearning.

transnational,

constructivist,

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1. INTRODUCTION: REFLECTIONS ON OPEN SOURCE COLLABORATIVE ELEARNING AND TRANSNATIONAL EDUCATION Our research and development efforts are designed to establish meaningful and sustainable transnational collaborations between faculty, students and academic programs across universities and ultimately K-12 schools. These mutually beneficial collaborations involve the sharing of ideas and resources concerning coursework, curricula, academic programs and co-teaching activities. We use the term open source collaborative eLearning in the broad sense where open source refers to all knowledge resources that are jointly constructed and openly shared. This definition is similar to Thomas Friedman‟s [1] broad interpretation of open sourcing as a key factor that is leveling global societies and economies. Computer science professionals associated with the open source software movement [2], often use the term open source in a more focused reference to software licensing agreements that allow the general public access to software code under relaxed or non-existent copyright restrictions. Certainly many of the policies and ideas that are now incorporated in open source knowledge resources were derived from the open source software movement. This migration of open source ideas is exemplified in the way that leaders in open source software, including MIT, are now offering open courseware that covers topics on engineering, health sciences, the humanities and arts. The addition of open source expands perspectives on transnational collaborative eLearning. It is not only that people from different parts of the world are collaborating for a time to build knowledge resources. Open source adds the perspective of a continuous community effort to address and openly share ideas and solutions on important problems. Our interpretation of transnational open source eLearning begins with the premise that partnerships between institutions will be mutually beneficial where faculty, students and programs contribute significantly in advancing learning environments. This differs considerably from descriptions of “off shore” transnational education [3] where sending institutions are located in a country different from where the receiving institutions‟ learners are based. We prefer the literal interpretation of transnational education as “education that transcends national boundaries” without the diminishing stipulation that there are unique sending and receiving institutions. This interpretation involves faculty, students and institutions growing together in mutually beneficial learning environments that support sustainable constructivist pedagogy.

2. OPEN SOURCE COLLABORATIVE TEACHING This investigation of an international university partnership highlights some of the strategies that we used to enrich our courses and academic programs at the University of Costa Rica (UCR) and the University of Kansas (KU). Every university

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partnership will be unique and the overall goals may differ. Our pilot development involved, three one-on-one faculty partnerships covering courses in Educational Technology, Language Analysis, and Second Language Acquisition at both universities. We focus this investigation on the Educational Technology partnership that reviewed curricula, syllabi, instructional strategies, technology resources, and facilities to assess how we could combine our strength to improve the quality of our courses and academic programs. We drew on previous experience in designing interfaces and systems for eLearning content [4, 5], faculty professional development [6] and using technology to enrich language learning in rural urban K-12 schools [7]. We also benefited from a rich heritage of experiences between UCR and KU including a 50-year formal partnership in international studies. The University of Costa Rica is the largest university in Costa Rica with many established ties to businesses, industry, and K-12 schools. UCR founded the first Teaching English as a Second Language (TESL) program in Central America. This offers KU's faculty and student a unique perspective of current trends and strategies for implementing successful and sustainable programs in Latin America. Ranking in the top 20 Schools of Education in the United States, KU‟s School of Education has many outstanding educational technology initiatives, academic programs, and 25 years of experience in preparing TESL doctorates worldwide. International partnership between other universities will likely offer different, but no less significant, opportunities for expanding diversity and growth in research and academic programs. After analyzing course syllabi and teaching strategies, the faculty partners determined the topics that they would teach. The primary instructor at the host institution taught most of the classes and the "guest" instructor taught at least one live and one online session at the host. This method capitalized on the understandings and experiences of each faculty member to enrich the instructional quality in both courses. For example, the faculty partner at UCR is experienced in working with diverse students in integrating technology in language learning. This topic fit well in the Integrating Educational Technology class taught at KU. The faculty partner at KU had experience in designing user interfaces with online glosses, a topic that fits well within the Technological Resources for Language Learning course taught at UCR. Faculty posted their presentations in advance so that students could download the materials before the presentations. We sought to develop an affordable strategy that employed standard audio equipment, video cameras, free teleconferencing software (Skype) and a readily accessible (PDF) format for presentation. The presentations typically lasted about 40 minutes with 15 minutes for questions. As our experience increased we began to add pre and post activities to augment the sessions where students were asked to review notes, locate materials in advance, and provide followup reports. Figure 1 shows the layout for the online presentations. In this case, students could see the instructor in the upper left corner with the chat board directly below and the presentation as a PDF on the right side of the display screen.

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Fig. 1. Online Collaborative Teaching

Fig. 2. Knowledge Construction Teams

3. OPEN SOURCE COLLABORATIVE KNOWLEDGE CONSTRUCTION Janet Salmons [8] defined collaborative eLearning as, “constructing knowledge, negotiating meanings, and/or solving problems through mutual engagement of two or more learners in a coordinated effort using Internet and electronic communications.” Salmons described the levels of engagement in collaborative eLearning as: dialogue, peer review, parallel review, sequential collaboration, and synergistic collaboration. Stahl [9] noted that successful cooperative behavior requires trust-building activities, joint planning, and team support. Duffy and Cunningham [10] also note that the primary outcome of collaborative knowledge construction is the dialogs and reflexivity among learners. In addition to sharing teaching presentations we also sought to engage students in activities where they collaborate to construct new knowledge. Drawing on the philosophies of Piaget [11], Papert [12], Bruner [13], and Vgotsky [13], this constructivist approach assumes that the most meaningful learning occurs when learners are actively involved in mentally constructing new information. Rather than the direct one-way “pouring in” of knowledge that often characterizes traditional instruction, constructivist‟s pedagogy focuses on the importance of peer relationships, the context, the learning environment and learners‟ beliefs. With these constructivist principles in mind, we developed instructional strategies for piloting the Collaborative Knowledge Construction activity in the summer semester. We planned to form teams of 3 to 7 students with approximately equal representation from each institution and use an approach advocated by Hitz [14], to engage learners in team projects where they construct artifacts that demonstrate their new knowledge and skills. Our classes did not meet at the same time so the communication among the international team members was organized through the knowledge construction team web site shown in Figure 3 and relied heavily on

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asynchronous email and threaded discussions. We did have synchronous Skype teleconferences with the two classes where the faculty met and discussed the project status. We also instructed the students in the use of Skype and encouraged them to arrange live teleconferences with their group on their own time. Our first task was to assist student in selecting the topics with international implications that would be interesting and meaningful to students and relevant for our courses. During the initial course and program review we noted that both the KU and UCR educational technology courses engaged students in building resources that focused in part on one of the six National Educational Technology Standards (NETS) to advanced students‟ social awareness and encouraged them to "apply technology resources to enable and empower learners with diverse backgrounds, characteristics, and abilities." Aside from being relevant to critical aspects of both courses, we saw these “social awareness” activities as a means to begin the process of establishing a more open source approach to building knowledge resource on topics that are of interest and importance to transnational audiences. Furman and Negi [15] observed that social work educators must begin to help students become more comfortable with transnational exchanges. When forming teams to engage in open source collaborative eLearning, some examples of topics that students investigated include: Bilingualism, Healthy Diets, Ageing Populations, Immigration, Worldwide Adoption of eCommunities and e-Books, Global Warming, Reef Pollution, Rainforest Destruction, and the Central American Free Trade Agreement (CAFTA). The review of literature and planning for the pilot projects raised several questions regarding these transnational collaborative activities. How do online teaching presentations compare to traditional face-to-face communications? Did the eLearning technologies support adequate faculty-students and student-student interactions? Are the topics and course content deemed important and relevant? Did the activities advance understanding of culture and language? How can we best design the collaborative knowledge construction activities to maximize cooperation and engagement of all team members? Do attitudes and benefits from collaborative eLearning differ across gender, institutions, ages or experience with online courses? With these questions in mind we framed the following research with the understanding that we would use lessons in the pilot phase to revise and extend the subsequent collaborative partnerships.

4. METHOD Participants: The investigation involved two faculty and 52 student participants, 22 from UCR and 29 from KU who ranged in age from 22 to 58 years with a mean age of 27. Data collection took place during the Summer (N=24) and Fall (N=27) with 16 male and 36 female participants. The multicultural nature of participants included natives from Brazil, Canada, China, Columbia, Costa Rica, Panama, Peru, Puerto Rico, Saudia Arabia, South Korea, Taiwan, Tunisia, Turkey and at least 9 of the United States. Survey Instrument: We designed an online survey on international collaborative eLearning using a 5 point likert scale (1=strongly agree, 3=neutral and 5=strongly

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disagree) with items regarding the technical clarity and relevance of presentations, comparisons to traditional face-to-face presentations and the success of interactions among the instructors and other team members in the collaborative knowledge construction activities. The survey collects information on Age, Gender, Online Course Experience, Academic Major and Institutional Affiliation. The survey also included open-ended questions including: “How does this online collaboration activity compare to a traditional face-to-face educational collaboration. How is it better? How is it worse? What are the advantages and/or disadvantages of international collaborations? Procedures: Although the faculty partners had previous experience in the collaborative co-teaching, the collaborative knowledge construction was a new activity that we first piloted in the spring semester. Both of our courses were held in lab environments, and we conducted site visits midway during the semester to meet with faculty, plan activities and review technical configurations. We administered the survey at the end of the Summer and Fall semesters using the online SurveyMonkey system.

5. RESULTS Survey Results: We compiled descriptive and analytical statistics using SPSS from the survey data. After reviewing the survey results from the Summer (N=24) semester and the responses to the open ended items we made several adjustments to our courses and strategies and administered the survey at the end of the Fall semester (N=27). Table one shows the results on items from the survey on collaborative eLearning. Means for the summer semester class were near “neutral” (M=2.9). The fall semester means (M=1.9) were clearly lower indicating, “agree” to “strongly agree.” The significantly lower values for the fall semester indicate that the participants agreed more strongly with the positively worded items. Table 1. Collaborative eLearning Survey Results Across Semesters Survey Items (abbreviated)

1. was easy to see. 2. was easy to hear. 3. covered important content. 4. had an international flavor. 5. helped me learn important ideas. 6. made it easy to ask questions. 7. was as good as

Sum mer

Fall

Sum mer

Fall

F(1, 50)

M= 2.2 M= 3.5 M= 2.3 M= 2.4 M= 2.7 M= 3.0 M=

M= 1.6 M= 1.8 M= 1.7 M= 1.5 M= 1.8 M= 2.2 M=

SD= .82 SD= .93 SD= .99 SD= .75 SD= .81 SD= .91 SD=

SD= .84 SD= .75 SD= .81 SD= .64 SD= .85 SD= .97 SD=

5.9 48 49. 41 4.0 64 9.0 04 16. 33 7.3 3 18.

Sig.

P=.01 8* P=.00 0** P=.04 9* P=.00 4** P=.00 0** P=.00 9** P=.00

52

a face-to-face. 8. learned about another language. 9. easy to interact with the instructor. 10. easy to interact with other students. 11. learned more about other cultures. Average Means:

3.3 M= 3.3 M= 3.3 M= 3.0 M= 2.4 M= 2.9

1.9 M= 2.7 M= 2.1 M= 2.0 M= 2.0 M= 1.9

1.2 SD= 1.1 SD= 1.3 SD= 1.2 SD= 1.1 SD= 1.0

1.0 SD= 1.3 SD= 1.0 SD= .98 SD= 1.1 SD= .93

83 2.5 6 12. 50 11. 14 1.0 16

0** P=.11 6 P=.00 1** P=.00 2** P=.31 8

Survey items were rated on a 5 point Likert scale (1= strongly agree, 3= neutral, 5= strongly disagree) * P characters have being escaped. 3.1 User Models server The server was designed to use XML files in the request/response calls. XMLBeans have been chosen as the data binding framework because of its compliance with the requirements and its easy learning curve. It provides the following interfaces: • getUserModelAttribute: takes the identifier of the model where the attribute has to be looked for and an XML file where the conditions for the queries are defined. It sends back another XML file with the data. • setUserModelAttribute: allows the client to add to a previous node a new one by sending an XML file specifying the target node, the new value and the identifier of the model in the database. Due to the IMS specification does not have a field that could be used as identifier for each node, the server may found several nodes that match the client request. In this case, the client is provided with the parameter "allowDuplicated". If true, the server will set the value in all found nodes. If false, and more than one node is found, the server will abort the operation and send an error. • updateUserModelAttribute: similar to the previous one but instead of adding a new node, it will update an existing node with the received value. The target node and the new node must be objects of the same type. • deleteUserModelAttribute: deletes the targeted node (or nodes if the client marks "allowDuplicated" as true). • getUserModel: returns the XML file of the model stored in the database. • setUserModel: stores the received model in the database and returns the identifier assigned (auto-incremental sequence) in the database. • updateUserModel: updates the existing model with the new one. • deleteUserModel: deletes the model from the data base. • getIdentifier: since there is not an univocal identifier in the IMS specification for the learner profile, for this implementation we have agreed that the client manages as identifier the value stored in the element contenttype.referential.sourceid.id. However, the request to the database defined by the above methods has to be done with the

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identifier created in the database. Thus, this method provides the database identifier(s) associated to the given contenttype.referential.sourceid.id element. If the client logic is properly implemented, only one database identifier should be returned. 3.2 Device profile server The device profile server has been implemented using Deli v.0,9,78 to parse the UAProf files because the library developed by Sun to work with CC/PP called jsr 188 (which was the initial option) is not currently supported and has not been updated to work with the UAProf 2.0 version. Deli is a library developed by Mark Butler in the HP labs9 that uses Jena10 to easily parse RDF. It does not fully support the UAProf 2.0 version, but is the library that the Open Mobile Alliance recommends the manufacturers to use to test their profiles. For the device profile server, the following interfaces have been implemented: • getIdentifier: when a profile is set in the database the server stores creates the autoincremental value as the identifier. However, the profile manages one of the fields of the profile as the identifier. As the corresponding method in the user model, this one allows the client to know the value of the database identifier associated to this client identifier. Since it can be more than one client identifier, more than one database identifier could be returned. • getDeviceProfileParameter: receives the identifier of the profile in the database and the device feature the client wants to know, and returns the value (or values) of this parameter in the device. • getDeviceProfile: returns the RDF profile stored in the database • setDeviceProfile: stores the profile in the database and returns the identifier assigned to it. • deleteDeviceProfile: deletes the device profile from the database. • updateDeviceProfile: changes the device profile with the new one for the given database identifier.

4. INTEGRATION IN THE OPENACS/DOTLRN FRAMEWORK The integration in OpenACS/dotLRN framework can be done by using the xo-soap package, a SOAP protocol plugin for xorb. Xorb stands for XOTcl Request Broker, an OO-interface that wraps up OpenACS‘s Service Contracts and provides a generic

8 Deli:

http://delicon.sourceforge.net/

HP Labs: http://www.hpl.hp.com/techreports/2001/HPL-2001-260.html 10 Jena: http://jena.sourceforge.net/ 9

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invocation dispatcher, supporting local (original ACS Service Contracts) and remote redirection (protocol-plugins: xotcl-soap) [3]. Considering the current information architecture in OpenACS/dotLRN, we propose to link the user model and device profile information from the Control Panel. Both will present in a user-friendly way, the information about user and device. Regarding the user model, when the user is registered in the system, an instance for her user model is created in the server. This information should be sent to the user model server. As the user fills in her personal information (name, address, email, ...) the corresponding data should be update in the server, too. Moreover, as the user enrols in the platform communities and courses, and progresses in the course with qualifications, update of the corresponding information have to be sent, too.. From the control panel, the user will be able to see the information stored in her profile, such as the learning styles, activities in the communities and courses, and so no. If the user is dropped from the system (or does not want to have her profile stored, the profile has to be deleted). For the device profile, the user will be able to access information about the features of the device she is using, so the information can be sent within the request. In any case, the user may be able to upload the information about her device profile into the server. In this way, the information presented to the user can be adapted to the characteristics of the device of the user.

5. CONCLUSIONS AND FUTURE WORKS A prototype for a user model and device profile service has been implemented in an Axis2 framework. This solution supports IMS-LIP, IMS-AccLIP and CC/PP specifications. Considering the web services support available in OpenACS/dotLRN, we have made a proposal upon the OpenACS/dotLRN framework to integrate the services offered. In this way, the functionality of OpenACS/dotLRN framework can be extended to support the users in a personalized way, by considering their needs and preferences as well as the device being used. This support will facilitate the production of adaptation tasks in terms of a dynamic support based on recommendations, as described in [4]. IMS AccLIP is being internationalised in the ISO/IEC JTC1 standard on Individualised Adaptability and Accessibility in Learning, Education and Training (24751) as ISO Personal Needs and Preferences [5]. Since this standard is RDF based, a similar solution as the device profile server will have to be implemented in the user model server to manage. The follow-up of this work is carried out in the framework of the EU4ALL project11. Furthermore, IMS is currently developing version 2.0 of AccLIP and it is expected to harmonise with the ISO version. The implementation of this prototype has encountered various difficulties. On the one hand, regarding the user model specifications, LIP and AccLIP specifications sem not to have been developed in collaboration, as the naming schema and XML types are different, which did not allow reusability of the code to implement both. 11 EU4ALL:

http://www.eu4all-project.eu/

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Moreover, the AccLIP schemas in the document validate only with a schema version that is not publicly available. On the other hand, regarding the device profile, it seems that UAProf has not in practice being accepted by the market. There is no library that fully supports the 2.0 version. In fact, Sun was going to develop a library for it, but stopped the project. And the device profiles found are usual for old mobiles, except for Nokia. The way to go seems to be an extension of the UAProf called Wireless Universal Resource File (WURFL). It consists on an XML configuration file which contains information about capabilities and features of many mobile devices and it is basically an extension of UAProf.

REFERENCES 1. Santos, O.C., Boticario, J.G., Rodríguez-Ascaso, A. and Barrera, C. Modelling Learners Interaction Preferences in dotLRN. OpenACS/dotLRN Spring Conference. International Conference and Workshops on Community Based Environments, 2007. 2. Cuartero, A. Implementation of a User Model based on standards and open source to support adaptation tasks. Final Degree Project. Supervised by Boticario, J.G. and Santos, O.C. aDeNu Group, 2008. 3. Sobernig, S. Xorb/xosoap. Remoting for OACS / .LRN. dotLRN Conference Boston, 2006. 4. Santos, O.C., Raffenne, E, Granado, J. and Boticario, J.G. Dynamic support in OpenACS/dotLRN: Recommending actions for all. Proceedings of the International Conference and Workshops on Community based environments, 2008 (in press). 5. ISO IEC JTC1 Individualized Adaptability and Accessibility in E-.learning, Education and Training - Part 2: Access For All Personal Needs and Preferences Statement most recent public drafts on http://jtc1sc36.org/doc/36N1140.pdf, visited 14rd December 2007 6. ISO IEC JTC1 Individualized Adaptability and Accessibility in E-.learning, Education and Training - Part 3: Access For All Digital Resource Description, most recent public drafts on http://jtc1sc36.org/doc/36N1141.pdf , visited 14rd December 2007

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Dynamic support in OpenACS/dotLRN: Technological infrastructure for providing dynamic recommendations for all in open and standard-based LMS Olga C. Santos1, Emmanuelle Raffenne1, Jorge Granado1, Jesús G. Boticario1 aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, C/Juan del Rosal, 16. 28040 Madrid, Spain {ocsantos, eraffenne, jorge.granado, jgb}@dia.uned.es http://adenu.ia.uned.es/ 1

Abstract. The paper presents a recommending service that we have integrated into the OpenACS/dotLRN framework via web services. The recommending service that we are currently developing at aDeNu group is to be used by learning management systems (LMS) to ask for the appropriate recommendations for the user currently working in the LMS. These recommendations are very diverse and currently are being produced in an inclusive way with standard-based user modelling techniques. Their objective is to provide dynamic support to the user at the course execution to overcome impasses that learners may encounter, and which are not covered by the design of the course. To provide this support, the learner‘s interactions and their evolution overtime are considered. Keywords: OpenACS, dotLRN, Accessible services, Open architectures, Web Services, Life Long Learning, Standards, Adaptation, User Modelling, MultiAgent Systems, Machine Learning techniques, Artificial Intelligence.

1. INTRODUCTION A key working area at aDeNu1 Research Group is focused on developing a flexible, open, standard-based architecture to support universal online access to the life long learning (LLL) by applying user modelling and machine learning techniques. The main purpose here is to produce adaptive interfaces that cope with the diverse functional needs for all, including people with the so-called disabilities and an increasing number of adult learners whose main educational option is LLL. This approach is applied in different projects we have been involved (aLFanet 2, SAMAP3, FAA4) as well as the ones currently in progress: ALPE 5, EU4ALL6, ADAPTAPlan7. 1 aDeNu:

https://adenu.ia.uned.es http://adenu.ia.uned.es/alfanet/ 3 SAMAP: http://scalab.uc3m.es/~dborrajo/samap/ 4 FAA: http://adenu.ia.uned.es/faa/ 5 ALPE: http://adenu.ia.uned.es/alpe/ 2 aLFanet:

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In particular, EU4ALL is intended to improve the efficiency and efficacy of implementing Accessible Lifelong Learning (ALL) following three key strategies [1]: 1. That the technology that mediates lifelong learning does so accommodating the diversity of ways people interact with technology and the content and services it delivers. 2. That this technology is used to bring support services to disabled learners. 3. Supporting services and technical infrastructure that enable teaching, technical and administrative staff of educational institutions to offer their teaching and services in a way that is accessible to disabled learners. According to the first and third strategies, there is a need for modelling user requirements and providing adapted responses to them to ―accommodate the diversity of ways people interact with technology and the content and services it delivers‖ to ―offer their teaching and services in a way that is accessible to all learners including those that are sometimes described as disabled learners‖. Nevertheless, in aLFanet project we detected some difficulties in developing and modelling standard-based adaptive scenarios [2], which we are currently trying to solve in ADAPTAPlan project. The goal of ADAPTAPlan is to reduce the design effort, which is proven as a major bottleneck in adaptive standard-based learning management systems that support the full life cycle of eLearning [3]. Current educational specifications assume an ideal design scenario where all required elements can be managed at design time. Nevertheless, diverse issues make unaffordable to design in advance all possible situations: a) learners‘ performance, b) synchronization and temporization issues, c) evolving learners‘ needs and preferences, d) adaptation process sustainable over time, e) pedagogical requirements affected by runtime adaptations and f) dynamic modelling. Universal design approaches does not suffice. Therefore, there is a need for dynamic support at runtime [4] that considers the learners‘ interactions and their evolution over time. In order to deliver contents, activities and services following the appropriate instructional design and fostering collaboration, a learning environment that supports this wide range of functionalities is needed. Moreover, these functionalities have to be provided in an accessible and adaptive way. Although the current state of the art in learning environments shows that there is no environment that fulfil these requirements, our experience shows that the OpenACS8/dotLRN9 framework is the most suitable platform [5]. OpenACS/dotLRN is an open source learning environment that provides the main functionality in terms of learning and collaboration services, and whose internal architecture and data model allows us to consider accessibility and adaptation. The functionality provided comprises, among others, calendar, discussion forum, file storage, notifications of members‘ contributions, management of user preferences, user tracking of interactions, assessments in IMS-QTI10 standard, management of IMS-CP11 and SCORM12 based courses and IMS-LD13 pedagogical designs. 6 EU4ALL:

http://www.eu4all-project.eu/ http://adenu.ia.uned.es/adaptaplan/ 8 OpenACS: http://openacs.org/ 9 dotLRN: http://dotlrn.org/ 10 IMS-QTI: http://www.imsglobal.org/question/ 11 IMS-CP: http://www.imsglobal.org/content/packaging/ 7 ADAPTAPlan:

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Moreover, regarding the interface, it provides the Zen theme based on style sheets which are easily configurable to change the user interface according to the users‘ preferences. Although not external accessibility reviews have been published so far, OpenACS/dotLRN framework claims itself to be compliant with WAI WCAG14 AA for the front-end of the platform (i.e. there the users access to use the services). Unfortunately, preliminary studies done on the back-end (e.g. in the course administration) show that professors face big accessibility problems when administering the educational packages for SCORM, IMS-QTI and IMS-LD (i.e. LORS, Assessment and Grail packages) [6]. Regarding usability, dotLRN has obtained the highest score in a heuristics comparison with two of its main competitors (Moodle and Sakai) [7]. Under this context, this paper presents our ongoing work on providing a web services support in OpenACS/dotLRN framework for a recommending system. This recommending service it to be used by learning management systems (LMS) to ask for the appropriate recommendations for the user currently working in the LMS. It focuses on providing user-centred services that consider individual user‘s needs and preferences, past interactions, the current context and psychopedagogical guidelines. It defines the service provision in a reusable way, integrating learning design in their definition and establishing clear procedures and measures for quality assurance. In particular, the paper introduces relevant issues to be considered in the user modelling and the recommending process and presents the technological support provided in the first prototype, which is intended to support in a general way recommendations to be integrated in different LMS.

2. USER MODELLING AND THE RECOMMENDING PROCESS The application of user modelling techniques to provide adaptation is very diverse [8], including any personalised service that is to be provided by the user in an electronic environment. These techniques deal with storing user information and matching users with the appropriate adaptation strategies considering their preferences and the context at hand. Different users of a system have different interests and sets of needs that may evolve over time. Moreover, system responses can often be improved in terms of usability by using information derived from detailed user interactions. This information can be used to guide system behaviour by producing adapted responses in terms of recommendations. Recommender technology has been widely used [9] and can be applied to provide dynamic support to learners during the course execution in an inclusive way. In particular, it can be used to support learners overcome impasses at course execution (runtime). Recommending systems in education can be used to guide people to interesting materials [10] or services based on opinions or behaviours of others and can be personalized to the preferences of different users. There is a large design space of alternative ways to organize such systems. The information that other people 12 SCORM: 13 IMS-LD:

http://www.adlnet.gov/ http://www.imsglobal.org/learningdesign/

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14 WCAG:

http://www.w3.org/TR/WAI-WEBCONTENT/

provide may come from explicit ratings, tags, or reviews, or implicitly from how they interact with the system. The information can be aggregated and used to select, filter, sort or highlight items. In order to support learners at runtime, it has to be described what, when, how and why recommend [11]. At the recommendation process, the context, the record of interactions and the user model are used to produce recommendations. A key issue in the recommendation process is the follow-up of the recommendation provided, to measure the degree of success. To support this approach, an Accessible and Adaptive Module (A2M) [4] is being developed on top of a multi-agent architecture (of Jade15 agents). It is based on open software solutions and artificial intelligence (AI) techniques that makes a pervasive use of standards to i) model learners and resources accessibility features, ii) follow the course design, iii) generate the presentation of the information and iv) communicate with other systems. The objective of the A2M is twofold: 1. Update the user models from the learners‘ interactions with machine learning techniques. 2. Generate dynamic contextual recommendations during the course execution based on these models and collaborative filtering and collaborative content techniques. The dynamic support is provided to overcome the impasses that learners may encounter at the course execution, which are not covered within the design of the course. In this approach, the design is used to build the skeleton of standard-based models which are dynamically updated according to learners‘ interactions over time with machine learning techniques [12]. 2.1. First approach for modelling The first step has been to apply and relate existing specifications to promote reusability among systems. Moreover, complying with specifications contribute to build open models, which can be accessed by the learners and help to increase the learning performance [13]. ADAPTAPlan approach as described in [3] has been followed. There, a proposal for linking different educational specifications to support the dynamic modelling during the learning process is presented and three types of user characteristics are considered to generate the adaptation: Felder learning styles, which define several dimensions regarding how people process information [14, 15]. The knowledge competency level per course objective based on Bloom‘s taxonomy [16], whose improvement is measured through IMS-QTI questionnaires. The collaborative competency level per course, which is computed with AI techniques, which take into account the usage of the course services. Moreover, the device capabilities as introduced in [17] as well as the accessibility preferences of the users are also considered.

15 JADE:

http://jade.tilab.com/

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The learning styles, the knowledge and collaborative competency levels, and the accessibility preferences are stored in the learner profile. This learner profile is defined in terms of two IMS specifications. In particular, the learner profile combines IMS Learner Information Package16 and IMS Access For All17 specifications. The former is a collection of information structured upon the following elements: accessibilities, activities, affiliations, competencies, goals, identifications, interests, qualifications, certifications and licences, relationship, security keys, and transcripts. The later defines the preferences or needs for alternative presentations of resources, alternative methods of controlling resources, alternative equivalents to the resources themselves and enhancements or supports required by the user. These preferences or needs would be declared using the IMS-LIP accessibility element of the specification. To model the device capabilities, we use the Composite Capabilities Preference Profile (CC/PP)18 specification, and we have applied the User Agent Profile base vocabulary19 from the Open Mobile Alliance (OMA). The device to access the LMS have traditionally been personal computers. However, there is a significant growth in the usage of PDA or smartphones, among others, to access learning contents and perform associated learning activities. This new paradigm is being called the mlearning (mobile learning). 2.2. First approach for producing recommendations For the recommendation process we are considering an hybrid approach that consists on a part based on knowledge (the filter) and a part based on learning (the guide), as proposed in [18], [19]. For the first type, the user model has to be induced in some way from user interactions. Three techniques have been defined: Collaborative filtering techniques: predict the utility of the objects for each particular user based on a database of scores from other users. Scores can be obtained implicitly (inferring from the user behaviour) or explicitly (from direct voting of each item by each user). These type of systems are based on people-to-people correlation ('users like you' – assumes users will prefer like-minded prefer and dissimilar dislike, based on objects ranking by users), which allows its application to any kind of object. This means that no specific information is required for the objects, but if the objects have not been rated in advance by the users, they cannot be recommended. Content based techniques: also build the user model from the scores (explicit or implicit) on the items using supervised machine learning to induce a classifier to discriminate between interesting and uninteresting items for the user. Each item is represented by a set of descriptors and models are built by machine learning algorithms using the items rated by a user as training examples, and the descriptors as the predictive attributes (i.e. the attributes that are to be learnt). Therefore, the technique used is called item-to-item correlation ('people who did this also did...' -

16 IMS-LIP:

http://www.imsglobal.org/profiles/ http://www.imsglobal.org/accessibility/index.html 18 CC/PP: http://www.w3.org/Mobile/CCPP/ 19 UaProf: http://www.openmobilealliance.org/tech/profiles/ccppschema-20030226.html 17 IMS-AccessForAll:

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connects users to items they may be unaware of based on the items features). Demographic techniques: the techniques used are similar to collaborative filtering, but instead of using the scores to find similar users, they use personal attributes and classify users in stereotypes. The interest for an object is predicted from the scores of demographically similar users. They also use people-to-people correlation. For the second type, the user model is filled in explicitly by the users or introduced externally in terms of rules. Utility based: provide recommendations after computing the utility of each object for the user. The problem is to build the utility function. Usually, each user has to built her own preferences function by assigning a weight to each possible feature of the existing objects, such as price, quality, etc. Knowledge based: recommend items from inferences regarding the needs and preferences of the users. They have an explicit knowledge (usually defined in terms of rules) regarding the relation among items and user needs. For any of the cases above, three elements are considered for producing the recommendation: a) the initial data (what the system knows before the recommendation), b) the input data (information from the user that is required by the system to generate the recommendation), and c) an algorithm (combines initial and input data to solve the recommendation) [18].

3. THE PROTOTYPE A prototype of a recommending service has been developed and the corresponding client has been implemented in OpenACS/dotLRN. The prototype offers a recommendation service to be used by external components (i.e. LMS) to ask for the appropriate recommendations for the user currently working in the LMS. This first prototype is focused on the integration with the LMS via web services communication and the management of the data in the database. To access the required information as defined in section 2.1, two parallel developments are used: An OpenACS/dotLRN package to compute the learning style of the user. This ackage has been developed at aDeNu group to be used at educational institutions for educational purposes following the Felder‘s Learning Style Inventory20. From the OpenACS control panel, the user can fill in the questionnaire and the four dimensions of Felder learning style (i.e. processing, perception, input and understanding) are computed. Moreover, from the administrator of a group (class or community), the professor can access the learning styles‘ information for the members of the group. An external component which offers web services communication to manage the learner and the device profiles. This package is described in [20]. Moreover, it will take into account the Tracking and Auditing Engines infrastructure (TAE package) that is being built in OpenACS [21].

20 Felder

LSI: http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSdir/ILS.pdf

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3.1 Client side on OpenACS/dotLRN Two packages (recommendation-portlet and dotlrn-recommendation) have been implemented in OpenACS to send requests to the recommending service. The recommendation client relies on xo-soap, a SOAP protocol plugin for xorb. Xorb stands for XOTcl Request Broker, an OO-interface that wraps up OpenACS‘s Service Contracts and provides a generic invocation dispatcher supporting local (original OpenACS Service Contracts) and remote (protocol-plugins: xotcl-soap) redirection [22]. A portlet displays the result from the request. The response consists on an introductory text (personalized with the user‘s name) and an HTML list of recommendations. Each of these recommendations (item list) is a piece of text (message) describing a possible action to be done by the user. Part of the text is defined as a hyperlink (or more commonly called, link). Since the link may most of the times be within the recommendation text, the message is usually divided into two parts (text before the link and text after the link). The link is defined by: the content: the text that is shown to the user and consists on the underlying part of the recommendation offered the title: the information saying where the link goes the pointer: the URI that opens the link, it can be a URL or an object identifier within the LMS, depending on the type the type: it can be internal to the LMS or an external URL Each time the page with the recommendation portlet is loaded, a request is sent to the recommending system. The response (i.e. the list of recommendations) is printed on this portlet. The user is free to follow them. If the user clicks on the link of any of the recommendations, the OpenACS recommendation client captures the action, calls the recommending system to tell it that the corresponding recommendation has been selected and redirects the user to the link of the recommendation. This allows the recommending sytem to follow up the user‘s choice. The following figure shows what the recommending system looks like in the OpenACS/dotLRN user interface within a community (it could also be within a course).

Figure 1: Snapshot of the Recommendations portlet in OpenACS/dotLRN

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3.2 Server side on Axis The server side implements the basic infrastructure of the A2M as introduced in section 2. Currently, it allows the integration with the LMS and provides persistence of the data exchange. The developments have been done in JDK1.5 to be run on Apache Tomcat open source servlet container. On top of it, the open source Apache Axis2 architecture has been used as web service engine. The classes to support the webservice communication have been obtained by first designing the services and message format. With this information we created the WSDL, and using a tool provided by Axis2 (wsdl2java), stubs, skeletons and data types were automatically generated. For the latter, XMLBeans has been chosen as the data binding framework because of its compliance with the requirements and its easy learning curve. The recommending service consists on two parts, the front-end and the back-end. The front-end waits for a request from the client. When received, it connects to the database, stores the request information and retrieves the recommendations available for the user for whom the request was made, prepares the list of recommendations and sends it back to the client. The back-end is the core of the A2M and is still under development. It is in charge of producing the appropriate recommendations for each user taking into account her user model, the context, past interactions and psychopedagogical guidelines. Two hierarchies of agents are being defined, following the aLFanet approach for the Adaptation Module [2]. On the one hand, a set of recommender agents are in charge of producing the recommendations. These agents are diverse, and look independently for the recommendations. Collaborative filtering techniques are applied. Each recommender agent contacts the appropriate model agents to gather information about the learners, course, contents, device and interactions. The information is stored in the database following the specifications and theories mentioned above (IMS, Felder, Bloom and CC/PP). These model agents have the knowledge to extract the relevant information for the recommender agents. They obtain this information from the developments introduced at the beginning of section 3, i.e. the Felder Learning Style Inventory and the web service support for the learner and device profiles. As mentioned above, they will also benefit from the TAE package. The process described so far takes place at runtime (on-line), during the course execution. However, there is also an off-line process that happens in the second hierarchy of agents. Here is where the attributes for the user models are updated. This off-line process can be activated by the model agents or self-activated. This update of the values of the attributes is performed by a set of modelling agents. These agents can implement diverse AI techniques, such as fuzzy logic, data mining, machine learning and bayesian networks.

4. CONCLUSIONS AND FUTURE WORKS A prototype of a recommending service has been implemented in an Axis2 framework and the corresponding client has been integrated in OpenACS/dotLRN. It offers a recommendation service to be used by external components to ask for the

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appropriate recommendations for the user currently working in the LMS. This prototype is focused on the integration with the LMS via web services communication and the management of the data in the database. The recommendation service focuses on providing user-centred services that consider individual user‘s needs and preferences, past interactions, the current context and psychopedagogical guidelines, and defines the service provision in a reusable way, integrating learning design in their definition and establishing clear procedures and measures for quality assurance. The objective of the recommendations is to provide dynamic support to the user at the course execution to overcome impasses that learners may encounter, and which are not covered by the design of the course. To provide this support, the learner‘s interactions and their evolution overtime are considered. In this approach, the design is used to build the skeleton of standard-based models which are dynamically updated according to learners‘ interactions over time with machine learning techniques. The paper has introduced relevant issues to be considered in the user modelling and the recommending process and presented the technological support provided in the first prototype, which is intended to support in a general way recommendations to be integrated in different LMS. Current works are focused on the back-end of the A2M, where an intensive use of AI techniques is being done to offer dynamic support in learning management systems. These tasks are being undertaken within the scope of a Ph.D thesis [23]. Acknowledgments. Authors would like to thank the European Commission and the Spanish Government for funding the research involved in this work. Authors would also like to thank the backing from the OpenACS/dotLRN community for these tasks.

REFERENCES 1. Cooper, M., Boticario, J.G., Montandon, L. An introduction to Accessible Lifelong Learning (ALL) - a strategy for research and development uniting accessible technology, services, and e-learning infrastructure. Proceedings of the 14th EDEN (European Distance and ELearning Network) Research Workshop: Research into online distance and e-learning: Making the Difference. Barcelona, October 25-28, 2006. 2. Boticario, J.G. and Santos, O.C. An open IMS-based user modelling approach for developing adaptive learning management systems. Journal of Interactive Media in Education. Special issue on Adaptation and IMS Learning Design, 2007 3. Baldiris, S, Santos O., Barrera C., Boticario J.G., Velez J., Fabregat, R. Integration of Educational Specifications and Standards to Support Adaptive Learning Scenarios in ADAPTAPlan. Special Issue on New Trends on AI techniques for Educational Technologies. International Journal of Computer Science and Applications (IJCSA), 2008 (in press). 4. Santos O.C. Dynamic recommendations to support ‗all‘ in open standard-based adaptive learning environments. In Proceedings for the 1st Doctoral Consortium. 13rd International Conference on Artificial Intelligence and Education, 2007. 5. Santos, O.C., Boticario, J.G., Raffenne, E., Pastor, R. Why using dotLRN? UNED use cases. FLOSS International Conference, 2007. 6. Revilla, O. Can dotLRN be administered by all professors? Proceedings of the International Conference and Workshops on Community based environments, 2008 (in press). 7. Martin, L., Roldán, D., Revilla, O., Aguilar, M.J., Santos, O.C. Boticario. J.G. Usability in eLearning Platforms: heuristics comparison between Moodle, Sakai and dotLRN.

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Proceedings of the International Conference and Workshops on Community based environments, 2008 (in press). 8. Kobsa A . Generic User Modeling Systems. User Modeling and User-Adapted Interaction, 11, pp 49—63. The Netherlands: Kluwer, 2001 9. Resnik, P., and Varian H.R. Recommender Systems. Communications of the ACM, 1997. 10. Tang, T. and McCalla, G. Smart Recommendation for an Evolving E-Learning System. Workshop on Technologies for Electronic Documents for Supporting Learning, International Conference on Artificial Intelligence in Education (AIED 2003), 2003. 11. Santos, O.C., Boticario, J.G. Supporting learners in an inclusive way with standard-based user modelling techniques. Educational Technology Newsletter. Volume 9, Issue 3-4. October 2007. 12. Santos, O.C., Baldiris, S., Velez, J., Boticario, J.G., Fabregat, R. Dynamic Support in ADAPTAPlan: ADA+. Proceedings of CAEPIA. (Eds.) Borrajo, D., Castillo, L. and Corchado, J.M. Actas de la XII Conferencia de la Asociación Española para la Inteligencia Artificial. Vol. II. 2007, p. 131-140. 13. Bull, S. and Kay, J. A framework for designing and analysing open learner modelling. 12 International Conference on Artificial Intelligence in Education. Workshop 11, Kay, J., Lum, A., Zapata-Rivera (eds.), p. 81-90, 2005. 14. Felder R. M., Silverman L. K., ‗Learning and Teaching Styles In Engineering Education‘, Engr. Education, 78(7), 674–681 (1988) – Preface: Felder R. M., June 2002. 15. Felder, R.M. and Soloman, B.A. Index of Learning Styles. http://www.ncsu.edu/felderpublic/ ILSpage.html>, accessed January 11, 2008. 16. Bloom, B.S. Taxonomy of Educational Objectives. New York: David Mckay, 1956. 17. Santos, O.C. A standard-based approach for modeling ‗all‘ users in adaptive learning management systems with artificial intelligence techniques. Proceedings of CAEPIA. (Eds.) Borrajo, D., Castillo, L. and Corchado, J.M. Actas de la XII Conferencia de la Asociación Española para la Inteligencia Artificial. Vol. II. 2007, p. 339-342. 18. Burke, R. Hybrid Recommender systems. User Modelling and User-Adapted Interaction, 12 (4):331-370, 2002. 19. Hernández, F. Contributions to the evaluation to adaptive recomendar sstems (Contribuciones a la evaluación de sistemas recomendadores adaptativos). Ph. D Thesis. Supervised by Jesús G. Boticario. Artificial Intelligence Department. UNED, 2005. 20. Cuartero, A., Santos, O.C., Granado, J., Raffenne, E. and Boticario, J.G. Mangement of standard-based User Model and Device Profile in OpenACS. Proceedings of the International Conference and Workshops on Community based environments, 2008 (in press). 21.Couchet, J., Santos, O.C., Raffenne, E. Granado, J., Boticario, J.G. and Manrique, D. A General Tracking and Auditing Architecture for the OpenACS framework. Proceedings of the International Conference and Workshops on Community based environments, 2008 (in press). 22.Sobernig, S. Xorb/xosoap. Remoting for OACS / .LRN. dotLRN Conference Boston, 2006. 23. Santos, O.C. Contributions to the Design, Implementation and Evaluation of Adaptive Learning Management Systems based on standards, which integrate Instructional Design with User Modelling based on Machine Learning. Ph.D Thesis. Supervised by Jesús G. Boticario. Artificial Intelligence Department. UNED. To be presented in the last quarter of 2008.

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Sesión de Pósters Universidad Galileo

MEMORIAS 2ª. Conferencia Internacional E-Learning Integral 2.0 Y 6ª. Conferencia Internacional de OpenACS y .LRN

Guatemala, 12 al 15 de febrero de 2008

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Galileo´s Infrastructure Ing. Rocael Hernández, Ing. Victor Guerra, Ing. Byron Linares, César Hernández, Research and Development Department, GES, Universidad Galileo, Guatemala [roc, guerra, bhlr, cesarhj]@galileo.edu

Este poster detalla la infraestructura con la que cuenta el LMS (Learning Management System) que utiliza la Universidad Galileo. En él se mencionan los subsistemas que conforman esta estructura tecnologica. El poster describe, las tecnologías que le dan vida al sistema, la forma en que fluyen los datos dentro del mismo y presenta de una forma grafica la interaccion que existe entre cada uno de los subsistemas. De igual manera se especifican configuraciones de algunos servidores utilizados en los subsistemas. La finalidad del poster es proveer una idea general de la configuración e interacción que tienen los subsistemas dentro de la Universidad Galileo; el exponer las experiencias que la Universidad Galileo ha tenido puede ser de gran ayuda a otras entidades para mejorar la calidad de los servicios que prestan. Workshop: OpenACS Contribuciones y fácil utilización de la herramienta. Uno de los puntos claves en el crecimiento de proyectos como OpenACS, es la fácil incorporación de actualizaciones de la herramienta hacia las instalaciones que tienen los usuarios que utilizan la plataforma. Igual de importante es para el proyecto, poder contar con las contribuciones de los usuarios de vuelta al repositorio de código; muchas veces los miembros de la comunidad no contribuyen debido a que el proceso de tomar cambios locales e incorporarlos al repositorio de OpenACS es demasiado complicado. Por lo general los usuarios que utilizan OpenACS cuentan con un repositorio local de código, normalmente implementado sobre CVS, de hecho, la herramienta CVS es utilizada para mantener el repositorio de código de OpenACS. Actualmente ha habido esfuerzos para utilizar la herramienta SVN, la cual facilita el manejo del código (la mejora mas importante es en el Merge). Pero este cambio no ataca el problema de mantenerse actualizado ante los cambios que sufre el código de OpenACS. La idea de este Workshop es presentar algunas ideas de cómo se podría mejorar el proceso de contribución de las mejoras que los usuarios de OpenACS puedan tener. La herramienta que se pretende utilizar en este Workshop es GIT; la cual es una herramienta para el control de versiones, utilizada en grandes proyectos, con la característica de adaptarse a modelos de desarrollo distribuidos. Workshop: Contribuciones de Galileo El equipo de desarrollo de la Universidad Galileo se encuentra en constante desarrollo de aplicaciones basadas en las tecnologías que utiliza OpenACS/.LRN. Hemos tomado varias aplicaciones existentes en el repositorio de código de OpenACS y

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hemos customizado algunas interfaces para suplir algunos requermientos que han surgido dentro de la Universidad. Dentro de estos desarrollos podemos citar: -

Aplicación de evaluaciones a catedráticos: Basado en el paquete de assessment, se construyo un paquete de manejo de cuestionarios, con la finalidad de proveer al personal administrativo de la Universidad una herramienta para poder administrar las evaluaciones que miden el rendimiento de los catedráticos y auxiliares. Este paquete cuenta con la versatilidad de manejar encuestas para poder recoger opiniones de los estudiantes.

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Copia de elementos entre cursos: Dentro de la Universidad Galileo los catedráticos regularmente hacían la requisicion de poder hacer uso del material que habían utilizado en cursos pasados. En base a estas requisiciones se realizo una aplicación que permite al catedrático copiar elementos de los paquetes ―file-storage‖ y ―evaluation‖; con esto el catedrático puede obtener todo el material que utilizo asi como la ponderación de sus calificaciones.

Durante el workshop, algunas otras contribuciones de la Universidad Galileo serán presentadas. Workshop: JavaScript en OACS La experiencia que los usuarios tienen dentro de un sitio web marca la pauta para que los usuarios regresen y sigan utilizando los servicios que el sitio provee, sin importar que tipo de servicios sean. Hoy en día, tecnologías como AJAX ayudan a mejorar la experiencia que el usuario tiene ante una interfaz web, es por eso que es de suma importancia para el proyecto OpenACS el poder adaptar este tipo de tecnologías dentro de la plataforma. Actualmente existe el paquete AjaxHelper, el cual incluye un conjunto de librerías de ajax ( las más utilizadas ); el paquete también incluye algunos procedimientos en TCL para una mejor integración con la plataforma. La Universidad Galileo ha extendido los procedimientos para poder utilizar algunas otras funcionalidades que proveen las librerías de AJAX: Como por ejemplo: - Interfaces divididas por TABS – YUI. -

Integración de modulo de historial del explorador.

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Submit de formularios en el background (integración con TABS ).

El propósito es de presentar las formas en que la Universidad Galileo ha mejorado sus interfaces, haciendo uso de estas tecnologías.

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Diseño y Evolución del Clúster de E-Learning (.LRN) en la Universitat de València Aula Virtual: Una aplicación en contínuo crecimiento

Salvador Roca Marquina Agustín López Bueno Darío Roig García Servei d‟ Informàtica de la Universitat de València C/ Dr. Moliner, 50 - Campus de Burjassot 46100 - Burjassot (VALENCIA) Spain http://www.uv.es/siuv http://aulavirtual.uv.es [email protected] Palabras clave: Software de apoyo a la docencia universitaria, evaluación e implantación de sistemas de gestión del aprendizaje, virtualización del aprendizaje, software libre, cluster, alta disponibilidad. Línea prioritaria: Sistemas de Aula Virtual y teledocencia.

Resumen En el curso académico 2004-2005 se implantó en la Universitat de València (UV) la primera versión de la actual plataforma de eLearning (OpenACS / .LRN) tras un piloto de 6 meses con un grupo de profesores interesados y tras un estudio de las plataformas existentes. Esta implantación, su mantenimiento y los desarrollos posteriores han sido realizados por técnicos del Servicio de Informática. La arquitectura inicial del sistema en tres capas (presentación, lógica de negocio y datos) se implementó sobre dos servidores con procesadores duales AMD Opteron y sistema operativo Debian GNU/Linux, de los cuales, uno tenía instalado el servidor web AOLServer junto con la aplicación .LRN, y el otro el servidor de base de datos PostgreSQL (7.4). La versión de la aplicación instalada correspondía a OpenACS 5.1 y .LRN 2.0. Este diseño inicial carecía de tolerancia a fallos y estaba limitado en cuanto a escalabilidad. El éxito de la implantación de la herramienta entre nuestros usuarios llevó a un incremento en la carga de los sistemas. Se pasó de 40 sesiones de media y máximos de 80 al centenar de sesiones concurrentes. En 2006-2007 se modificó el diseño incorporando un clúster de cuatro nodos en las capas web y de aplicación, y se separó el servicio de elementos estáticos incorporando servidores Apache. Aprovechamos para ello que la aplicación OpenACS está preparada para funcionamiento en clúster e instalamos un balanceador por software (Pound) para los servidores Web con un conjunto de reglas de reescritura adecuadas para gestión de las Urls. El código de la aplicación se compartió mediante NFS entre los nodos del clúster para facilitar el mantenimiento. Con esta

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modificación de la arquitectura, la carga total pudo aumentar hasta las 400 sesiones concurrentes (con picos de 1.000) con tiempos de respuesta similares a las del año anterior. El curso 2007-2008 se ha iniciado con otra serie de mejoras. Se ha aumentado el número de elementos del clúster hasta seis. Se han virtualizado los servidores web y de aplicaciones (bajo un entorno Xen) para favorecer y simplificar su mantenimiento y la gestión de nuevos elementos. Se ha simplificado el diseño sustituyendo el par Apache + Pound por el servidor Nginx. Para eliminar el punto de fallo del balanceador se ha incorporado una solución basada en ―heart-beat‖ de manera que otro elemento del clúster toma el rol de balanceador en caso de fallo del principal. Se ha adquirido e integrado en la arquitectura general del sistema un servidor multimedia (streaming). Actualmente, nuestra mayor preocupación reside en la tolerancia a fallos de la base de datos. Bien es cierto que disponemos de la información replicada en armarios de discos (SAN) y copias de seguridad, pero este escenario no garantiza la alta disponibilidad del servidor de base de datos. Esperemos que las soluciones que ofrece el software libre nos permitan mejorar en este aspecto.

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Modelo Educativo de e-Learning implementado en Universidad Galileo Proyectos de e-Learning desarrollados en Universidad Galileo 2006-2007 Universidad Galileo Ing. Miguel Morales, Inga, Mónica De La Roca, Licda. Sonia García (amorales, monica_dlr, sonia_ges) @galileo.edu

Modelo Educativo de e-Learning El modelo educativo de e-Learning implementado en Universidad Galileo es una representación del proceso de enseñanza-aprendizaje que se emplea en los cursos e-Learning, en éste se exhibe la distribución de funciones y la secuencia de operaciones en la forma ideal que resulta de las experiencias recogidas al ejecutar una o varias teorías del aprendizaje. El conocimiento de este modelo educativo permite tener un panorama de cómo se elaboran los cursos, de cómo operan y cuáles son los elementos que desempeñan un papel determinante en la implementación.

Proyectos de e-Learning desarrollados en Universidad Galileo 2006-2007 Presentación de los diferentes proyectos que se han implementado en Universidad Galileo, así como, proyectos en alianzas estratégicas con otras organizaciones importantes del país como lo son Prensa Libre, periódico de mayor circulación en Guatemala y La Super Intendencia de Administración Tributaria, SAT.

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Arquitectura de .LRN Vivian Aguilar Viaro Networks [email protected] Se presenta la arquitectura e infraestructura de .LRN, organizada en 4 capas

1. SISTEMA Comprende una descripción de sistemas operativos, bases de datos, motores de búsqueda, servidores web, lenguaje de programación y otros.

2. SERVICIOS DE LA PLATAFORMA Comprende 3 grandes áreas que son Desarrollo de Software, Orientación a Objetos, Seguridad.

3. SERVICIOS DE APLICACIÓN Los cuales son herramientas a ser utilizadas por aplicaciones para usuarios.

4. MÓDULOS DE APLICACIÓN Comprende Estándares, Contenidos, Colaboración Administración del Curso y otros.

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