From collaborative virtual research environment SOA to teaching and learning environment SOA

This paper explores the extension of the CORE VRE SOA to a collaborative virtual teaching and learning environment (CVTLE) SOA. Key points are brought up to date from a number of projects researching and developing a CVTLE and its component services. Issues remain: there are few implementations of the key services needed to demonstrate the CVTLE concept; there are questions about the feasibility of such an enterprise; there are overlapping standards; questions about the source and use of user profile data remain difficult to answer; as does the issue of where and how to coordinate, control, and monitor such a teaching and learning syste

A note on organizational learning and knowledge sharing in the context of communities of practice

Please, cite this publication as: Antonova, A. & Gourova, E. (2006). A note on organizational learning and knowledge sharing in the context of communities of practice. Proceedings of International Workshop in Learning Networks for Lifelong Competence Development, TENCompetence Conference. September 12th, Sofia, Bulgaria: TENCompetence. Retrieved June 30th, 2006, from http://dspace.learningnetworks.orgThe knowledge management (KM) literature emphasizes the impact of human factors for successful implementation of KM within the organization. Isolated initiatives for promoting learning organization and team collaboration, without taking consideration of the knowledge sharing limitations and constraints can defeat further development of KM culture. As an effective instrument for knowledge sharing, communities of practice (CoP) are appearing to overcome these constraints and to foster human collaboration.This work has been sponsored by the EU project TENCompetenc

Adaptive intelligent personalised learning (AIPL) environment

As individuals the ideal learning scenario would be a learning environment tailored just for how we like to learn, personalised to our requirements. This has previously been almost inconceivable given the complexities of learning, the constraints within the environments in which we teach, and the need for global repositories of knowledge to facilitate this process. Whilst it is still not necessarily achievable in its full sense this research project represents a path towards this ideal.In this thesis, findings from research into the development of a model (the Adaptive Intelligent Personalised Learning (AIPL)), the creation of a prototype implementation of a system designed around this model (the AIPL environment) and the construction of a suite of intelligent algorithms (Personalised Adaptive Filtering System (PAFS)) for personalised learning are presented and evaluated. A mixed methods approach is used in the evaluation of the AIPL environment. The AIPL model is built on the premise of an ideal system being one which does not just consider the individual but also considers groupings of likeminded individuals and their power to influence learner choice. The results show that: (1) There is a positive correlation for using group-learning-paradigms. (2) Using personalisation as a learning aid can help to facilitate individual learning and encourage learning on-line. (3) Using learning styles as a way of identifying and categorising the individuals can improve their on-line learning experience. (4) Using Adaptive Information Retrieval techniques linked to group-learning-paradigms can reduce and improve the problem of mis-matching. A number of approaches for further work to extend and expand upon the work presented are highlighted at the end of the Thesis

Computer-Driven Instructional Design with INTUITEL

INTUITEL is a research project that was co-financed by the European Commission with the aim to advance state-of-the-art e-learning systems via addition of guidance and feedback for learners. Through a combination of pedagogical knowledge, measured learning progress and a broad range of environmental and background data, INTUITEL systems will provide guidance towards an optimal learning pathway. This allows INTUITEL-enabled learning management systems to offer learners automated, personalised learning support so far only provided by human tutors INTUITEL is - in the first place - a design pattern for the creation of adaptive e-learning systems. It focuses on the reusability of existing learning material and especially the annotation with semantic meta data. INTUITEL introduces a novel approach that describes learning material as well as didactic and pedagogical meta knowledge by the use of ontologies. Learning recommendations are inferred from these ontologies during runtime. This way INTUITEL solves a common problem in the field of adaptive systems: it is not restricted to a certain field. Any content from any domain can be annotated. The INTUITEL research team also developed a prototype system. Both the theoretical foundations and how to implement your own INTUITEL system are discussed in this book

Extensión de la especificación IMS Learning Design desde la adaptación e integración de unidades de aprendizaje

IMS Learning Design (IMS-LD) representa una corriente actual en aprendizaje online y blended que se caracteriza porque: a) Es una especificación que pretende estandarizar procesos de aprendizaje, así como reutilizarlos en diversos contextos b) Posee una expresividad pedagógica más elaborada que desarrollos anteriores o en proceso c) Mantiene una relación cordial y prometedora con Learning Management Systems (LMSs), herramientas de autoría y de ejecución d) Existe una amplia variedad de grupos de investigación y proyectos europeos trabajando sobre ella, lo que augura una sostenibilidad, al menos académica Aun así, IMS Learning Design es un producto inicial (se encuentra en su primera versión, de 2003) y mejorable en diversos aspectos, como son la expresividad pedagógica y la interoperabilidad. En concreto, en esta tesis nos centramos en el aprendizaje adaptativo o personalizado y en la integración de Unidades de Aprendizaje, como dos de los pilares que definen la especificación, y que al mismo tiempo la potencian considerablemente. El primero (aprendizaje adaptativo) hace que se puedan abordar itinerarios individuales personalizados de estudio, tanto en flujo de aprendizaje como en contenido o interfaz; el segundo (integración) permite romper el aislamiento de los paquetes de información o cursos (Unidades de Aprendizaje, UoL) y establecer un diálogo con otros sistemas (LMSs), modelos y estándares, así como una reutilización de dichas UoLs en diversos contextos. En esta tesis realizamos un estudio de la especificación desde la base, analizando su modelo de información y cómo se construyen Unidades de Aprendizaje. Desde el Nivel A al Nivel C analizamos y criticamos la estructura de la especificación basándonos en un estudio teórico y una investigación práctica fruto del modelado de Unidades de Aprendizaje reales y ejecutables que nos proporcionan una información muy útil de base, y que mayormente adjuntamos en los anexos, para no interferir en el flujo de lectura del cuerpo principal. A partir de este estudio, analizamos la integración de Unidades de Aprendizaje con otros sistemas y especificaciones, abarcando desde la integración mínima mediante un enlace directo hasta la compartición de variables y estados que permiten una comunicación en tiempo real de ambas partes. Exponemos aquí también las conclusiones de diversos casos de estudio basados en adaptación que se anexan al final de la tesis y que se vuelven un instrumento imprescindible para lograr una solución real y aplicable. Como segundo pilar de la tesis complementario a la integración de Unidades de Aprendizaje, estudiamos el aprendizaje adaptativo: Los tipos, los avances y los enfoques y restricciones de modelado dentro de IMS-LD. Por último, y como complemento de la investigación teórica, a través de diversos casos prácticos estudiamos la manera en que IMS-LD modela la perzonalización del aprendizaje y hasta qué punto. Este primer bloque de análisis (general, integración y aprendizaje adaptativo) nos permite realizar una crítica estructural de IMS-LD en dos grandes apartados: Modelado y Arquitectura. Modelado apunta cuestiones que necesitan mejora, modificación, extensión o incorporación de elementos de modelado dentro de IMS-LD, como son procesos, componentes y recursos de programación. Arquitectura engloba otras cuestiones centradas en la comunicación que realiza IMS-LD con el exterior y que apuntan directamente a capas estructurales de la especificación, más allá del modelado. Aunque se encuentra fuera del núcleo de esta tesis, también se ha realizado una revisión de aspectos relacionados con Herramientas de autoría, por ser este un aspecto que condiciona el alcance del modelado y la penetración de la especificación en los distintos públicos objetivo. Sobre Herramientas, no obstante, no realizamos ninguna propuesta de mejora. La solución desarrollada, se centra en las diversas cuestiones sobre Modelado y Arquitectura encontradas en el análisis. Esta solución se compone de un conjunto de propuestas de estructuras, nuevas o ya existentes y modificadas, a través de las que se refuerza la capacidad expresiva de la especificación y la capacidad de interacción con un entorno de trabajo ajeno. Esta investigación de tres años ha sido llevada a cabo entre 2004 y 2007, principalmente con colegas de The Open University of The Netherlands, The University of Bolton, Universitat Pompeu Fabra y del departamento Research & Innovation de ATOS Origin, y ha sido desarrollada parcialmente dentro de proyectos europeos como UNFOLD, EU4ALL y ProLearn. La conclusión principal que se extrae de esta investigación es que IMS-LD necesita una reestructuración y modificación de ciertos elementos, así como la incorporación de otros nuevos, para mejorar una expresividad pedagógica y una capacidad de integración con otros sistemas de aprendizaje y estándares eLearning, si se pretenden alcanzar dos de los objetivos principales establecidos de base en la definición de esta especificación: La personalización del proceso de aprendizaje y la interoperabilidad real. Aun así, es cierto que la implantación de la especificación se vería claramente mejorada si existieran unas herramientas de más alto nivel (preferiblemente con planteamiento visual) que permitieran un modelado sencillo por parte de los usuarios finales reales de este tipo de especificaciones, como son los profesores, los creadores de contenido y los pedagogos-didactas que diseñan la experienicia de aprendizaje. Este punto, no obstante, es ajeno a la especificación y afecta a la interpretación que de la misma realizan los grupos de investigación y compañías que desarrollan soluciones de autoría. _____________________________________________IMS Learning Design (IMS-LD) is a current asset in eLearning and blended learning, due to several reasons: a) It is a specification that points to standardization and modeling of learning processes, and not just content; at the same time, it is focused on the re-use of the information packages in several contexts; b) It shows a deeper pedagogical expressiveness than other specifications, already delivered or in due process c) It is integrated at different levels into well-known Learning Management Systems (LMSs) d) There are a huge amount of European research projects and groups working with it, which aims at sustainability (in academia, at least) Nevertheless, IMS-LD is roughly an initial outcome (be aware that we are still working with the same release, dated on 2003). Therefore, it can and must be improved in several aspects, i.e., pedagogical expressiveness and interoperability. In this thesis, we concentrate on Adaptive Learning (or Personalised Learning) and on the Integration of Units of Learning (UoLs). They both are core aspects which the specification is built upon. They also can improve it significantly. Adaptation makes personalised learning itineraries, adapted to every role, to every user involved in the process, and focus on several aspects, i.e., flow, content and interface. Integration fosters the re-use of IMS-LD information packages in different contexts and connects both-ways UoLs with other specifications, models and LMSs. In order to achive these goals we carry out a threephase analysis. First, analysis of IMS-LD in several steps: foundations, information model, construction of UoLs. From Level A to Level C, we analyse and review the specification structure. We lean on a theoretical frameword, along with a practical approach, coming from the actual modeling of real UoLs which give an important report back. Out of this analysis we get a report on the general structure of IMS-LD. Second, analysis and review of the integration of UoLs with several LMSs, models and specifications: we analyse three different types of integration: a) minimal integration, with a simple link between parts; b) embedded integration, with a marriage of both parts in a single information package; and d) full integration, sharing variables and states between parts. In this step, we also show different case studies and report our partial conclusions. And third, analysis and review of how IMS-LD models adaptive learning: we define, classify and explain several types of adaptation and we approach them with the specificacion. A key part of this step is the actual modeling of UoLs showing adaptive learning processes. We highlight pros and cons and stress drawbacks and weak points that could be improved in IMS-LD to support adaptation, but also general learning processes Out of this three-step analysis carried out so far (namely general, integration, adaptation) we focus our review of the IMS-LD structure and information model on two blocks: Modeling and Architecture. Modeling is focused on process, components and programming resources of IMS-LD. Architecture is focused on the communication that IMS-LD establishes outside, both ways, and it deals with upper layers of the specification, beyong modeling issues. Modeling and Architecture issues need to be addressed in order to improve the pedagogical expressiveness and the integration of IMS-LD. Furthermore, we provide an orchestrated solution which meets these goals. We develop a structured and organized group of modifications and extensions of IMS-LD, which match the different reported problems issues. We suggest modifications, extensions and addition of different elements, aiming at the strength of the specification on adaptation and integration, along with general interest issues. The main conclusion out of this research is that IMS-LD needs a re-structure and a modification of some elements. It also needs to incorporate new ones. Both actions (modification and extension) are the key to improve the pedagogical expressiveness and the integration with other specifications and eLearning systems. Both actions aim at two clear objectives in the definition of IMS-LD: the personalisation of learning processes, and a real interoperability. It is fair to highlight the welcome help of high-level visual authoring tools. They can support a smoother modeling process that could focus on pedagogical issues and not on technical ones, so that a broad target group made of teachers, learning designers, content creators and pedagogues could make use of the specification in a simpler way. However, this criticism is outside the specification, so outside the core of this thesis too. This three-year research (2004-2007) has been carried out along with colleagues from The Open University of The Netherlands, The University of Bolton, Universitat Pompeu Fabra and from the Department of Research & Innovation of ATOS Origin. In addition, a few European projects, like UNFOLD, EU4ALL and ProLearn, have partially supported it

A service-orientated architecture for adaptive and collaborative e-learning systems

This research proposes a new architecture for Adaptive Educational Hypermedia Systems (AEHS). Architectures in the context of this thesis refer to the components of the system and their communications and interactions. The architecture addresses the limitations of AEHS regarding interoperability, reusability, openness, flexibility, and limited tools for collaborative and social learning. It presents an integrated adaptive and collaborative Web-based learning environment. The new e-learning environment is implemented as a set of independent Web services within a service-oriented architecture (SOA). Moreover, it uses a modern Learning Management System (LMS) as the delivery service and the user interface for this environment. This is a two-way solution, whereby adaptive learning is introduced via a widely adopted LMS, and the LMS itself is enriched with an external - yet integrated - adaptation layer. To test the relevance of the new architecture, practical experiments were undertaken. The interoperability, reusability and openness test revealed that the user could easily switch between various LMS to access the personalised lessons. In addition, the system was tested by students at the University of Nottingham as a revision guide to a Software Engineering module. This test showed that the system was robust; it automatically handled a large number of students and produced the desired adaptive content. However, regarding the use of the collaborative learning tools, the test showed low levels of such usage

Design and Implementation Strategies for IMS Learning Design

SIKS Dissertation Series No. 2008-27The IMS Learning Design (LD) specification, which has been released in February 2003, is a generic and flexible language for describing the learning practice and underlying learning designs using a formal notation which is computer-interpretable. It is based on a pedagogical meta-model (Koper & Manderveld, 2004) and supports the use of a wide range of pedagogies. It supports adaptation of individual learning routes and orchestrates interactions between users in various learning and support roles. A formalized learning design can be applied repeatedly in similar situations with different persons and contexts. Yet because IMS Learning Design is a fairly complex and elaborate specification, it can be difficult to grasp; furthermore, designing and implementing a runtime environment for the specification is far from straightforward. That IMS Learning Design makes use of other specifications and e-learning services adds further to this complexity for both its users and the software developers. For this new specification to succeed, therefore, a reference runtime implementation was needed. To this end, this thesis addresses two research and development issues. First, it investigates research into and development of a reusable reference runtime environment for IMS Learning Design. The resulting runtime, called CopperCore, provides a reference both for users of the specification and for software developers. The latter can reuse the design principles presented in this thesis for their own implementations, or reuse the CopperCore product through the interfaces provided. Second, this thesis addresses the integration of other specifications and e-learning services during runtime. It presents an architecture and implementation (CopperCore Service Integration) which provides an extensible lightweight solution to the problem. Both developments have been tested through real-world use in projects carried out by the IMS Learning Design community. The results have generally been positive, and have led us to conclude that we successfully addressed both the research and development issues. However, the results also indicate that the LD tooling lacks maturity, particularly in the authoring area. Through close integration of CopperCore with a product called the Personal Competence Manager, we demonstrate that a complementary approach to authoring in IMS Learning Design solves some of these issues

Augmented Reality and Context Awareness for Mobile Learning Systems

Learning is one of the most interactive processes that humans practice. The level of interaction between the instructor and his or her audience has the greatest effect on the output of the learning process. Recent years have witnessed the introduction of e-learning (electronic learning), which was then followed by m-learning (mobile learning). While researchers have studied e-learning and m-learning to devise a framework that can be followed to provide the best possible output of the learning process, m-learning is still being studied in the shadow of e-learning. Such an approach might be valid to a limited extent, since both aims to provide educational material over electronic channels. However, m-learning has more space for user interaction because of the nature of the devices and their capabilities. The objective of this work is to devise a framework that utilises augmented reality and context awareness in m-learning systems to increase their level of interaction and, hence, their usability. The proposed framework was implemented and deployed over an iPhone device. The implementation focused on a specific course. Its material represented the use of augmented reality and the flow of the material utilised context awareness. Furthermore, a software prototype application for smart phones, to assess usability issues of m-learning applications, was designed and implemented. This prototype application was developed using the Java language and the Android software development kit, so that the recommended guidelines of the proposed framework were maintained. A questionnaire survey was conducted at the University, with approximately twenty-four undergraduate computer science students. Twenty-four identical smart phones were used to evaluate the developed prototype, in terms of ease of use, ease of navigating the application content, user satisfaction, attractiveness and learnability. Several validation tests were conducted on the proposed augmented reality m-learning verses m-learning. Generally, the respondents rated m-learning with augmented reality as superior to m-learning alone

Computer-Driven Instructional Design with INTUITEL

INTUITEL is a research project that was co-financed by the European Commission with the aim to advance state-of-the-art e-learning systems via addition of guidance and feedback for learners. Through a combination of pedagogical knowledge, measured learning progress and a broad range of environmental and background data, INTUITEL systems will provide guidance towards an optimal learning pathway. This allows INTUITEL-enabled learning management systems to offer learners automated, personalised learning support so far only provided by human tutors INTUITEL is - in the first place - a design pattern for the creation of adaptive e-learning systems. It focuses on the reusability of existing learning material and especially the annotation with semantic meta data. INTUITEL introduces a novel approach that describes learning material as well as didactic and pedagogical meta knowledge by the use of ontologies. Learning recommendations are inferred from these ontologies during runtime. This way INTUITEL solves a common problem in the field of adaptive systems: it is not restricted to a certain field. Any content from any domain can be annotated. The INTUITEL research team also developed a prototype system. Both the theoretical foundations and how to implement your own INTUITEL system are discussed in this book

Design of a scrutable learning system

Personal Learning Environments (PLEs) refer to systems that allow individual learners to manage and control their own learning in their own space and at their own pace. In this work we explore the different ways in which a learning experience can be informal, and propose a 4D model of informal learning to characterise the informal aspects of a learning experience.The model includes dimensions for learning objectives, the learning environment, learning activities and learning tools, and reveals how much of the experience is really under the control of the learner. In an analysis of mobile tools presented in the mLearn 2008 conference we show that many emerging m-learning systems focused on informality in the environment dimension but not in the others.To solve this problem this report proposes a scrutable learning model approach that allows personal learners to take control of their learning objectives while still allowing the system to intelligently support them with appropriate learning activities and resources. In addition an experimental design is described based around a prototype of a scrutable learning system for mobile devices