Collaboration scripts - a conceptual analysis

This article presents a conceptual analysis of collaboration scripts used in face-to-face and computer-mediated collaborative learning. Collaboration scripts are scaffolds that aim to improve collaboration through structuring the interactive processes between two or more learning partners. Collaboration scripts consist of at least five components: (a) learning objectives, (b) type of activities, (c) sequencing, (d) role distribution, and (e) type of representation. These components serve as a basis for comparing prototypical collaboration script approaches for face-to-face vs. computer-mediated learning. As our analysis reveals, collaboration scripts for face-to-face learning often focus on supporting collaborators in engaging in activities that are specifically related to individual knowledge acquisition. Scripts for computer-mediated collaboration are typically concerned with facilitating communicative-coordinative processes that occur among group members. The two lines of research can be consolidated to facilitate the design of collaboration scripts, which both support participation and coordination, as well as induce learning activities closely related to individual knowledge acquisition and metacognition. In addition, research on collaboration scripts needs to consider the learners’ internal collaboration scripts as a further determinant of collaboration behavior. The article closes with the presentation of a conceptual framework incorporating both external and internal collaboration scripts

Designing electronic collaborative learning environments

Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Content analysis: What are they talking about?

Quantitative content analysis is increasingly used to surpass surface level analyses in Computer-Supported Collaborative Learning (e.g., counting messages), but critical reflection on accepted practice has generally not been reported. A review of CSCL conference proceedings revealed a general vagueness in definitions of units of analysis. In general, arguments for choosing a unit were lacking and decisions made while developing the content analysis procedures were not made explicit. In this article, it will be illustrated that the currently accepted practices concerning the ‘unit of meaning’ are not generally applicable to quantitative content analysis of electronic communication. Such analysis is affected by ‘unit boundary overlap’ and contextual constraints having to do with the technology used. The analysis of e-mail communication required a different unit of analysis and segmentation procedure. This procedure proved to be reliable, and the subsequent coding of these units for quantitative analysis yielded satisfactory reliabilities. These findings have implications and recommendations for current content analysis practice in CSCL research

A Development Evaluation Study of a Professional Development Initiative to Strengthen Organizational Conditions in Early Education Settings

High quality instruction is essential to producing developmental gains for young children and can mitigate risk factors such as family poverty and low parental education. Even in programs with highly qualified teachers, teacher-child interactions often do not provide the level of instructional support that children need to be well-prepared for success in kindergarten. In order to improve instructional quality, an emerging focus on early childhood professional development involves supporting leaders in creating a web of supports for teacher learning and child growth. The purpose of the 3-year evaluation study was to assess the effectiveness of an Early Childhood Education Professional Development Initiative (ECE PDI) in advancing the knowledge, skills, and dispositions of community-based early childhood leaders and teachers in relation to creating the conditions for superior developmental outcomes for low-income students served by these community-based centers. Findings from the implementation and impact studies are reported

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

Learn-ciam: a model-driven approach for the development of collaborative learning tools

This paper introduces Learn-CIAM, a new model-based methodological approach for the design of flows and for the semi-automatic generation of tools in order to support collaborative learning tasks. The main objective of this work is to help professors by establishing a series of steps for the specification of their learning courses and the obtaining of collaborative tools to support certain learning activities (in particular, for in-group editing, searching and modeling). This paper presents a complete methodological framework, how it is supported conceptually and technologically, and an application example. So to guarantee the validity of the proposal, we also present some validation processes with potential designers and users from different profiles such as Education and Computer Science. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of learning courses and the generation of collaborative learning tools for professionals of both profiles

Skills and Knowledge for Data-Intensive Environmental Research.

The scale and magnitude of complex and pressing environmental issues lend urgency to the need for integrative and reproducible analysis and synthesis, facilitated by data-intensive research approaches. However, the recent pace of technological change has been such that appropriate skills to accomplish data-intensive research are lacking among environmental scientists, who more than ever need greater access to training and mentorship in computational skills. Here, we provide a roadmap for raising data competencies of current and next-generation environmental researchers by describing the concepts and skills needed for effectively engaging with the heterogeneous, distributed, and rapidly growing volumes of available data. We articulate five key skills: (1) data management and processing, (2) analysis, (3) software skills for science, (4) visualization, and (5) communication methods for collaboration and dissemination. We provide an overview of the current suite of training initiatives available to environmental scientists and models for closing the skill-transfer gap