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

Toward a script theory of guidance in computer-supported collaborative learning

This article presents an outline of a script theory of guidance for computer-supported collaborative learning (CSCL). With its four types of components of internal and external scripts (play, scene, role, and scriptlet) and seven principles, this theory addresses the question how CSCL practices are shaped by dynamically re-configured internal collaboration scripts of the participating learners. Furthermore, it explains how internal collaboration scripts develop through participation in CSCL practices. It emphasizes the importance of active application of subject matter knowledge in CSCL practices, and it prioritizes transactive over non-transactive forms of knowledge application in order to facilitate learning. Further, the theory explains how external collaboration scripts modify CSCL practices and how they influence the development of internal collaboration scripts. The principles specify an optimal scaffolding level for external collaboration scripts and allow for the formulation of hypotheses about the fading of external collaboration scripts. Finally, the article points towards conceptual challenges and future research questions

The mechanics of CSCL macro scripts

Macro scripts structure collaborative learning and foster the emergence of knowledge-productive interactions such as argumentation, explanations and mutual regulation. We propose a pedagogical model for the designing of scripts and illustrate this model using three scripts. In brief, a script disturbs the natural convergence of a team and in doing so increases the intensity of interaction required between team members for the completion of their collaborative task. The nature of the perturbation determines the types of interactions that are necessary for overcoming it: for instance, if a script provides students with conflicting evidence, more argumentation is required before students can reach an agreement. Tools for authoring scripts manipulate abstract representations of the script components and the mechanisms that relate components to one another. These mechanisms are encompassed in the transformation of data structures (social structure, resources structure and products structure) between script phases. We describe how this pedagogical design model is translated into computational structures in three illustrated script

Facilitating collaborative knowledge construction in computer-mediated learning with structuring tools

Collaborative knowledge construction in computer-mediated learning environments puts forward difficulties regarding what tasks learners work on and how learners interact with each other. For instance, learners who collaboratively construct knowledge in computer-mediated learning environments sometimes do not participate actively or engage in off-task talk. Computer-mediated learning environments can be endorsed with socio-cognitive structuring tools that structure the contents to be learned and suggest specific interactions for collaborative learners. In this article, two studies will be reported that applied content- and interaction-oriented structuring tools in computer-mediated learning environments based on electronic bulletin boards and videoconferencing technologies. In each study the factors "content-oriented structuring tool" and "interaction-oriented structuring tool" have been independently varied in a 2X2-factorial design. Results show that interaction-oriented structuring tools substantially foster the processes of collaborative knowledge construction as well as learning outcomes. The content-oriented structuring tools facilitate the processes of collaborative knowledge construction, but have no or negative effects on learning outcome. The findings will be discussed against the background of recent literatGemeinsame Wissenskonstruktion in computervermittelten Lernumgebungen birgt Schwierigkeiten in Bezug darauf, welche Aufgaben Lernende bearbeiten und wie sie dabei miteinander interagieren. Lernende, die gemeinsam Wissen in computervermittelten Lernumgebungen konstruieren, nehmen z. B. manchmal nicht aktiv an der Bearbeitung von Lernaufgaben teil oder beschäftigen sich mit inhaltsfremden Themen. Computervermittelte Lernumgebungen können mit Hilfe sozio-kognitiver Strukturierungswerkzeuge unterstützt werden, die die Lerninhalte vorstrukturieren und den Lernenden spezifische Interaktionen nahe legen. In diesem Beitrag werden zwei Studien berichtet, die inhalts- und interaktionsbezogene Strukturierungswerkzeuge in computervermittelten Lernumgebungen, die auf web-basierten Diskussionsforen und Videokonferenz-Technologien beruhen, zum Einsatz gebracht und analysiert haben. In jeder der Studien wurden die Faktoren "inhaltsbezogenes Strukturierungswerkzeug" und "interaktionsbezogenes Strukturierungswerkzeug" unabhängig voneinander in einem 2X2-Design variiert. Die Ergebnisse zeigen, dass interaktionsbezogene Strukturierungswerkzeuge die Prozesse sowie die Ergebnisse gemeinsamer Wissenskonstruktion substanziell fördern können. Die inhaltsbezogenen Strukturierungswerkzeuge unterstützen die Prozesse gemeinsamer Wissenskonstruktion, zeitigen aber keine oder negative Effekte auf die Lernergebnisse. Die Befunde werden vor dem Hintergrund aktueller theoretischer Ansätze diskut

Development of online learning groups based on MBTI learning style and fuzzy algorithm

Group development is an initial step and an important influence on learning collaborative problem solving (CPS) based on the digital learning environment (DLE). Group development based on the Myers-Briggs types indicators (MBTI) rule proved successful for the educational and industrial environment. The MBTI ideal group rules are reached when a group leader has the highest level of leadership and compatibility between group members. The level of leadership and suitability of group members is determined based on the MBTI learning style (LS). Problems arise when the population of MBTI LS with the highest level of leadership is over. This will lead to dual leadership problems and have an impact on group disharmony. This study proposes an intelligent agent software for the development of the ideal group of MBTI, using the Fuzzy algorithm. The intelligent agent was developed on the SKACI platform. SKACI is a DLE for CPS learning. Fuzzy algorithm for solving dual leadership problems in a group. Fuzzy algorithm is used to increase the population of MBTI LS to 3 levels, namely low, medium and high. Increasing the population of MBTI LS can increase the probability of forming an ideal group of MBTI. Intelligent agents are tested based on a quantitative analysis between experimental classes (applying intelligent agents), and control classes (without intelligent agents). Experiment results show an increase in performance and productivity is better in the experimental class than in the control class. It was concluded that the development of intelligent agents had a positive impact on group development based on the MBTI LS

Learning to diagnose collaboratively – Effects of adaptive collaboration scripts in agent-based medical simulations

We investigated how medical students' collaborative diagnostic reasoning, particularly evidence elicitation and sharing, can be facilitated effectively using agent-based simulations. Providing adaptive collaboration scripts has been suggested to increase effectiveness, but existing evidence is diverse and could be affected by unsystematic group constellations. Collaboration scripts have been criticized for undermining learners' agency. We investigate the effect of adaptive and static scripts on collaborative diagnostic reasoning and basic psychological needs. We randomly allocated 160 medical students to one of three groups: adaptive, static, or no collaboration script. We found that learning with adaptive collaboration scripts enhanced evidence sharing performance and transfer performance. Scripting did not affect learners’ perceived autonomy and social relatedness. Yet, compared to static scripts, adaptive scripts had positive effects on perceived competence. We conclude that for complex skills complementing agent-based simulations with adaptive scripts seems beneficial to help learners internalize collaboration scripts without negatively affecting basic psychological needs

InstanceCollage: a tool for the particularization of collaborative IMS-LD scripts

Current research work in e-learning and more specifically in the field of CSCL (Computer Supported Collaborative Learning) deals with design of collaborative activities, according to computer-interpretable specifications, such as IMS Learning Design, and their posterior enactment using LMSs (Learning Management Systems). A script that describes such collaborative activities is typically designed beforehand in order to structure collaboration, and defines the features that determine the behavior of the LMS, for instance, the sequence of activities or the groups/role distribution. In CSCL settings, group management and composition are especially relevant and affect the chances of achieving the expected learning outcomes. This paper presents a software tool, named InstanceCollage, which aims at facilitating the configuration and population of groups for IMS-LD scripts created with the authoring tool Collage, and discusses the implications of the IMS-LD specification with respect to this task. InstanceCollage is designed to process collaboration scripts based on CLFPs (Collaborative Learning Flow Patterns). Using this type of patterns, InstanceCollage focuses on the importance of understanding the function of groups within the learning strategy of the script. This paper describes the approach taken in InstanceCollage to facilitate this understanding for non-expert users. Additionally, two case studies are presented, which represent complex authentic collaborative learning scenarios, as a proof of concept of the functionality of this tool. The case studies are also used to illustrate the requirements of group configuration tools and to show that InstanceCollage complies to such requirements

System Orchestration Support for a Collaborative Blended Learning Flow

Portable and interactive technologies are changing the nature of collaborative learning practices and open up new possibilities for Computer Supported Collaborative Learning (CSCL). Now, activities occurring in and beyond the classroom can be combined and integrated leading to a new type of complex collaborative blended learning scenarios. However, to organize and structure these scenarios is challenging and represent a workload for practitioners, which hinder the adoption of these technology-enhanced practices. As an approach to alleviate this workload, this paper proposes a proof of concept of a technological solution to overcome the limitations detected in an analysis of an actual collaborative blended learning experiment carried out in a previous study. The solution consists on a Unit of Learning suitable to be instantiated with IMS Learning Design and complemented by a GenericService Integration system. This chapter also discusses to which extent the proposed solution covers the limitations detected in the previous study and how useful could be for reducing the orchestration effort in future experiences.This work has been partially funded by the Project Learn3 (TIN2008- 05163/TSI) from the Plan Nacional I+D+I and "Investigación y Desarrollo de Tecnologías para el e-Learning en la Comunidad de Madrid” funded by the Madrid Regional Government under grant No. S2009/TIC-1650