Computational Representation of Collaborative Learning Flow Patterns Using IMS Learning Desing

Davinia Hernández-Leo (1) , Yannis Dimitriadis (1) , Juan I. Asensio-Perez (1)

1 Department of Signal Theory, Communications and Telematic Engineering

The identification and integration of reusable and customizable CSCL (Computer Supported Collaborative Learning) may benefit from the capture of best practices in collaborative learning structuring. The authors have proposed CLFPs (Collaborative Learning Flow Patterns) as a way of collecting these best practices. To facilitate the process of CLFPs by software systems, the paper proposes to specify these patterns using IMS Learning Design (IMS-LD). Thus, teachers without technical knowledge can particularize and integrate CSCL tools. Nevertheless, the support of IMS-LD for describing collaborative learning activities has some deficiencies: the collaborative tools that can be defined in these activities are limited. Thus, this paper proposes and discusses an extension to IMS-LD that enables to specify several characteristics of the use of tools that mediate collaboration. In order to obtain a Unit of Learning based on a CLFP, a three stage process is also proposed. A CLFP-based Unit of Learning example is used to illustrate the process and the need of the proposed extension.

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Davinia Hernández-Leo, Yannis Dimitriadis, Juan I. Asensio-Perez. Computational Representation of Collaborative Learning Flow Patterns Using IMS Learning Desing. Journal of Educational Technology and Society, 2005, 8(4), pp.75-89. ⟨hal-00190154⟩