Towards reusability and tailorability in collaborative learning systems using IMS-LD and grid services

Bote-Lorenzo, Miguel L.; Hernández Leo, Davinia; Dimitriadis, Yannis; Asensio-Pérez, Juan I.; Gómez-Sánchez, Eduardo; Vega-Gorgojo, Guillermo; Vaquero-Gónzalez, Luis M.

doi:http://dx.doi.org/10.2316/Journal.208.2004.3.208-0810

Towards reusability and tailorability in collaborative learning systems using IMS-LD and grid services

Citation

Bote-Lorenzo ML, Hernández-Leo D, Dimitriadis Y, Asensio-Pérez JI, Gómez-Sánchez E, Vega-Gorgojo G, Vaquero-Gónzalez LM. Towards reusability and tailorability in collaborative learning systems using IMS-LD and grid services. Advanced Technology for Learning. 2004;1(3):129-38. DOI: 10.2316/Journal.208.2004.3.208-0810

Abstract

CSCL applications are complex distributed systems that pose/nspecial requirements towards achieving success in educational/nsettings. Flexible and efficient design of collaborative activities/nby educators is a key precondition in order to provide CSCL tailorable systems, capable of adapting to the needs of each/nparticular learning environment. Furthermore, some parts of/nthose CSCL systems should be reused as often as possible in/norder to reduce development costs. In addition, it may be necessary to employ special hardware devices, computational resources that reside in other organizations, or even exceed the/npossibilities of one specific organization. Therefore, the/nproposal of this paper is twofold: collecting collaborative/nlearning designs (scripting) provided by educators, based on/nwell-known best practices (collaborative learning flow patterns) in a standard way (IMS-LD) in order to guide the tailoring of CSCL systems by selecting and integrating reusable CSCL software units; and, implementing those units in the form of grid services offered by third party providers. More specifically, this paper outlines a grid-based CSCL system having these features and illustrates its potential scope and applicability by means of a sample collaborative learning scenario.