A multicase evaluation study of Collage has shown that the use of CLFPs facilitates the design task, and that the tool itself provides an intuitive and easy way for non-expert users to design complex collaborative learning scenarios (Hernandez-Leo et al., 2008).
In the previous section we indicated that InstanceCollage is focused on scripts generated with Collage, that is, IMSLD scripts based on CLFPs. This section discusses the technical implications of IMS-LD with respect to group particularization.
On the contrary, in the case of all the other CLFPs Collage takes the approach of defining one single role for each group type, thus leaving the creation of the necessary copies for the instantiation phase.
Based on the positive evaluation of Collage, the importance of group formation, and the relation between groups and the learning strategy of CLFPs, InstanceCollage was proposed to facilitate the task of group particularization.
The graphical representations for CLFPs depict, at least, phases and groups.
To do this, the user can browse through the different CLFPs and their phases.
The script was, similarly to that of Case Study A, composed by the PYRAMID and JIGSAW CLFPS, and PEER REVIEW activities; group particularization was done almost in the same way, but without configuring TPS groups.
In the two cases, the collaborative activities were predefined in a CSCL script, based on CLFPs. An LMS was used to support the enactment of the activities.
Information from CLFPs is used to display and communicate information about the purpose of groups within the script, not only during design, but also in the instantiation phase.
The first and main limitation is derived from the usage of CLFPs, which determine that only CLFP-based IMS-LD scripts can be processed by this tool.
CLFPs have prove useful to facilitate the access to collaborative learning scripts for non-expert users (for instance, teachers), and therefore InstanceCollage can help such users to close the life-cycle of CSCL scripts.
In addition to such strategies, CLFPs provide information about how to configure groups; see for instance the case of the JIGSAW in Figure 2.