Designing Configurations of CSCL Scripts Using Interaction Analysis Findings

Various learning strategies have been widely used in the Computer-Supported Collaborative Learning (CSCL) script design practice. For this reason strategies have been documented in a designer friendly way as collaborative flow design patterns (CLFP). A CLFP defines the sequence of the tasks that the strategy dictates as well as other elements needed for the various tasks, such as the duration of a task, the use of a particular tool for a given task and so on. Designers often face the difficulty of mixing and matching strategies in order to create personalized scripts that are more appropriate to the learners' preferences, knowledge level, needs and the learning context in general. Designers need to make configurations of a “traditional” CLFP in order to balance a variety of organizational, administrative, instructional and technological components. This task is even more challenging when such configurations need to be made on-the-fly, i.e. during the learning process and in response to the learner' actions and history of interaction. The aim of this paper is to discuss how the learners' interaction data that is collected during the CSCL process and analysed using interaction analysis indicators can be used to make valuable alternatives of a CSCL script depending on learning conditions. The design challenge is to automate as much as possible this configuration process in order to help practitioners easily proceed in making these configurations easily and quickly.