Interpreting learners´ traces in collaborative learning environments

Currently, numerous researches made several treatments on the traces recorded from distance learning environments. These researches collect traces from different sources to solve several problems: (i) support learners, (ii) engineering/reengineering of learning devices and (iii) adaptation/personalization of learning environments. The traces describe the learners´ actions where they interacting with the learning platform. Also, several researches have visualized the traces for the tutors, teachers and designers to help them in their various tasks. However, several problems arise in the learning process of learners where the most of works visualize traces without any interpretation for these traces. The goal of this paper is to propose a new approach that aims at interpreting learners´ traces and providing recommendations for them. Furthermore, it aims at offering an interpretation for the tutors to help them in their tasks of monitoring and tutoring. Many tools can be used for interpreting traces. Among these tools, we can quote the Animated Conversational Agents (ACA). So, a new approach is proposed for interpreting traces using an animated conversational agent. For practical applications, the validation of the model is examined in the context of the collaborative learning platform LETline 2.0 (www.labstic.com/letline/). The developed tool was experimented at an Algerian university. After the experiment and analyzing the results, we observe that the proposed approach helps the learners by interpreting their learning traces and helps the tutors to know the levels of their learners. The feasibility of this approach is proved through concrete cases.