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As in innumerable other fields of activity, information and communication technologies have invaded the field of teaching and training. A recent study of Bersin (2006) shows that North American industries’ budget for e-learning and instrumented training amounts to 46.6 billion dollars. The amount spent on external technologies, products and services represents around 14.8 billion dollars. The amount concerned with tele-learning (Learning Management Systems - LMS) represents 3 to 7% of the total expenditure on training in an organisation. In the last few years, a large number of big industrial groups have tried to improve and consolidate the usage that is made of LMSs. According to (Bersin, 2006), in the next 12 months, a third of companies plan to increase the number of systems used within their organisations. Virtual class systems have been adopted in numerous sectors. In Bersin (2006) we can read that 60% of the companies listed in the study use virtual classes for company training. In another study from the same group, published in January 2006, the global budget of the LMSs was 480 million dollars.
Moreover, the performances of e-learning solutions have up until now, been judged to be insufficient by the companies. In particular, they do not have a real idea of the results of the implementation of such solutions. Among the reasons for this state of affairs, we think that a lack of knowledge of what happens in a “real use situation” plays an important role. In fact, an understanding of what is involved in the interaction between learner(s) and system(s) is fundamental for improving the appropriation of these systems and for their efficient use. The researchers in the technology enhanced learning field, who study instrumented learning situations, discover flaws in the conceptualization of interactions between learner(s) and system(s), particularly as multiple learners are involved with them: the learner himself, the system designer etc. In this context, it seems to us that the fact of tracing the learner-environment interactions is a very interesting path to follow, so that they can be later used to help learners appropriate the system. Besides, it appears to be both relevant and urgent that a typology is proposed for computer assisted learning situations which already use computer traces of the interactions between learner(s) and system(s). This is precisely what this conceptual article is concerned with.
We will firstly present the context of the intelligent learning environments, followed by a classification of the systems tracing interactions. We will finally explain how traces can facilitate the learner’s appropriation of the environment.