Conclusion

The purpose of this book is notv to propose, once again, an improvement of the current DLE analysis models. It is rather to propose a new approach consisting of representing these environments, taking into account their complexity. We thus decided to abandon some past practices and use systems science and in particular the complex systems theory. As Bachelard said, access to science involves accepting the contradiction of the past. This epistemological rupture comes from our desire to break from the illusory belief that Cartesian and analytical modeling can reach the objectivity built in principle. Therefore, we decided to register the models on which we will reason in the paradigm of systemic modeling, one of the features of which is to consider models as living and evolutionary constructions rather than as preconceived data. Given the highly innovative nature of this change, our goal is not to just apply this systemic modeling to a particular environment (although practical applications are often very telling), but rather to cast the foundations of this new approach in order to initiate new research in this sense. Remember: this model has no vocation to serve to the design of an adaptive learning environment, which would claim to manage all the cases in order to allow personalized learning. So far, this goal seems impossible to achieve in view of the excessive amount of variables involved. The approach we propose instead seeks to model latest generation DLE using ...

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