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mance skills. Collaborative sessions with autism ex-
perts have been conducted.
First results emphasized the interest in validating
the adaptation elements and rules on the early design
phases. It could be useful to avoid the re-engineering
cost when adaptation rules are invalidated when test-
ing a prototype. Moreover, MDE tools provide some
facilities in order to drive and ease the development of
a basic console-based generator. Models are both ex-
ecutable by the generator, and human-readable when
specified with a tree-based model editor (generated
by EMF from the metamodel specification). It then
offers a support for conducting collaborative design
sessions between computer scientists and domain ex-
perts.
Our very first validation sessions confirmed our
proposition to help domain experts in designing and
validating the required rules to drive the generation
of adapted learning scenarios. Further developments
will focus on integrating the generator output models
(XML file) in our Unity-based game prototype. It will
allow us to propose more accurate validations with
respect to the prototype independence to changes on
the generator. We also highlighted that the dynamical
domain rules, like the generation rules and the map-
ping rules between the difficulty levels and the game
objects involved within a scene resolution, are not ex-
plicit: they are hard-coded in the generator. Because it
can also lead to coding issues, further research works
about specifying such informations have to be under-
taken. We have started tackling this issue by exper-
imenting various MDE techniques: models transfor-
mations, model weaving, validation of models in con-
formance to rules written with the Object Constraint
Language (OCL), etc.
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