Authors:
Camila Morais Canellas
1
;
2
;
François Bouchet
2
;
Thibaut Arribe
1
and
Vanda Luengo
2
Affiliations:
1
Société Kelis, France
;
2
Sorbonne Université, CNRS, LIP6, F-75005 Paris, France
Keyword(s):
Learning Analytics, Models, Model-driven Engineering, Publishing Chains.
Abstract:
In a context of pedagogical resource production via publishing chains that are based on an model-driven engineering approach, we consider the proposition of a learning analytics implementation. We argue that, by using the same approach to carry out such an implementation versus a classical one, a series of benefits could be assessed, whether they are related to the fact that it is using this specific context, methodological approach or both. Perhaps one of the most particular benefits is the detailed knowledge of the semantics and structure of any document produced, that could therefore be automatically added to the traces/analysis. Other potential improvements discussed are: separation of content and form, interoperability, compliance with data privacy, maintainability, performance, multi format, customization and reproducibility.