Petri net-based engine for adaptive learning

In this paper, an IMS LD engine based on a Petri net model that represents the operational semantics of units of learning based on this specification is presented. The Petri nets of this engine, which is called OPENET4LD, verify the structural properties that are desirable for a learning flow and also facilitate the adaptation of the engine if potential changes in the IMS LD specification were proposed. Furthermore, OPENET4LD has an open and flexible architecture based on a set of ontologies that describe both the semantics of the Petri nets execution and the semantics of each learning flow component of IMS LD. Furthermore, the implementation of this architecture has been exhaustively validated with a number of UoLs that are compliant with the levels A and B of IMS LD.

► We model a Petri net-based operational semantics for adaptive learning.
► We formally verify the properties of the proposed Petri net model.
► We developed an ontology-based and Petri net-based engine for IMS Learning Design.
► The engine can be easily adapted to changes in the semantics of the IMS LD.
► The engine has the ability to modify the adaptation mechanism of IMS LD in runtime.