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This article presents the instantiation of the Model of Adaptation of Learning Objects (MALO) developed in previous works, using the competencies to be developed in a given educational context. MALO has been developed for virtual environments based on an extension of the LOM standard. The model specifies modularly and independently two categories of rules, of adaptation and conversion, giving it versatility and flexibility to perform different types of adaptation to the learning objects, incorporating or removing rules in each category. In this work, we instance these rules of MALO using the competencies considered in a given educational context.

COVID-19 has generated a lot of information in different formats, and one of them is in the ontology format. Also, there are previous ontologies from other disciplines that can help to analyze the COVID-19 pandemic. Thus, due to the large quantity of COVID-19 information in the form of ontologies, approaches to ontology integration and interoperability could be beneficial. In this context, this research proposes a new ontology, called COVID-19 Pandemic ontology, which is the product of an ontological engineering process proposed in this research that allows the integration of several ontologies to cover all the aspects of this infectious disease. The ontological engineering process defines tasks of fusion, alignment, and linking for integrating the ontologies. The resulting pandemic ontology provides a simple repository for storing information about the COVID-19, reusing existing ontologies, to offer multiple views about the disease, including the social context. This ontology has been tested in different case studies to prove its capabilities to infer useful information about the COVID-19 pandemic.