Logic Programming allows for the exploration and development of Computational Thinking skills. Those being abstraction, decomposition, pattern recognition, and generalisation, among others, which are fundamental for problem-solving. What is more, Logic Programming helps building up Logical Thinking skills, also distinguishing valid arguments from fallacies and contradictions, as well as establishing connections between arguments through reasoning, to name some. These practices are not only essential for the development of knowledge in STEM, but also in Computer Science disciplines. Recently, arguments have been standing in favour of the early integration of programming within compulsory education. The international scientific community is looking forward to researching and initiatives being deployed towards this direction. This article introduces Metagame -Metajuego-, a methodological proposal which integrates key aspects to facilitate the effective integration of Logic Programming in primary education through collaborative and gamifying strategies. Furthermore, concrete cases of games are presented which have been formulated thanks to applying the proposed methodological approach to different educational contexts. The narratives upon which these games are based, incorporate learning cores presented in the curricular school designs where they were implemented.

In this work, we treat web interoperability in terms of interchanging ontologies (as knowledge models) within user-centred ontology engineering environments, involving visual and serialised representations of ontologies. To do this, we deal with the tool interoperability problem by re-using an enough expressive ontology-driven metamodel, named KF, proposed as a bridge for interchanging both knowledge models. We provide an extensible web framework, named crowd 2.0, unifying the standard conceptual data modelling languages for generating OWL 2 ontologies from semantic visualisations. Visual models are designed as UML, ER or ORM 2 diagrams, represented as KF instances, and finally, formalised as DL-based models. Reasoning results may be newly incorporated into the shared KF instance to be visualised in any of the provided languages.