In this paper, we propose an abstract domain-neutral architecture for a cognitive digital twin (CDT) and a software platform to develop such CDTs, including machine reasoning capabilities. Sustainable development refers here to an abstract model that enables a holistic view of limiting resources and has an ability to adapt to different application domains while reusing existing resources. The proposed solution allows for a unified abstract representation and the development of a wide range of diverse digital twins, as well as facilitating their interoperability. The abstract architecture consists of a four-layer structure (observation/actuation layer, data management layer, reasoning layer, and simulation layer) with an upper ontology to which the domain ontology of the specific CDT is mapped. The architecture relies on semantic web technologies, including ontology-based reasoning using OWL, and a loosely coupled, component-based service-oriented software architecture. The platform utilizes a microservice architecture that enables separate, loosely coupled services on each layer, message queues to provide asynchronous communication, and possesses cloud technologies to achieve scalability. The proposed approach was validated by implementing a software platform prototype and demonstrating its key features through two dissimilar scenarios. The first scenario demonstrates simple sustainable energy management through IoT systems inside smart buildings, while the second one demonstrates knowledge quality management based on knowledge space theory.

This paper proposes a novel application of knowledge space theory for identifying discrepancies between the knowledge structure that experts expect students to have and the real knowledge structure that students demonstrate on tests. The proposed approach combines two methods of constructing knowledge spaces. The expected knowledge space is constructed by analysing the problem-solving process, while the real knowledge space is identified by applying a data-analytic method. These two knowledge spaces are compared for graph difference and the discrepancies between the two are analysed. In this paper, the proposed approach is applied to the domain of stoichiometry. Although there was a decent agreement between expected and real knowledge spaces, a number of relations that were not present in the expected one appeared in the real knowledge space. The obtained results led to a general conclusion for teaching stoichiometry and pointed to some potential improvements in the existing methods for evaluating cognitive complexity.

Purpose: The purpose of this paper is to identify the benefits of an approach in which document management systems (DMSs) are based on a formal and explicit document model, primarily in terms of facilitating domain-specific customization.Design/methodology/approach: Within this paper, a generic document model is proposed. The model consists of two layers. A general purpose layer, which represents common features of the documents, and a domain-specific layer, modeling properties particular to application domain. The general purpose layer is based on ISO 82045, providing high degree of interoperability with other systems developed with respect to this set of standard.Findings: Splitting document model into the layers enables DMSs to be tailored for each particular domain of application, depending on the general purpose layer. The existence of domain-specific layer allows documents to be interpreted differently in different domains of application.Practical implications: In order to enable customization of DMS for a particular domain, the implementation of domain-specific document layer is required. Also, the proposed model does not explicitly deal with document dynamics.Originality/value: The proposed document ontology is general enough to provide the representation of documents not depending on a specific scope of application, yet flexible enough to enable extensions through which domain-specific document features can be expressed. The separation of document model enables development of core DMS offering services relying explicitly on the general purpose layer on one hand, as well as domain-specific customization of DMS on the other.

This paper presents research on using a model-driven approach to the development and management of electronic courses. We propose a course management system which stores a course model represented as distinct machine-readable components containing domain knowledge of different course aspects. Based on this formally defined platform-independent source course model, the system programmatically generates a final course in different platform-specific target models. Currently, our system supports the generation of IMS learning design, SCORM, LAMS and Sakai courses. The case study presents a formal model of the Web programming course and its transformation to the supported target models.

Purpose: The purpose of this research is to enable dynamic customization of metadata that describes educational resources in digital repositories.Design/methodology/approach: Users need to describe educational resources in digital repositories according to a user-specific metadata set. As users generally do not have the skills to customize the software application manually, this approach relies on the techniques of model-driven software engineering, which should allow customization of the software application programmatically with no need to develop or order a new software application. An experiment was conducted to evaluate the proposed solution.Findings: A software platform for managing educational resources described by dynamically extendable metadata is proposed. The platform enables the creation of data models that are programmatically transformed to a Web application for the management of educational resources. In this way, users can create their own models of metadata that are relevant in a particular domain.Research limitations/implications: The solution has been verified by users with technical knowledge. The appropriateness of the model should still be explored for domain experts with little technical knowledge who desire to define new metadata in their domain.Practical implications: The solution can be used for digital repositories that store diverse educational resources. Each resource could be described using metadata that relates to the domain the resource belongs to.Originality/value: Digital repositories standardly describe educational resources using some general metadata, which are more focused on the physical characteristics of resources rather than their semantics. The proposed solution introduces custom domain-specific semantics into the description of the resources, which improves their retrieval.