Conceptual Correspondence Monitoring: Multimode Information Logistics Approach

Abstract. The paper addresses the problems arising in situations where conceptual correspondence has to be monitored, i.e., there are two or more structures of concepts which have a physical or abstract mapping and the changes in the structures of concepts may introduce the changes in the mapping. Usually the monitoring of conceptual correspondence requires manual, semiautomatic, and automatic information processing and exposes high level of complexity. The integration of different types of information processing units can be achieved by the use of multimode information logistics. The paper discusses challenges of the use of multimode information logistics in monitoring conceptual correspondence and proposes an approach that helps to partly meet the discussed challenges by jointly using functional and morphological spaces of representation of information logistics networks. The proposed approach is illustrated by an example of monitoring conceptual correspondence between knowledge demand and offer in the area of education

Variability Handling in Educational Context

Today there are many different forms of educational activities present, e.g., traditional lecturing, e-learning, blended learning and living labs. Also, the audience becomes more and more international and heterogeneous in terms of background knowledge of students, their educational purposes, capabilities and expectations. This introduces a high level of variability in educational settings and requires new methods and tools for managing this variability. Customized application of feature models, known in software product line management, is one possible solution applicable for variability handling in educational context. This paper proposes the development of a feature model as the method for variability handling

ORTUS - Gateway to University IS

Each university engages in supporting its processes with ICT solutions. The decision about the choice and variety of the ICT is based on the history, the competence and the competitiveness of the university. This paper describes the core information systems (IS) in Riga Technical University, particularly, the university portal ORTUS that facilitates the access and the use of existing and new core information systems aimed to support the study, science and administrative processes. The way of organizing and integrating systems through ORTUS influences usage and overall acceptance of the provided services

Monitoring Services to Support Continuous Curriculum Engineering

Continuous and rapid developments in science and technology have raised a challenge for compliance between the learning content provided by study programs and (1) the actual state of art in the domains the programs are addressing and (2) the actual needs of companies that will employ the graduates of these programs. To achieve such compliance continuously, the digitalization of learning content (curriculum) engineering could provide supporting tools that facilitate awareness of incompliance, which is the first step in introducing changes in the learning content. In this article we discuss how such awareness, regarding the needs of companies, can be supported by a service system that monitors the gap between educational demand and offer. The proposed service system provides possibilities for automatic, semi-automatic and manual analysis of texts representing the educational demand, versus the texts representing the educational offer. The implementation of the service system has been demonstrated, having been applied at university. The experiments showed that the system can provide valuable information for educational content development, but its maintenance and incorporation in study process management and curriculum engineering still require additional research

Conceptual Correspondence Monitoring: Multimode Information Logistics Approach

The paper addresses the problems arising in situations where conceptual correspondence has to be monitored, i.e., there are two or more structures of concepts which have a physical or abstract mapping and the changes in the structures of concepts may introduce the changes in the mapping. Usually the monitoring of conceptual correspondence requires manual, semi-automatic, and automatic information processing and exposes high level of complexity. The integration of different types of information processing units can be achieved by the use of multimode information logistics. The paper discusses challenges of the use of multimode information logistics in monitoring conceptual correspondence and proposes an approach that helps to partly meet the discussed challenges by jointly using functional and morphological spaces of representation of information logistics networks. The proposed approach is illustrated by an example of monitoring conceptual correspondence between knowledge demand and offer in the area of education

Application of interactive classification system in university study course comparison

The growing amount of information in the world has increased the need for computerized classification of different objects. This situation is present in higher education as well where the possibility of effortless detection of similarity between different study courses would give the opportunity to organize student exchange programmes effectively and facilitate curriculum management and development. This area which currently relies on manual time-consuming expert activities could benefit from application of smartly adapted machine learning technologies. Data in this problem domain is complex leading to inability for automatic classification approaches to always reach the desired result in terms of classification accuracy. Therefore, our approach suggests an automated/semi-automated classification solution, which incorporates both machine learning facilities and interactive involvement of a domain expert for improving classification results. The system’s prototype has been implemented and experiments are carried out. This interactive classification system allows to classify educational data, which often comes in unstructured or semi-structured, incomplete and/or insufficient form, thus reducing the number of misclassified instances significantly in comparison with the automatic machine learning approac