The paper investigates interdisciplinarity of scientific fields based on graph of collaboration between the researchers. A new measure for interdisciplinarity is proposed that takes into account graph content and structure. Similarity between science categories is estimated based on text similarity between their descriptions. The proposed new measure is applied in exploratory analysis of research community in Slovenia. We found that Biotechnology and Natural sciences are the most interdisciplinary in their publications and collaborations on research projects. In addition evolution of interdisciplinarity of scientific fields in Slovenia is observed, showing that over the last decade interdisciplinarity increases the fastest in Medical sciences mainly due to collaborations with Natural and Technical sciences.

Abstract. Tagging educational content with knowledge components (KC) is key to providing useable reports to teachers and for use by assessment algorithms to determine knowledge component mastery. With many systems using fine-grained KC models that range from dozens to hundreds of KCs, the task of tagging new content with KCs can be a laborious and time consuming one. This can often result in content being left untagged. This paper describes a system to assist content developers with the task of assigning KCs by suggesting knowledge components for their content based on the text and its similarity to other expert-labeled content already on the system. Two approaches are explored for the suggestion engine. The first is based on support vector machines text classifier. The second utilizes K-nearest neighbor algorithms employed in the Lemur search engine. Experiments show that KCs suggestions were highly accurate.

Purpose The purpose of this paper is to propose an approach for conceptualizing science based on collaboration and competences of researchers. Design/methodology/approach The research is conducted by exploratory analysis of collaboration and competences using case studies from humanistic, engineering, natural sciences and a general topic. Findings The findings show that by applying the proposed approach on bibliographic data that readily exist for many national sciences as well as for international scientific communities, one can obtain useful new insights into the research. The approach is demonstrated with the following exploratory findings: identification of important connections and individual researchers that connect the community of anthropologists; collaboration of technical scientists in the community of anthropologists caused by an interdisciplinary research project; connectivity, interdisciplinary and structure of artificial intelligence, nanotechnology and a community based on a general topic; and identifying research interest shift described with concretization and topic-shift. Practical implications As demonstrated with the practical implementation (http://scienceatlas.ijs.si/), users can obtain information of the most relevant competences of a researcher and his most important collaborators. It is possible to obtaining researchers, community structure and competences of an arbitrary research topic. Social implications The map for collaboration and competences of a complete science can be a crucial tool for policy-making. Social scientists can use the results of the proposed approach to better understand and direct the development of science. Originality/value Originality and value of the paper is in combining text (competences) and network (research collaboration and co-authoring) approaches for exploring science. Additional values give the results of analysis that demonstrate the approach.

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