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For almost 20 years, Open Educational Resources (OER) are an integral part of the debate about the digitisation of education. However, the empirical landscape of OER research is vivid and largely obscure. This article reviews the state of international empirical research on OER to reveal trends and gaps and, in this manner, identify possible desiderata for further research. We use a systematic mapping approach to examine the empirical English-speaking research landscape from 2015 to 2019. The results reveal that research primarily concentrates on the higher education sector while only a few studies are available for the school and other educational sectors. In terms of research methodologies, quantitative approaches are prevalent, with most of them being survey-based. The main research interests of the empirical studies lie in the perception of OER and the barriers to their use in educational practices. Open textbooks as a form of OER and their comparative cost advantages or qualitative comparability with traditional educational material constitute an emerging research field that is almost exclusively located in the U.S. Research gaps exist regarding the usability and user-friendliness of OER repositories. Addressing these gaps could support the numerous initiatives in different countries to establish and equip repositories. Additional gaps for empirical research were identified regarding the effects of the use of OER on pedagogical approaches and established educational practices.

Auf Grundlage einer Systematisierung des bisherigen Forschungsstands werden in diesem Beitrag einerseits Lebensbereiche definiert, in denen Geflüchtete von Diskriminierung und Rassismus betroffen sind. Andererseits werden konkrete Diskriminierungs- und Rassismusformen sowie -ausprägungen aufgezeigt. Die Befunde belegen, dass geflüchtete Menschen in Deutschland in nahezu allen Lebensbereichen individuell bzw. institutionell Diskriminierung und Rassismus erfahren.

ObjectivesThe secondary use of medical data contained in electronic medical records, such as hospital discharge letters, is a valuable resource for the improvement of clinical care (e.g. in terms of medication safety) or for research purposes. However, the automated processing and analysis of medical free text still poses a huge challenge to available natural language processing (NLP) systems. The aim of this study was to implement a knowledge-based best of breed approach, combining a terminology server with integrated ontology, a NLP pipeline and a rules engine.MethodsWe tested the performance of this approach in a use case. The clinical event of interest was the particular drug-disease interaction “proton-pump inhibitor [PPI] use and osteoporosis”. Cases were to be identified based on free text digital discharge letters as source of information. Automated detection was validated against a gold standard.ResultsPrecision of recognition of osteoporosis was 94.19%, and recall was 97.45%. PPIs were detected with 100% precision and 97.97% recall. The F-score for the detection of the given drug-disease-interaction was 96,13%.ConclusionWe could show that our approach of combining a NLP pipeline, a terminology server, and a rules engine for the purpose of automated detection of clinical events such as drug-disease interactions from free text digital hospital discharge letters was effective. There is huge potential for the implementation in clinical and research contexts, as this approach enables analyses of very high numbers of medical free text documents within a short time period.