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Start date: 2024 × DDC: 510 ×

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    QuaMath – A Large-scale Implementation Program to improve Mathematics Education
    (Hannover : Technische Informationsbibliothek, 2024-06-11) Hallemann, Svea
    This Mini-Review introduces the QuaMath program (“Developing Quality in Mathematics classrooms and teacher professional development”). The national 10-year program aims to develop the quality of mathematics instruction and teacher professional development in collaboration with the federal states. QuaMath is conducted within the framework of the DZLM (Deutsches Zentrum für Lehrkräftebildung Mathematik (German Center for Mathematics Teacher Education, a network of 12 German universities collaborated with the IPN Leibniz-Institute for Science and Mathematics Education)). Working with teachers and practitioners, the DZLM develops, implements, and researches effective training and support programs in mathematics for teachers and early childhood educators. I address the special role of the 400 facilitators in the implementation and success of the QuaMath program. They themselves receive intense training from the Consortium of Mathematics Education Professors (DZLM).
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    Bridging the Gap Between (AI-) Services and Their Application in Research and Clinical Settings Through Interoperability: the OMI-Protocol
    (Hannover : Technische Informationsbibliothek, 2024-02) Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Sigle, Stefan; Werner, Patrick; Schweizer, Simon; Caldeira, Liliana; Hosch, René; Dyrba, Martin; Fegeler, Christian; Grönke, Ana; Seletkov, Dmitrii; Kotter, Elmar; Nensa, Felix; Wehrle, Julius; Kaufmes, Kevin; Scherer, Lucas; Nolden, Marco; Boeker, Martin; Schmidt, Marvin; Pelka, Obioma; Braren, Rickmer; Stump, Shura-Roman; Graetz, Teresa; Pogarell, Tobias; Susetzky, Tobias; Wieland, Tobias; Parmar, Vicky; Wang, Yuanbin
    Artificial Intelligence (AI) in research and clinical contexts is transforming the areas of medical and life sciences permanently. Aspects like findability, accessibility, interoperability, and reusability are often neglected for AI-based inference services. The Open Medical Inference (OMI) protocol aims to support remote inference by addressing the aforementioned aspects. Key component of the proposed protocol is an interoperable registry for remote inference services, which addresses the issue of findability for algorithms. It is complemented by information on how to invoke services remotely. Together, these components lay the basis for the implementation of distributed inference services beyond organizational borders. The OMI protocol considers prior work for aspects like data representation and transmission standards wherever possible. Based on Business Process Modeling of prototypical use cases for the service registry and common inference processes, a generic information model for remote services was inferred. Based on this model, FHIR resources were identified to represent AI-based services. The OMI protocol is first introduced using AI-services in radiology but is designed to be generalizable to other application domains as well. It provides an accessible, open specification as blueprint for the introduction and implementation of remote inference services.