@article{58650,
  abstract     = {{Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.}},
  author       = {{Gräßler, Iris and Pottebaum, Jens and Nyhuis, Peter and Stark, Rainer and Thoben, Klaus-Dieter and Wiederkehr, Petra}},
  issn         = {{2942-6170}},
  journal      = {{Industry 4.0 Science}},
  keywords     = {{AI, artificial intelligence, Data Science, decision support, extreme data, Künstliche Intelligenz, product creation, product development}},
  number       = {{1}},
  publisher    = {{GITO mbH Verlag}},
  title        = {{{Hybrid Decision Support in Product Creation - Improving performance with data science and artificial intelligence}}},
  doi          = {{10.30844/i4sd.25.1.18}},
  volume       = {{2025}},
  year         = {{2025}},
}

@inproceedings{27491,
  abstract     = {{ Students often have a lack of understanding and awareness of where, how, and why personal data about them is collected and processed. Especially, when interacting with data-driven digital artifacts, an appropriate perception of the data collection and processing is necessary for self-determination. This dissertation deals with the development and evaluation of a concept called data awareness which aims to foster students’ self-determination interacting with data-driven digital artifacts.}},
  author       = {{Höper, Lukas}},
  booktitle    = {{21st Koli Calling International Conference on Computing Education Research}},
  isbn         = {{9781450384889}},
  keywords     = {{data awareness, machine learning, data science education, data-driven digital artifacts, artificial intelligence}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Developing and Evaluating the Concept Data Awareness for K12 Computing Education}}},
  doi          = {{10.1145/3488042.3490509}},
  year         = {{2021}},
}

@article{5910,
  author       = {{Heinemann, Birte and Opel, Simone and Budde, Lea and Schulte, Carsten and Frischemeier, Daniel and Biehler, Rolf and Podworny, Susanne and Wassong, Thomas}},
  isbn         = {{9781450365352}},
  journal      = {{Proceedings of the 18th Koli Calling International Conference on Computing Education Research - Koli Calling '18}},
  keywords     = {{Curriculum Development, Data Science, Secondary Sc, acm reference format, big data, curriculum development, data literacy, data science, educa-, interdisciplinary, secondary schools, tion}},
  number       = {{17}},
  pages        = {{1--5}},
  title        = {{{Drafting a Data Science Curriculum for Secondary Schools}}},
  doi          = {{10.1145/3279720.3279737}},
  year         = {{2018}},
}