Herwig, MauriceHundeshagen, NorbertKastaun, MaritKollenberg, CedricSchulz, SandraKiesler, Natalie2024-09-032024-09-0320242944-7682https://dl.gi.de/handle/20.500.12116/44529Reductions play a crucial role in the theory of computer science, aiding in the identification of computationally unsolvable or intractable problems. Despite their significance, mastering reductions remains challenging for students due to their high level of abstraction. In this work we report on an educational approach to learn reductions in a more practical way as a programming exercise. Through a pilot study (𝑛 = 41) with three measurement points, insights were gathered on the usage of a prototype learning tool for reductions, leveraging Python as the main computational model. Initial findings highlight further enhancements of computer-aided learning and teaching of reductions, such as incorporating mathematical foundations in a tools feedback, visualizing and generically generating problem instances, as well as improving extensibility by simplifying the creation of exercises.encomplexitycomputabilitylearning toolNP-completenessreductionsText/Conference poster10.18420/delfi2024_452944-7682