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- W4377078265 abstract "Addressing and solving challenges by designing innovative artifacts is one of the main objectives of design science research (DSR). However, to achieve this goal, learning the theory of DSR and its methodology alone is not enough. We argue that computational thinking (CT) is an important skill set for design science researchers, since it helps to understand and structure problems from a computational point of view, which is an important basis for developing effective and innovative information system artifacts. CT consists of four core components: (1) dividing the problem, (2) abstraction, (3) pattern recognition, and (4) algorithmic thinking. Therefore, it is a skill set that can support DSR researchers in a broad way. However, so far, CT is rarely taught and trained and mainly not part of DSR courses. To close this gap and to train CT comprehensively, we develop a course based on low code programming, in other words, programming with little to no code. Our training can be embedded in DSR courses in a modular way. Thus, during a DSR course, students and researchers can develop and improve various prototypes with little effort and transfer the acquired competence to new design projects. As a central contribution of our study, we show how the training of CT can be applied in a modular way in DSR courses." @default.
- W4377078265 created "2023-05-20" @default.
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- W4377078265 date "2023-01-01" @default.
- W4377078265 modified "2023-09-30" @default.
- W4377078265 title "Computational Thinking for Design Science Researchers – A Modular Training Approach" @default.
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- W4377078265 doi "https://doi.org/10.1007/978-3-031-32808-4_23" @default.
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