Conference proceedings article
Computational Thinking for Design Science Researchers - A Modular Training Approach
Publication Details
Authors: | Zahn, E.; Dickhaut, E.; Vonhof, M.; Söllner, M. |
Editor: | Gerber, Aurona; Baskerville, Richard |
Publisher: | Springer Cham |
Place: | Basel |
Publication year: | 2023 |
Pages range : | 360-374 |
Book title: | 18th International Conference on Design Science Research in Information Systems and Technology, DESRIST 2023 |
Title of series: | Lecture Notes in Computer Science |
Volume number: | 13873 |
ISBN: | 978-3-031-32807-7 |
DOI-Link der Erstveröffentlichung: |
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.KeywordsTransferable SkillsComputational ThinkingDesign Science Research
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.KeywordsTransferable SkillsComputational ThinkingDesign Science Research
