Volltext-Downloads (blau) und Frontdoor-Views (grau)

On the Use of low-Code and no-Code tools for teaching data science in applied industrial and university settings

  • The design and development of smart products and services with data science enabled solutions forms a core topic of the current trend of digitalisation in industry. Enabling skilled staff, employees, and students to use data science in their daily work routine of designing such products and services is a key concern of higher education institutions, including universities, company workshop providers and in further education. The scope and usage scenario of this paper is to assess software modules (‘tools’) for integrated data and analytics as service (DAaaS). The tools are usually driven by machine learning, may be deployed in cloud infrastructures, and are specifically targeted at particular needs of the industrial manufacturing, production, or supply chain sector. The paper describes existing theories and previous work, namely methods used in didactics, work done for visually designing and using machine learning algorithms (no-code / low- code tools), as well as combinations of these two topics. For tools available on the market, an extended assessment of their suitability for a set of learning scenarios and personas is discussed.

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Author:Martin DoblerORCiD, Jürg Meierhofer, Klaus Frick, Marcus Bentele
Parent Title (German):28th IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) & 31st International Association for Management of Technology (IAMOT) Joint Conference, Nancy, France, 19 - 23 June 2022
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Year of publication:2022
Release Date:2023/02/06
Tag:Data science; Didactics; Teaching support; Tool selection
Number of pages:8
First Page:242
Last Page:249
Organisationseinheit:Forschung / Forschungszentrum Business Informatics
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft
Open Access?:nein
Peer review:wiss. Beitrag, peer-reviewed
Publicationlist:Dobler, Martin