|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||On the use of low-code and no-code tools for teaching data science in applied industrial and university settings|
|Proceedings:||Conference Proceedings of 2022 IEEE 28th ICE/IMT & 31st IAMOT joint conference|
|Editors of the parent work:||Morel, Laure|
|Conference details:||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|
|Publisher / Ed. Institution:||IEEE|
|Subjects:||Data science; Teaching support; Tool selection; Didactics|
|Subject (DDC):||005: Computer programming, programs and data |
378: Higher education
|Abstract:||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.|
|Fulltext version:||Published version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Data Analysis and Process Design (IDP)|
|Published as part of the ZHAW project:||DATA4Des – Data Product / Service Design unterrichten|
|Appears in collections:||Publikationen School of Engineering|
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Dobler, M., Meierhofer, J., Frick, K., & Bentele, M. (2022). On the use of low-code and no-code tools for teaching data science in applied industrial and university settings [Conference paper]. In L. Morel, L. Dupont, & M. Camargo (Eds.), Conference Proceedings of 2022 IEEE 28th ICE/IMT & 31st IAMOT joint conference (pp. 242–249). IEEE. https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033266
Dobler, M. et al. (2022) ‘On the use of low-code and no-code tools for teaching data science in applied industrial and university settings’, in L. Morel, L. Dupont, and M. Camargo (eds) Conference Proceedings of 2022 IEEE 28th ICE/IMT & 31st IAMOT joint conference. IEEE, pp. 242–249. Available at: https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033266.
M. Dobler, J. Meierhofer, K. Frick, and M. Bentele, “On the use of low-code and no-code tools for teaching data science in applied industrial and university settings,” in Conference Proceedings of 2022 IEEE 28th ICE/IMT & 31st IAMOT joint conference, 2022, pp. 242–249. doi: 10.1109/ICE/ITMC-IAMOT55089.2022.10033266.
Dobler, Martin, et al. “On the Use of Low-Code and No-Code Tools for Teaching Data Science in Applied Industrial and University Settings.” Conference Proceedings of 2022 IEEE 28th ICE/IMT & 31st IAMOT Joint Conference, edited by Laure Morel et al., IEEE, 2022, pp. 242–49, https://doi.org/10.1109/ICE/ITMC-IAMOT55089.2022.10033266.
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