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Mit Jupyter Notebooks prüfen

Citation Link: https://doi.org/10.15480/882.2435
Publikationstyp
Conference Poster not in Proceedings
Publikationsdatum
2019-09-26
Sprache
German
Author
Kastner, Marvin orcid-logo
Podleschny, Nicole orcid-logo
Institut
Maritime Logistik W-12 
Zentrum für Lehre und Lernen ZLL 
DOI
10.15480/882.2435
TORE-URI
http://hdl.handle.net/11420/3553
Lizenz
http://rightsstatements.org/vocab/InC/1.0/
Citation
E-Prüfungs-Symposium, Universität Siegen (2019)
Contribution to Conference
E-Prüfungs-Symposium 2019, Universität Siegen 
The learning outcome of the interdisciplinary master module „machine learning in logistics“ is the ability to visualize, clean, and interpreting big data, as well as identifying connections with methods of machine learning. The media-didactical challenge is to make machine learning accessible for those students who do not possess sound programming skills. For this, we chose Jupyter Notebooks. In the exercises as well as in the final exam, students use a pre-structured Jupyter Notebook in order to write or rewrite code. They also document their answers and solutions. The poster documents the implementation of Jupyter Notebooks into the exam scenario and describes the examining process.
Schlagworte
Computergestützte Prüfung
Jupyter Notebooks
Maschinelles Lernen
JupyterHub
DDC Class
620: Ingenieurwissenschaften
Projekt(e)
MaLiTuP - Maschinelles Lernen in Theorie und Praxis 
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