2024
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2023
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2022
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2021
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2020
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2019
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2018
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2017
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2016
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2015
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2014
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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
2013
[182411] |
Title: Mit Jupyter Notebooks prüfen. |
Written by: Kastner, Marvin and Podleschny, Nicole |
in: <em>Beitrag zur Poster-Session des e-Prüfungs-Symposiums (ePS) in Siegen</em>. (2019). |
Volume: Number: |
on pages: |
Chapter: |
Editor: |
Publisher: |
Series: |
Address: |
Edition: |
ISBN: |
how published: |
Organization: |
School: |
Institution: |
Type: |
DOI: 10.15480/882.2435 |
URL: http://hdl.handle.net/11420/3553 |
ARXIVID: |
PMID: |
Note: malitup
Abstract: 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