Education should not be a privilege but a common good. It should be openly
accessible to everyone, with as few barriers as possible; even more so for key
technologies such as Machine Learning (ML) and Data Science (DS). Open
Educational Resources (OER) are a crucial factor for greater educational
equity. In this paper, we describe the specific requirements for OER in ML and
DS and argue that it is especially important for these fields to make source
files publicly available, leading to Open Source Educational Resources (OSER).
We present our view on the collaborative development of OSER, the challenges
this poses, and first steps towards their solutions. We outline how OSER can be
used for blended learning scenarios and share our experiences in university
education. Finally, we discuss additional challenges such as credit assignment
or granting certificates.
Описание
[2107.14330] Developing Open Source Educational Resources for Machine Learning and Data Science
%0 Generic
%1 bothmann2021developing
%A Bothmann, Ludwig
%A Strickroth, Sven
%A Casalicchio, Giuseppe
%A Rügamer, David
%A Lindauer, Marius
%A Scheipl, Fabian
%A Bischl, Bernd
%D 2021
%K aiskills data learning machine ml oer science
%T Developing Open Source Educational Resources for Machine Learning and Data Science
%U http://arxiv.org/abs/2107.14330
%X Education should not be a privilege but a common good. It should be openly
accessible to everyone, with as few barriers as possible; even more so for key
technologies such as Machine Learning (ML) and Data Science (DS). Open
Educational Resources (OER) are a crucial factor for greater educational
equity. In this paper, we describe the specific requirements for OER in ML and
DS and argue that it is especially important for these fields to make source
files publicly available, leading to Open Source Educational Resources (OSER).
We present our view on the collaborative development of OSER, the challenges
this poses, and first steps towards their solutions. We outline how OSER can be
used for blended learning scenarios and share our experiences in university
education. Finally, we discuss additional challenges such as credit assignment
or granting certificates.
@misc{bothmann2021developing,
abstract = {Education should not be a privilege but a common good. It should be openly
accessible to everyone, with as few barriers as possible; even more so for key
technologies such as Machine Learning (ML) and Data Science (DS). Open
Educational Resources (OER) are a crucial factor for greater educational
equity. In this paper, we describe the specific requirements for OER in ML and
DS and argue that it is especially important for these fields to make source
files publicly available, leading to Open Source Educational Resources (OSER).
We present our view on the collaborative development of OSER, the challenges
this poses, and first steps towards their solutions. We outline how OSER can be
used for blended learning scenarios and share our experiences in university
education. Finally, we discuss additional challenges such as credit assignment
or granting certificates.},
added-at = {2022-02-15T10:29:11.000+0100},
author = {Bothmann, Ludwig and Strickroth, Sven and Casalicchio, Giuseppe and Rügamer, David and Lindauer, Marius and Scheipl, Fabian and Bischl, Bernd},
biburl = {https://www.bibsonomy.org/bibtex/2861c9d6ce923ac040a9d00cce67c7108/jaeschke},
description = {[2107.14330] Developing Open Source Educational Resources for Machine Learning and Data Science},
interhash = {3137b327b40e7a464d98e5fc03e9d54c},
intrahash = {861c9d6ce923ac040a9d00cce67c7108},
keywords = {aiskills data learning machine ml oer science},
note = {cite arxiv:2107.14330Comment: 6 pages},
timestamp = {2022-02-15T10:29:11.000+0100},
title = {Developing Open Source Educational Resources for Machine Learning and Data Science},
url = {http://arxiv.org/abs/2107.14330},
year = 2021
}