Developing Open Source Educational Resources for Machine Learning and Data Science

Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl
Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, PMLR 207:1-6, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v207-bothmann23a, title = {Developing Open Source Educational Resources for Machine Learning and Data Science}, author = {Bothmann, Ludwig and Strickroth, Sven and Casalicchio, Giuseppe and R\"ugamer, David and Lindauer, Marius and Scheipl, Fabian and Bischl, Bernd}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, pages = {1--6}, year = {2023}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, volume = {207}, series = {Proceedings of Machine Learning Research}, month = {19--23 Sep}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v207/bothmann23a/bothmann23a.pdf}, url = {https://proceedings.mlr.press/v207/bothmann23a.html}, 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.} }
Endnote
%0 Conference Paper %T Developing Open Source Educational Resources for Machine Learning and Data Science %A Ludwig Bothmann %A Sven Strickroth %A Giuseppe Casalicchio %A David Rügamer %A Marius Lindauer %A Fabian Scheipl %A Bernd Bischl %B Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop %C Proceedings of Machine Learning Research %D 2023 %E Katherine M. Kinnaird %E Peter Steinbach %E Oliver Guhr %F pmlr-v207-bothmann23a %I PMLR %P 1--6 %U https://proceedings.mlr.press/v207/bothmann23a.html %V 207 %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.
APA
Bothmann, L., Strickroth, S., Casalicchio, G., Rügamer, D., Lindauer, M., Scheipl, F. & Bischl, B.. (2023). Developing Open Source Educational Resources for Machine Learning and Data Science. Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, in Proceedings of Machine Learning Research 207:1-6 Available from https://proceedings.mlr.press/v207/bothmann23a.html.

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