Developing Open Source Educational Resources for Machine Learning and Data Science

authored by

Ludwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl

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.

Organisation(s)

Machine Learning Section

External Organisation(s)

Ludwig-Maximilians-Universität München (LMU)

Type

Conference contribution

No. of pages

6

Publication date

28.07.2022

Publication status

E-pub ahead of print

Peer reviewed

Yes

Electronic version(s)

https://arxiv.org/abs/2107.14330 (Access: Open)