Developing Open Source Educational Resources for Machine Learning and Data Science

TL;DR

This paper emphasizes the importance of open source educational resources in machine learning and data science to promote equitable access, discussing requirements, challenges, and practical experiences in developing and implementing OSER.

Contribution

It highlights the specific needs for open source educational resources in ML and DS and discusses collaborative development, challenges, and practical applications in university education.

Findings

OSER can enhance blended learning in ML and DS.

Collaborative development of OSER faces challenges like credit and certification.

Practical experiences demonstrate feasibility and benefits of OSER.

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.