Developing Open Source Educational Resources for Machine Learning and Data Science
This work addresses educational equity for learners in machine learning and data science by proposing open-source resources, but it is incremental as it builds on existing open educational resource concepts without introducing new technical methods.
The paper tackles the need for accessible education in machine learning and data science by advocating for Open Source Educational Resources (OSER), which involve making source files publicly available to reduce barriers and promote equity, and it outlines collaborative development approaches, challenges, and initial solutions based on experiences in university settings.
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.