Home  | Publications | BSC+22

Developing Open Source Educational Resources for Machine Learning and Data Science

MCML Authors

Link to Profile David Rügamer PI Matchmaking

David Rügamer

Prof. Dr.

Principal Investigator

Statistics, Data Science and Machine Learning

Link to Profile Fabian Scheipl PI Matchmaking

Fabian Scheipl

PD Dr.

Principal Investigator

Functional Data Analysis

Link to Profile Bernd Bischl PI Matchmaking

Bernd Bischl

Prof. Dr.

Director

Statistical Learning and Data Science

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.

inproceedings

Teaching Machine Learning and Artificial Intelligence Workshop @ECML-PKDD 2022

3rd Teaching Machine Learning and Artificial Intelligence Workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Grenoble, France, Sep 19-23, 2022.

Authors

L. Bothmann • S. Strickroth • G. Casalicchio • D. Rügamer • M. Lindauer • F. Scheipl • B. Bischl

Links

URL

Research Area

 A1 | Statistical Foundations & Explainability

BibTeXKey: BSC+22

Back to Top