UNSUPERVISED MACHINE LEARNING AS LEARNING CONTENT IN LOWER SECONDARY SCHOOL

Katharina Bata, Martin Frank

Keywords: Machine Learning, Unsupervised Learning, Design Research, Jupyter Notebooks

Short Summary: This paper presents the preparation of cluster analysis as learning content to familiarise secondary school students in Germany with unsupervised machine learning. The mathematical foundations of clustering methods, such as k-Means and DBSCAN, are analysed following a subject-specific didactic analysis in order to adapt them to students' prior knowledge and curricular content. Based on this analysis, design principles are established to guide the creation of a workshop that fits the circumstances of the learning situation and supports the stated learning objectives

Topic Area: Learning Materials

Presenting Author: Katharina Bata

Presentation Type: Short Presentation (ca. 20 min)

Submission Number: 12