Data Cleaning In Mathematics Education: Teaching Statistical Methods Of Outlier Detection

Abstract

We live in a world increasingly shaped by data and algorithms. Everyday decisions are influenced by data science processes without one noticing it directly (Grzymek & Puntschuh, 2019). Consequently, it is essential to educate future generations in data science and to promote data literacy. Students should be taught to engage with data and data-driven algorithms in a reflective and informed manner from an early age (Schüller et al., 2021). This provides the motivation to develop teaching and learning material as part of a design research project with focusing on mathematical aspects of data processing. One of the first steps in data processes is data cleaning and data preprocessing. Data cleaning plays an important role in data science procedures and can have direct impacts on insights, predictions and decisions based on data science procedures.

Sarah Schönbrodt

Assistant professor @ University of Salzburg, Austria

Research in Mathematics Education and AI Education