Exploring students’ constructions of data-based decision trees after an introductory teaching unit on machine learning

Abstract This study explores how high school students construct decision trees using data cards and the software CODAP (codap.concord.org) in interviews after attending a teaching unit. We conceptualized data-based decision tree construction using nine key aspects that we intended to teach, tested variations of two design elements in teaching, and analyzed the interviews qualitatively to compare student behavior to intended outcomes. We found high alignment to intentions but also deviations in data activities and informal or context-based rather than data-based reasoning. The design element of context-free (blinded) data seems to enhance data-based reasoning, while the design element of data card use showed diagnostic potential.