The increasing demand for machine learning expertise calls for effective teaching methods for university-level courses. This research compares static versus interactive teaching methods in the context of machine learning, with the latter focusing on the student engaging more with the material. Specifically, this study investigates the impact of interactive visualisations on students' understanding of receiver operating characteristics (ROC) curve analysis, a critical concept in evaluating machine learning algorithms. Traditional static teaching methods often fall short of conveying complex ideas like ROC curves, which are pivotal in various fields, including medicine and psychology. This research also compares the efficacy of interactive versus static visualisations in enhancing student motivation. Twenty first-year computer science students from Delft University of Technology participated in the experiment and were randomly assigned to control (static visualisation) and experimental (interactive visualisation) groups. The results of the experiment were determined by analyzing the pre- and post-test scores, along with surveys measuring motivation. These indicate significant improvements in understanding for both groups, with a greater gain observed in the experimental group. This suggests that interactive visualisations may offer a superior approach to teaching complex machine learning concepts, but the experiment conducted in the study does not show a statistically significant difference between the static and interactive visualisations. The research also compares the student's motivation after completing an instructional material focused on the ROC, but the interactive visualisations did not provide better results. The study underscores the potential of interactive teaching tools to enhance educational outcomes in machine learning and highlights the need for further research into interactive methods for the teaching of machine learning.