With the increasing influence of Machine Learning (ML) on our lives, the need for education on this topic is growing. A key component of education is assessment and improving this aspect could lead to better student learning performance. This study aimed to investigate the influence of different assessment methods on students' learning performance in k-means clustering. Two different assessment methods were used: a closed-book problem-based assignment and an open-book short answer exam. Participants were notified of their assessment method, after which they were instructed to watch a video lecture and take the assessment. Results show a significantly improved learning gain when using the open-book assessment, where learning gain was defined as the difference in score between the pre- and post-test. Between these two methods the open-book assessment is therefore favourable. However, future research is needed to develop a validated concept inventory for k-means clustering and identify other possible assessment techniques.