The current study explored the relationship between learner profiles and the nature of their interaction with content in massive, open, and online learning environments. The research was conducted on the Anadolu University Open Education System, and data from 597,164 learners enrolled in 86 different degree programs were analyzed by unsupervised machine learning methods. Cluster analysis was used to identify learner profile groups and association rules were applied to identify learner-content interaction patterns. As a result of the analyses, five clusters were obtained, and it was determined that the attribute with the highest discrimination in determining the clusters was the learners' semester grade point average. The clusters were named according to learner-content interactions and the learners' semester grade point average. Analysis of the association rules revealed that various learner-content interactions emerged in the context of profile groups.