Effectively grouping learners in an online environment is a highly useful task. However, datasets used in this task often have large numbers of attributes of disparate types and different scales, which traditional clustering approaches cannot handle effectively. Here, a unique dissimilarity measure based on the random forest, which handles the stated drawbacks of more traditional clustering approaches, is presented. Additionally, a rule-based method is proposed for interpreting the resulting learner segmentations. The approach was implemented on a real dataset of users of the "Career"WISE online educational environment, designed to provide resilience training for women STEM doctoral students, and was shown to find stable and meaningful groups of users.