Toward a Framework for Learner Segmentation

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, the use of a dissimilarity measure based on the random forest, which handles the stated drawbacks of more traditional clustering approaches, is presented for this task. Additionally, the application of a rule-based method is proposed for interpreting the resulting learner segmentations. The approach was implemented on a real dataset of users of the CareerWISE online educational environment, designed to provide resilience training for women STEM doctoral students, and was shown to find stable and meaningful groups of users.