The research on student dropout demonstrates that there is no consensus on its definition and scope. Although there is an expanding collection of research on the topic, student dropout remains a significant issue, characterized by numerous uncertainties and ambiguous aspects. The primary aim of this investigation is to assess the research trends of student dropout within the distance education literature by employing data mining and analytic approaches. To identify these patterns, a total of 164 publications were examined by applying text mining and social network analysis. The study revealed some intriguing facts, such as the misinterpretation of the term “dropout” in different settings and the inadequacy of nonhuman analytics to explain the phenomenon, and promising implications on how to lessen dropout rates in open and distance learning environments. Based on the findings of the study, this article proposes possible directions for future research, including the need to provide a precise definition of the term “dropout” in the context of distance learning, to develop ethical principles, policies, and frameworks for the use of algorithmic approaches to predict student dropout, and finally, to adopt a human-centered approach aimed at fostering learners’ motivation, satisfaction, and independence to reduce the rate of dropout in distance education.