Personalizing the use of educational mathematics applets to fit learners' characteristics poses a great challenge. The present study adopted a unique approach by comparing personalization processes implemented by a machine to those implemented by a human teacher. Given the different affordances--the machine's access to historical log file data, computation and automatization, and the teacher's mathematical knowledge, pedagogical approach and personal acquaintance--the study hypothesized that different considerations would lead to different personalization and learning outcomes. Mathematical applets were assigned to 77 students in the 4th and 5th grades either by an expert teacher or by an algorithm. The assignment took place in a controlled setting in which the teacher was unaware which students were eventually assigned according to her recommendations. The teacher and the machine each recommended an ordered sequence of ten applets per student. The findings suggest that the teacher-assigned group outperformed the machine-assigned group among 5th graders when the applets were sequenced in increasing order of difficulty. In the 4th grade, only the machine recommended a sequence of increasing difficulty and both groups achieved equal performance. The study concludes that in the case of data-driven personalization processes, machines and teachers should learn from each other's affordances and considerations.