Observing Personalizations in Learning: Identifying Heterogeneous Treatment Effects Using Causal Trees

The incorporation of computer-based platforms in the classroom has introduced the ability to conduct numerous randomized control trials at scale with student-level randomization. Such systems are able to collect vast amounts of data on each student while completing work in the classroom and at home. It is often the case, however, that the effects of these trials are reported across all students, ignoring the potential for personalized learning. Personalized learning, or the observation of heterogeneous treatment effects, considers that the effects of a studied learning intervention may differ for individual students; while an intervention may work well for low-performing students, for example, it may have no effect for higher performing students. Personalized learning can lead to better instructional practices that maximizes the learning benefits for each individual student, and with the use of computer-based platforms, such individualized instruction is made feasible at large scales. In this work we use a causal decision tree to observe treatment effects in 9 experiments run in the ASSISTments online learning platform.