There is an increasing emphasis, especially in STEM areas, on students’ abilities to create explanatory descriptions. Holistic, overall evaluations of explanations can be performed relatively easily with shallow language processing by humans or computers. However, this provides little information about an essential element of explanation quality: the structure of the explanation, i.e., how it connects causes to effects. The difficulty of providing feedback on explanation structure can lead teachers to either avoid giving this type of assignment or to provide only shallow feedback on them. Using machine learning techniques, we have developed successful computational models for analyzing explanatory essays. A major cost of developing such models is the time and effort required for human annotation of the essays. As part of a large project studying students’ reading processes, we have collected a large number of explanatory essays and thoroughly annotated them. Then we used the annotated essays to train our machine learning models. In this paper, we focus on how to get the best payoff from the expensive annotation process within such an educational context and we evaluate a method called Active Learning.