Active learning involves selecting unlabeled data items to label in order to best improve an existing classifier. In most applications, batch mode active learning, where a set of items is picked all at once to be labeled and then used to re-train the classifier, is most feasible because it does not require the model to be re-trained after each individual selection and makes most efficient use of human labor for annotation. In this work, we explore using meta-learning to learn an active learning algorithm that selects the best set of unlabeled items to label given a classifier trained on a small training set. Our experiments show that our learned active learning algorithm is able to construct labeled sets that improve a classifier better than commonly used heuristics.