Mean-over-time produced competitive results on many prompts, but contrary to<cite> Taghipour and Ng (2016)</cite> , bidirectional LSTMS and attention produced some of the best results, which is consistent with results for neural models on other text classification tasks (e.g., Longpre et al. (2016) ). Research is needed to explain these emerging differences in effective neural architectures for AES vs. SAS, including model-specific factors such as the interaction of an LSTM's integration of features over time and the redundancy of predictive signals in essays vs. short answers, along with data-specific factors such as the consistency of human scoring, the demands of different rubrics, and the homogeneity or diversity of prompts in each setting.