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Teachers, like everyone else, need objective reliable feedback in order to improve their effectiveness. However, developing a system for automated teacher feedback entails many decisions regarding data collection procedures, automated analysis, and presentation of feedback for reflection. We address the latter two questions by comparing two different machine learning approaches to automatically model seven features of teacher discourse (e.g., use of questions, elaborated evaluations). We compared a traditional openvocabulary approach using n-grams and Random Forest classifiers with a state-of-the-art deep transfer learning approach for natural language processing (BERT). We found a tradeoff between data quantity and accuracy, where deep models had an advantage on larger datasets, but not for smaller datasets, particularly for variables with low incidence rates. We also compared the models based on the level of feedback granularity: utterance-level (e.g., whether an utterance is a question or a statement), class session-level proportions by averaging across utterances (e.g., question incidence score of 48%), and session-level ordinal feedback based on pre-determined thresholds (e.g., question asking score is medium [vs. low or high]) and found that BERT generally provided more accurate feedback at all levels of granularity. Thus, BERT appears to be the most viable approach to providing automatic feedback on teacher discourse provided there is sufficient data to fine tune the model.

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Rail switches are critical infrastructure components of a railroad network, that must maintain high-levels of reliable operation. Given the vast number and variety of switches that can exist across a rail network, there is an immediate need for robust automated methods of detecting switch degradations and failures without expensive add-on equipment. In this work, we explore two recent machine learning frameworks for classifying various switch degradation indicators: (1) a featureless recurrent neural network called a Long Short-Term Memory (LSTM) architecture, and (2), the Deep Wavelet Scattering Transform (DWST), which produces features that are locally time invariant and stable to time-warping deformations. We describe both methods as they apply to rail switch monitoring and demonstrate their feasibility on a dataset captured under the service conditions by Alstom Corporation. For multiple categories of degradation types, the baseline models consistently achieve near-perfect accuracies and are competitive with the manual analysis conducted by human switch-maintenance experts.

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