A Deep Transfer Learning Approach to Modeling Teacher Discourse in the Classroom

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 open-vocabulary 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.