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Rigorous and interactive class discussions that support students to engage inhigh-level thinking and reasoning are essential to learning and are a centralcomponent of most teaching interventions. However, formally assessingdiscussion quality 'at scale' is expensive and infeasible for most researchers.In this work, we experimented with various modern natural language processing(NLP) techniques to automatically generate rubric scores for individualdimensions of classroom text discussion quality. Specifically, we worked on adataset of 90 classroom discussion transcripts consisting of over 18000 turnsannotated with fine-grained Analyzing Teaching Moves (ATM) codes and focused onfour Instructional Quality Assessment (IQA) rubrics. Despite the limited amountof data, our work shows encouraging results in some of the rubrics whilesuggesting that there is room for improvement in the others. We also found thatcertain NLP approaches work better for certain rubrics.

Utilizing Natural Language Processing for Automated Assessment of Classroom Discussion