Modeling Classroom Discourse: Do Models That Predict Dialogic Instruction Properties Generalize across Populations?

It has previously been shown that the effective use of dialogic instruction has a positive impact on student achievement. In this study, we investigate whether linguistic features used to classify properties of classroom discourse generalize across different subpopulations. Results showed that the machine learned models perform equally well when trained and validated on different subpopulations. Correlation-Based Feature Subset evaluation revealed an inclusion relationship between different subsets in terms of their most predictive features. [For complete proceedings, see ED560503.]