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This work investigates how tutoring discourse interacts with students’ proximal knowledge to explain and predict students’ learning outcomes. Our work is conducted in the context of high-dosage human tutoring where 9th-grade students attended small group tutorials and individually practiced problems on an Intelligent Tutoring System (ITS). We analyzed whether tutors’ talk moves and students’ performance on the ITS predicted scores on math learning assessments. We trained Random Forest Classifiers (RFCs) to distinguish high and low assessment scores based on tutor talk moves, student’s ITS performance metrics, and their combination. A decision tree was extracted from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity among the two feature sources. Specifically, the best decision tree emerged from combining the tutor talk moves that encouraged rigorous thinking and students’ ITS mastery. In essence, tutor talk that encouraged mathematical reasoning predicted achievement for students who demonstrated high mastery on the ITS, whereas tutors’ revoicing of students’ mathematical ideas and contributions was predictive for students with low ITS mastery. Implications for practice are discussed. 

We present the Teacher Talk Tool, which automatically analyzes classroom audio and provides formative feedback on key aspects of teachers’ classroom discourse (e.g., use of open-ended questions). The tool was designed to promote teacher learning by focusing attention and sense-making on their discourse. We conducted a feedback- response study where five English & Language Art teachers used the Teacher Talk Tool in eight classroom sessions. Teachers completed repeated-measure surveys and semi-structured interviews providing quantitative and qualitative evidence of feedback response. Results indicated that the majority of automated feedback was perceived to be accurate and prompted a high degree of reflection, focusing teachers’ attention on the measured talk constructs. This feedback also led teachers to engage in a process of sense-making, linking the measured talk features to classroom processes and contexts. However, evidence of feedback uptake was more limited. Overall, results contribute to the nascent literature on the efficacy of automated feedback on instructional practice. 

In this study, we report results from a novel coding of the Measures of Effective Teaching (MET) Study data that offers evidence on a set of teacher discourse measures in the domain of teacher support including: public praise vs. admonishment, autonomy support vs. controlling language, strategy suggestion vs. lack thereof, and discourse supporting (vs.undermining) learning mindsets. Novel coding of these constructs is paired with extant measures of instruction and achievement in the MET data. Several of the newly coded discourse measures have promising features, including high lesson- and teacher-level variability, and convergent and discriminant validity with existing protocols. We also report possible associations with change in achievement over two years.