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Using Multimodal Classroom Data to Measure Student Competencies for Upward Mobility

  • This project uses AI-supported analysis of recorded classroom interactions to uncover the student skills embedded in real‑time instructional exchanges and assess how well these competencies—spanning both core mathematical reasoning tied to upward mobility and more durable skills such as agency, vocabulary use, and self‑management where disparities often emerge—serve as early precursors of long‑run mobility. Drawing on classroom audio recordings, surveys, and linked administrative data from the Midwest and South, the team applies natural language processing and machine learning methods to develop scalable measures that capture how students demonstrate these competencies during middle school math lessons and then links them to key high school outcomes such as attendance, course taking, and GPA. Led by a leading scholar of classroom interaction and mathematics education and supported by established partnerships with districts and education technology firms, the project is positioned to produce scalable tools that help teachers identify and strengthen mobility‑relevant skills in real time.

    Data Sources 

    • Data: 7th- and 8th-grade math classroom recordings, survey data, administrative data from three districts in North Carolina, Iowa, and Florida
    • Early Mobility Outcomes: Attendance, mathematics course-taking patterns, and grades

     

    Research Team

     

    Cohort 2: Cognitive, Social, and Emotional Skills | Measure Development With Early Mobility Outcomes

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