Abstract
The purpose of this study is to develop and apply causal machine learning methods to the study of teacher effectiveness in STEM education. The fundamental question examined is the extent to which we can identify and explain profiles, pathways and practices (e.g., who teachers are, what teachers know, what teachers believe, perceive and experience, what teachers do) that produce student learning and how these profiles and practices vary across STEM education contexts?
Details
Presentation Type
Theme
Science, Mathematics and Technology Learning
KEYWORDS
Machine Learning, Teacher Effects, Math, Causal Inference, Superlearners, Double Robust
