Using AI to Evaluate Pre-K Instruction
Summary
High-quality early learning programs boost children's kindergarten readiness and effects can last into adulthood. However, defining and measuring quality is difficult and expensive. Classroom video observations are a classic method used by education researchers to understand how the instructional environment relates to student learning. Although videos provide rich context on what occurs in the classroom, they require a significant amount of human labor and time to accurately code, which can lead to delays in acquiring information. Moreover, early education researchers have found that constructs from standard protocols do not always predict children's outcomes, indicating the need for improved construct measurement.
YPL is working with colleagues in U-M's computer science and education departments to develop an innovative, dynamic AI that uses classroom videos to provide actionable data on Pre-K instruction and the classroom environment. YPL researchers are contributing education content expertise on coding methods and classroom observations. The goal is to develop a flexible tool to allow researchers to ask novel measurement questions about the classroom environment without incurring excess costs or labor demands.
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