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David S. Woo
Human-, GRM-, and LLM-Generated School-Improvement Leadership Scores from Missouri: Structural Bias and Concurrent and Predictive Validity
Seth B. Hunter, David S. Woo, Katherine M. Bowser, Syeda Sabrina Akter, Thomas Hairston, Eter Mjavanadze, Antonios Anastasopoulos, Sarah Hairston.Topics: MethodsHead or assistant principal evaluation systems are widely deployed but rarely examined for validity, and AI scoring tools have entered K–12 workflows ahead of the evidence. We examine human supervisor ratings, Graded Response Model (GRM) rescaling, and large language model (LLM) scoring of 1,385… more →
Ready to Lead on Day One: Predicting Novice Principal Effectiveness with Information Available at Time of Hire
Topics: Teacher and Leader DevelopmentTags: Leadership, PrincipalsHigh rates of principal turnover nationally mean that school districts constantly are called on to recruit and select new principals. The importance of a school’s principal makes choosing candidates who will be effective paramount, yet we have little evidence linking information known to school… more →
Assistant Principal Mobility and its Relationship with Principal Turnover
Topics: Teacher and Leader DevelopmentTags: Leadership, PrincipalsAssistant principals are important education personnel, both as essential members of school leadership teams and apprentice principals. However, empirical evidence on their career outcomes remains scarce. Using statewide administrative data from Tennessee and Missouri, we provide the first… more →