Non-experimental value-added models have been shown to yield forecast-unbiased estimates of teacher and school effects. To investigate, we propose a dynamic state-space model of knowledge accumulation, in which test scores are imperfect measures of knowledge, and students receive temporary and persistent shocks to their stock of knowledge each…

We investigate the predictive validity of teacher licensure tests using data from the Massachusetts Tests for Educator Licensure (MTEL). MTEL scores predict teachers' in-service performance ratings and contributions to student test scores (i.e., value added). We then explore whether these relationships vary for teacher candidates of color.…

We examine the relationship between absenteeism and achievement since the onset of COVID-19. Applying first-differences models to North Carolina administrative data, we estimate that each absence was associated with a 0.0032 standard deviation (SD) decline in math achievement in 2022-23. As students averaged 3.3 more absences in 2022-23 than…

Recognizing the limitations of the existing frameworks and models applied to estimate the learning loss caused by COVID-19 across the world by different studies, and the urgent need for estimating such loss at the different education levels, this paper, employs the Education Value Chain Analysis ( EVCA) approach as a new paradigm, attempts to…