The first wave of the COVID-19 pandemic disrupted regular classes in spring 2020.Temporary school closures supposedly led to a considerable learning loss, particularly for low-achieving students. Schools in Baden-Württemberg, Germany, were closed for two months. Although distance learning was implemented, students spent less time learning.Additionally, teachers were faced with organizational and technological challenges of remote learning environments. The present study investigates the competencies of fifthgraders, using large-scale assessment results in reading and mathematics from annual mandatory tests in September (each n > 80,000). In line with studies from other countries, competence scores were slightly lower in 2020 compared with the three previous years (-0.07 standard deviations for reading comprehension, -0.09 for operations, and -0.03 for numbers). Low-achieving readers managed to attain pre-pandemic competence levels.Regarding mathematics competencies, low-achieving students seem to have a learning backlog that deserves attention in future education. School characteristic such as the average socio-cultural capital and the proportion of students with migration background played a minor role in mediating the schools' learning loss. Still, lower socio-cultural capital was positively associated with larger learning loss in mathematics.

Mechanisms causing item nonresponses in large-scale assessments are often said to be nonignorable. Parameter estimates can be biased if nonignorable missing data mechanisms are not adequately modeled. In trend analyses, it is plausible for the missing data mechanism and the percentage of missing values to change over time. In this article, we investigated (a) the extent to which the missing data mechanism and the percentage of missing values changed over time in real large-scale assessment data, (b) how different approaches for dealing with missing data performed under such conditions, and (c) the practical implications for trend estimates. These issues are highly relevant because the conclusions hold for all kinds of group mean differences in large-scale assessments. In a reanalysis of PISA (Programme for International Student Assessment) data from 35 OECD countries, we found that missing data mechanisms and numbers of missing values varied considerably across time points, countries, and domains. In a simulation study, we generated data in which we allowed the missing data mechanism and the amount of missing data to change over time. We showed that the trend estimates were biased if differences in the missing-data mechanisms were not taken into account, in our case, when omissions were scored as wrong, when omissions were ignored, or when model-based approaches assuming a constant missing data mechanism over time were used. The results suggest that the most accurate estimates can be obtained from the application of multiple group models for nonignorable missing values when the amounts of missing data and the missing data mechanisms changed over time. In an empirical example, we furthermore showed that the large decline in PISA reading literacy in Ireland in 2009 was reduced when we estimated trends using missing data treatments that accounted for changes in missing data mechanisms.

School closures during the first wave of the COVID-19 pandemic in early 2020 were associated with attenuated learning gains compared to pre-pandemic years. In Germany, two further pandemic waves led to school closures and periods of remote learning between December 2020 and May 2021. The present study investigates the academic achievement of all incoming fifth-graders in the federal state of Baden-Württemberg before and during the pandemic, using educational large-scale assessment results in reading and mathematics. Each year, the assessments took place at the beginning of the school year in September (each n > 84,000). The comparison of average competence levels in 2021 with pre-pandemic years (2017–2019) indicates that the downward trend that was observed after the first pandemic wave in 2020 came to a halt in the domain of reading and continued at a slower rate in the domain of mathematical operations. Achievements in the mathematical domain of numbers even rebounded to pre-pandemic levels. Longer periods of school closures were associated with larger learning losses. Additional analyses showed larger learning losses for the group of low-achieving students and for schools with less socio-cultural capital. The partial rebound of learning outcomes suggests that most teachers and students successfully adapted to the pandemic situation in 2021. Still, disadvantaged student groups are at high risk of further substantial learning losses due to school closures that may negatively affect their future education. Accordingly, disadvantaged student groups in particular should receive additional support to compensate for the loss of learning opportunities in the classroom.

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