Pandemic Impact on School Performance

Autor/inn/en	Kim, Dong-In; Julian, Marc; Boughton, Keith; Phenow, Aurore
Titel	Pandemic Impact on School Performance
Quelle	(2022), (16 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Pandemics; COVID-19; Academic Achievement; English; Language Arts; Mathematics Achievement; Elementary School Students; Middle School Students; Scores; Prediction; Achievement Tests; School Districts; Educational Change; Models; Goodness of Fit; Comparative Analysis; Correlation; Effect Size; School Effectiveness; Regression (Statistics); Student Characteristics; Institutional Characteristics + Suchen Sie Ihr Suchwort? Schulleistung; English language; Englisch; Sprachkultur; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin; Vorhersage; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; School district; Schulbezirk; Bildungsreform; Analogiemodell; Korrelation; Schuleffizienz; Regression; Regressionsanalyse
Abstract	Pandemic-related policies are typically developed by districts and translated to all schools for implementation. Understanding the degree to which the pandemic impacted school-level performance would provide additional perspective for researchers looking to help district and school officials move forward. The main purpose of this study is to evaluate the pandemic impact at the school level using three different regression approaches. Large scale assessment datasets in ELA and Mathematics grades 5-8 were used in this study. The reference group data included test-takers from the Spring 2017 and 2019 administrations, which were not impacted by the pandemic. The study group data was test-takers from the Spring 2019 and 2021 administrations. Residual values were estimated by subtracting predicted 2021 scores with the regression coefficients of 2017 to 2019 from observed 2021 scale scores, with the residuals being considered as the pandemic impact. A school residual was calculated by averaging the residuals of the students in the schools. There were three study questions. The first question was about the stability of school effects on students' performances across 2019 and 2021. For both 2019 and 2021, there were some school effects on students' performance. For ELA, ICC values for spring 2021 were lower than those for spring 2019. The second question was regarding which regression method produced the best-fit model. As expected, the regression model with the nested school design produced the best fit. The third question was how to flag schools impacted by the pandemic. In this study, the two flagging criteria utilized were the SD and effect size flags. Because the SD flag compares school residuals to state residuals, it can be considered a relative criterion. These effect size flags compared expected performance under a normal environment and the 2021 observed performance of a school and thus can be regarded as an absolute criterion. (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01