Testing The Validity of Value-Added Measures of Educational Progress With Genetic Data

Autor/inn/en	Morris, Tim T.; Davies, Neil M.; Dorling, Danny; Richmond, Rebecca C.; Smith, George Davey
Titel	Testing The Validity of Value-Added Measures of Educational Progress With Genetic Data
Quelle	In: British Educational Research Journal, 44 (2018) 5, S.725-747 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Morris, Tim T.) ORCID (Davies, Neil M.) ORCID (Richmond, Rebecca C.) ORCID (Smith, George Davey)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0141-1926
DOI	10.1002/berj.3466
Schlagwörter	Validity; Value Added Models; Genetics; Foreign Countries; Teacher Effectiveness; School Effectiveness; Teacher Attitudes; Teacher Salaries; Academic Achievement; Correlation; Context Effect; Academic Ability; Case Studies; Cognitive Ability; Bias; Data Analysis; Secondary School Students; Secondary School Teachers; United Kingdom + Suchen Sie Ihr Suchwort? Gültigkeit; Humangenetik; Ausland; Effectiveness of teaching; Instructional effectiveness; Lehrerleistung; Unterrichtserfolg; Schuleffizienz; Lehrerverhalten; Lehrerbesoldung; Lehrervergütung; Schulleistung; Korrelation; Case study; Fallstudie; Case Study; Denkfähigkeit; Auswertung; Sekundarschüler; Großbritannien
Abstract	Value-added measures of educational progress have been used by education researchers and policy-makers to assess the performance of teachers and schools, contributing to performance-related pay and position in school league tables. They are designed to control for all underlying differences between pupils and should therefore provide unbiased measures of school and teacher influence on pupil progress, however, their effectiveness has been questioned. We exploit genetic data from a UK birth cohort to investigate how successfully value-added measures control for genetic differences between pupils. We use raw value-added, contextual value-added (which additionally controls for background characteristics) and teacher-reported value-added measures built from data at ages 11, 14 and 16. Sample sizes for analyses range from 4,600 to 6,518. Our findings demonstrate that genetic differences between pupils explain little variation in raw value-added measures but explain up to 20% of the variation in contextual value-added measures (95% CI = 6.06% to 35.71%). Value-added measures built from teacher-rated ability have a greater proportion of variance explained by genetic differences between pupils, with 36.3% of their cross-sectional variation being statistically accounted for by genetics (95% CI = 22.8% to 49.8%). By contrast, a far greater proportion of variance is explained by genetic differences for raw test scores at each age of at least 47.3% (95% CI: 35.9 to 58.7). These findings provide evidence that value-added measures of educational progress can be influenced by genetic differences between pupils, and therefore may provide a biased measure of school and teacher performance. We include a glossary of genetic terms for educational researchers interested in the use of genetic data in educational research. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01