Teachers' Ratings of Multilingual Learners' Science Skills

Seeing Skills Beyond Standardized Test Performance

Bibliographic Data

ID21392888
AuthorsF Chris Curran (0000-0001-5680-1446, University of Florida Gainesville Florida USA, corresponding author), Lelydeyvis Boza (0009-0009-5233-1307, University of Florida Gainesville Florida USA), Katharine Taylor Harris‐Walls (Augusta University Augusta Georgia USA), Mark B Pacheco (0000-0002-9706-2704, University of Florida Gainesville Florida USA), Amber Deig (0000-0003-4181-0707, University of Southern Mississippi Hattiesburg Mississippi USA), Tiffany S Tan (0009-0001-5317-5651, Learning Policy Institute Palo Alto California USA)
Year2026
Publication date2026-04-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueScience Education (JOURNAL)
Journal identifiersISSN: 0036-8326 • E-ISSN: 1098-237X
PublisherWiley (PUBLISHER • GB)
DOI10.1002/sce.70075
OpenAlexW7156287415
LanguageEN
References cited113

Though prior research has explored test score differences in science performance between multilingual learners (MLs) and non‐MLs, most assessments of multilingual learners' (MLs) science learning consist not of standardized test scores but of routine formative and summative assessments provided by teachers who work with them day‐to‐day. However, teachers' assessments of ML students' science skills may be uniquely different from other students. This study leveraged nationally representative data from the Early Childhood Longitudinal Study Kindergarten Class of 2011 (ECLS‐K:2011) to document teacher ratings of science skills for MLs compared to non‐MLs as well as the relationship between teacher ratings and ML status after accounting for standardized science achievement. The findings demonstrate that elementary school teachers rate MLs somewhat lower in science skills than non‐MLs in unadjusted models in the earliest grades of elementary school. After accounting for standardized test performance, however, teachers in the lowest grades rate ML students' science skills higher on average than non‐MLs conditional on test scores. The analysis explores variation across ML subgroups and informs policy and practice

Formative assessment · Science education · Standardized test · Summative assessment · Test (biology) · Variation (astronomy) · EFL/ESL Teaching and Learning · Multilingual Education and Policy · Second Language Learning and Teaching