Using data from students in Grades 3–8 in school years 2018–2019 (N = 1,871) and 2021–2022 (N = 1,740), we examined the strength of fall math screening using Measures of Academic Progress (MAP) for predicting end-of-year state math assessment performance levels and whether this prediction varied across English language proficiency (ELP). In addition, we examined whether differential predictive strength varied across screenings using English or Spanish MAP math (2018–2019 only). MAP math was similarly predictive across the ELP continuum (odds ratios between 6 and 15; log-odds between 1.5 and 2.5). However, in 2018–2019, there was more variation in screening prediction across ELP for students assessed using the English MAP math compared to Spanish MAP math. For 2021–2022, there was little evidence of differential screening prediction across ELP. Our findings suggest that MAP math is moderately predictive across ELP and school years. We discuss limitations, future directions, and implications for research and practice.
AlbersC. A.MartinezR. (2015). Promoting success with English language learners: Best practices for RTI. Guilford Press.
2.
American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. American Educational Research Association.
3.
BarrettC. A.JohnsonL. J.TruckenmillerA. J.VanDerHeydenA. M. (2024). Comparing the cost-accuracy ratios of multiple approaches to reading screening in elementary schools. Remedial and Special Education, 45(2), 71–84. https://doi.org/10.1177/07419325231190809
4.
BraffordT.ClarkeB.GerstenR. M.SmolkowskiK.SutherlandM.DimoniJ.FainsteinD. (2023). Exploring an early numeracy screening measure for English learners in primary grades. Early Childhood Research Quarterly, 63(2), 278–287. https://doi.org/10.1016/j.ecresq.2022.12.007
5.
BuonoS.JangE. E. (2021). The effect of linguistic factors on assessment of English language learners’ mathematical ability: A differential item functioning analysis. Educational Assessment, 26(2), 125–144. https://doi.org/10.1080/10627197.2020.1858783
6.
BürknerP.-C. (2017). brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. https://doi.org/10.18637/jss.v080.i01
7.
BürknerP.-C. (2024). Handle missing values with brms. https://cran.r-project.org/web/packages/brms/vignettes/brms_missings.html
ChoE.FuchsL. S.SeethalerP. M.FuchsD.ComptonD. L. (2020). Dynamic assessment for identifying Spanish-speaking English learners’ risk for mathematics disabilities: Does language of administration matter?Journal of Learning Disabilities, 53(5), 380–398. https://doi.org/10.1177/0022219419898887
10.
de LeónS. C.JiménezJ. E.GarcíaE.GutiérrezN.GilV. (2021). Universal screening in mathematics for Spanish students in first grade. Learning Disability Quarterly, 44(2), 123–135. https://doi.org/10.1177/0731948720903273
11.
DepaoliS.WinterS. D.VisserM. (2020). The importance of prior sensitivity analysis in Bayesian statistics: Demonstrations using an interactive Shiny app. Frontiers in Psychology, 11(2020), 608045. https://doi.org/10.3389/fpsyg.2020.608045
12.
EdwardsA. A.van DijkW.WhiteC. M.SchatschneiderC. (2022). Screening screeners: Calculating classification indices using correlations and cut-points. Annals of Dyslexia, 72(3), 445–460. https://doi.org/10.1007/s11881-022-00261-5
FuchsL. S.SeethalerP. M.FuchsD.EspinasD. (2023). Severe pandemic learning loss and the promise of remotely delivered intervention in students with comorbid reading and mathematics learning difficulty. Journal of Learning Disabilities, 56(4), 278–294. https://doi.org/10.1177/00222194231170313
15.
GelmanA.HillJ. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
HallG. J.DoyleE. (2024a). Back to Bayes-ics: Improving universal screening decisions by quantifying uncertainty. EdArXiv. https://doi.org/10.35542/osf.io/rkzna
18.
HallG. J.DoyleE. (2024b). Learning from learning loss: Bayesian updating in academic universal screening during learning disruptions. EdArXiv. https://doi.org/10.35542/osf.io/ktc3e
19.
HallG. J.MarkhamM. A.McMackinM.MooreE. C.AlbersC. A. (2022). Predicting interim assessment outcomes among elementary-aged English learners using mathematics computation, oral reading fluency, and English proficiency levels. School Psychology Review, 51(4), 498–516. https://doi.org/10.1080/2372966X.2022.2041211
20.
HallG. J.SwanlundL. (2023). Differential nonlinear relations of language proficiencies to reading and math achievement in Spanish or English. School Psychology, 38(5), 294–307. https://doi.org/10.1037/spq0000545
21.
HarrisB.KulkarniT.SullivanA. L. (2024). Review of state policies and guidance for the identification of culturally and linguistically minoritized students with specific learning disabilities. Journal of Learning Disabilities, 57(1), 3–15. https://doi.org/10.1177/00222194231158070
22.
HospM. K.HospJ. L.HowellK. W. (2016). The ABCs of CBM: A practical guide to curriculum-based measurement (2nd ed.). Guilford Press.
23.
KaplanD. (2023). Bayesian statistics for the social sciences (2nd ed.). Guilford Press.
24.
Keller-MargulisM. A.MattaM.Landry PierceL.ZopattiK.ReidE. K.SchandingG. T. (2022). A comparison of reading screeners in kindergarten: The Texas Primary Reading Inventory and Acadience Reading with English learners and monolingual English speakers. Assessment for Effective Intervention, 48(3), 127–137. https://doi.org/10.1177/15345084221133559
25.
KiefferM. J.ThompsonK. D. (2018). Hidden progress of multilingual students on NAEP. Educational Researcher, 47(6), 391–398. https://doi.org/10.3102/0013189X18777740
26.
KlingbeilD. A.OsmanD. J.CarberryC. K.CarriganJ. E.Berry-CorieK. (2021a). Predicting performance on a statewide reading achievement test in Spanish with aimswebPlus Spanish. Psychology in the Schools, 58(9), 1768–1781. https://doi.org/10.1002/pits.22535
27.
KlingbeilD. A.Van NormanE. R.NelsonP. M. (2021b). Using interval likelihood ratios in gated screening: A direct replication study. Assessment for Effective Intervention, 47(1), 14–25. https://doi.org/10.1177/1534508420953894
28.
KongJ. E.ArizmendiG.DoablerC. T. (2023). Implementing the science of math in a culturally sustainable framework for students with and at risk for math learning disabilities. TEACHING Exceptional Children, 56(1), 44–51. https://doi.org/10.1177/00400599221127385
29.
KuhfeldM.SolandJ.LewisK. (2022). Test score patterns across three COVID-19-impacted school years. Educational Researcher, 51(7), 500–506. https://doi.org/10.3102/0013189X221109178
30.
LerouxA. J.BeretvasS. N. (2022). Cross-classified random-effects models. In O'ConnellA. A.McCoachD. B.B. A. Bell (Eds.), Multilevel modeling with introductory and advanced applications (pp. 165–208). Information Age Publishing, Inc.
NewellK. W.CoddingR. S.FortuneT. W. (2020). Oral reading fluency as a screening tool with English learners: A systematic review. Psychology in the Schools, 57(8), 1208–1239. https://doi.org/10.1002/pits.22406
PalyB. J.KlingbeilD. A.ClemensN. H.OsmanD. J. (2022). A cost effectiveness analysis of four approaches to universal screening for reading risk in upper elementary and middle school. Journal of School Psychology, 92, 246–264. https://doi.org/10.1016/j.jsp.2022.03.009
37.
PepeM. S.JanesH.LongtonG.LeisenringW.NewcombP. (2004). Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. American Journal of Epidemiology, 159(9), 882–890. https://doi.org/10.1093/aje/kwh101
38.
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
39.
Renaissance Learning. (2020). How kids are performing: Tracking the impact of COVID-19 on reading and mathematics achievement. https://renaissance.widen.net/s/wmjtlxkhbm
Robinson-CimpianJ. P.ThompsonK. D.UmanskyI. M. (2016). Research and policy considerations for English learner equity. Policy Insights From the Behavioral and Brain Sciences, 3(1), 129–137. https://doi.org/10.1177/2372732215623553
43.
RobinsonJ. P. (2010). The effects of test translation on young English learners’ mathematics performance. Educational Researcher, 39(8), 582–590. https://doi.org/10.3102/0013189X10389811
44.
SeethalerP. M.FuchsL. S.FuchsD.ComptonD. L. (2016). Does the value of dynamic assessment in predicting end-of-first-grade mathematics performance differ as a function of English language proficiency?The Elementary School Journal, 117(2), 171–191. https://doi.org/10.1086/688870
45.
SivulaT.MagnussonM.MatamorosA. A.VehtariA. (2020). Uncertainty in Bayesian leave-one-out cross-validation based model comparison. arXiv. https://arxiv.org/abs/2008.10296
46.
Solano-FloresG. (2014). Probabilistic approaches to examining linguistic features of test items and their effects on the performance of English language learners. Applied Measurement in Education, 27(4), 236–247. https://doi.org/10.1080/08957347.2014.944308
47.
Solano-FloresG. (2016). Assessing English language learners: Theory and practice. Routledge.
48.
Solano-FloresG.Barnett-ClarkeC.KachchafR. R. (2013). Semiotic structure and meaning making: The performance of English language learners on mathematics tests. Educational Assessment, 18(3), 147–161. https://doi.org/10.1080/10627197.2013.814515
49.
SwansonH. L.KongJ.PetcuS. D. (2023). Stability of learning disabilities, cognitive growth, and L1 in English learners: A latent class and transition analysis. Journal of Educational Psychology, 115(3), 379–404. https://doi.org/10.1037/edu0000771
VegaD.WolfJ. N. (2023). Best practices in assessment of bilingual learners. In HarrisonP.ProctorS.ThomasA. (Eds.), Best practices in school psychology (7th ed., pp. 163–174). National Association of School Psychologists.
YaremychH. E.PreacherK. J.HedekerD. (2023). Centering categorical predictors in multilevel models: Best practices and interpretation. Psychological Methods, 28(3), 613–630. https://doi.org/10.1037/met0000434
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
