Project Activities
This project will follow two cohorts of students in multiple U.S. states to evaluate the use of universal screening and progress monitoring procedures. Analyses will also be designed to explore the connection between English language proficiency and literacy in English.
Structured Abstract
Setting
Schools in World-Class Instructional Design and Assessment (WIDA) Consortium states, an organization dedicated to the design and implementation of high standards and equal educational opportunities for ELLs.
Sample
1,800 ELL students and 300 non-ELL students in each of two cohorts will be followed for four years. When the project begins, cohort 1 will be in kindergarten and cohort 2 will be in fourth grade.
Researchers will examine the psychometric properties (such as predictive validity, specificity, sensitivity, negative and positive predictive power, and reliability) of the Curriculum-Based Measures (CBM) used in this study, Dynamic Indicators of Basic Early Literacy Skills (DIBELS), and Indicadores Dinámicos del Éxito en la Lectura (IDEL) for screening ELLs and identifying students at risk for academic difficulties, both for the overall group and for students with varying language levels. Researchers will gather evidence on how well the instruments meet established criteria for universal screening, such as appropriateness, technical adequacy, and usability. In addition, researchers will assess the adequacy of the instruments for distinguishing between ELL students who are making adequate academic progress from students at risk for academic failure.
Research design and methods
The project team will collect data assessing English language proficiency and performance on standardized achievement tests once each year. Curriculum-based assessments will be administered monthly for progress monitoring purposes. To determine the most appropriate measures for use in progress monitoring for ELLs, the validity of CBM and DIBELS in predicting performance on the Stanford Achievement Test, Tenth Edition and state reading assessments will be determined. The degree to which language level moderates predictive validity will also be determined. The team will assess sensitivity of instruments as related to progress monitoring (as compared to screening), by determining if measures detect improvement in reading proficiency and individual differences in growth rates. The power of assessments in predicting correct classification of ELLs with regard to diagnostic criteria will be estimated. Student growth trajectories will be studied to explore the connection between rate of growth for ELLs in English language proficiency and English literacy skills. Analyses will also explore whether students exhibit different types of learning trajectories, and if so, what characteristics differentiate ELL students with different trajectories in reading achievement.
Control condition
With the exception of the Spanish measures, assessment results will also be collected for a sample of non-ELL students.
Key measures
Measures include Assessing Comprehension and Communication in English State-to-State for English Language Learners, DIBELS, IDEL (only Spanish-speaking students), CBMs (such as word identification, oral reading fluency probes, maze procedures), SAT 10, and state academic achievement measures.
Data analytic strategy
Multilevel regression analysis will be used to study predictive validity to account for nesting of students within schools. Incremental validity analysis will be used to identify the unique predictive power of each screening instrument. To determine predictive validity of the screening tools, cut scores will be calculated and measures of sensitivity, specificity, and positive and negative predictive power will be determined for CBM, DIBELS, and IDEL in identifying ELLs who are not making adequate progress. Procedures will be used with dichotomous screening data to determine which multiple identifiers are more useful. Multi-level growth mixture modeling will be used to study latent classes of students with different growth trajectories. Multivariate growth curve modeling will be used to identify the typical language acquisition process and rates of reading growth among ELLs.
People and institutions involved
IES program contact(s)
Products and publications
Products: Products include peer-reviewed articles that will summarize guidelines for the most effective use of literacy measures for screening and describe the progress of ELLs.
Book chapter
Albers, C.A., and Mission, P.L. (2013). Universal Screening Within ELL Populations. In R.J. Kettler, T.A. Glover, C.A. Albers, and K.A. Feeney-Kettler (Eds.), Universal Screening of Students: Best Practices for Identification, Implementation, and Interpretation. Washington, DC: American Psychological Association.
Albers, C.A., Mission, P.L., and Bice, B.J. (2013). Considering Diverse Learner Characteristics in Problem-Solving Assessment. In R. Brown-Chidsey, and K. Andren (Eds.), Problem-Solving Based Assessment for Educational Intervention (2nd ed., pp. 101-122). New York: Guilford.
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
