|Title:||Cognitive and Motivational Contributors to Reading Comprehension in English Learners (ELs) and English Monolinguals (EMs): Different or Similar Growth Patterns|
|Principal Investigator:||Taboada Barber, Ana||Awardee:||University of Maryland, College Park|
|Program:||English Learners [Program Details]|
|Award Period:||4 years (7/1/2016-6/30/2020)||Award Amount:||$1,399,985|
Co-Principal Investigator: Kelly Cartwright (Christopher Newport University); Laura Stapleton (University of Maryland, College Park)
Purpose: The purpose of this project is to examine known cognitive factors (e.g., word level factors, oral language, strategy use), as well as novel cognitive malleable factors (e.g., EF skills: inhibitory control, working memory, and cognitive flexibility) and reading engagement components involved in the development of reading comprehension in English Learners (ELs) and English Monolinguals (EMs). Although ELs have adequate word reading skills in the early elementary grades, they perform relatively poorly compared to their EM peers on reading comprehension as early as third grade. There is little known about how the developmental trajectories of ELs and EMs compare during this age period, particularly with respect to underlying cognitive and engagement factors.
Project Activities: In order to understand how these cognitive components and reading engagement contribute to reading comprehension, researchers will work with four cohorts of students in grades 1 to 5 over three years. Using a cohort sequential design, the team will assess students in the fall and spring of each academic year. In Year 4, the research team will analyze the data and disseminate the findings.
Products: The products include evidence of the contributions of cognitive factors and reading engagement to the development of reading comprehension in both English Learners and English Monolinguals in the elementary grades. The team will also produce peer reviewed publications.
Setting: This study will take place in public schools in suburban Maryland. The school district includes a diverse student body, both along economic dimensions and ethnic characteristics. The research team will work with schools that have a high incidence of Spanish-speaking ELs.
Sample: Participants will include approximately 1,000 students in grades one to five, both ELs and EMs. The research team will recruit participating students in Year 1 and students will participate in the study longitudinally.
Intervention: There is no intervention in this project.
Research Design and Methods: The research team will use a cohort sequential design with four cohorts of students over 3 years. In each of the first three years, the research team will assess students in the fall and spring. In Year 1, approximately 250 students in each of grades 1, 2, 3, and 4, will participate. The following year, these same students, now in grades 2, 3, 4, and 5, will participate. In Year 3, students remaining in elementary school, now in grades 3, 4, and 5, will participate. The battery of measures includes both individual and group measures, and will take approximately 2 hours to administer. In Year 4, the research team will analyze the data and disseminate the findings.
Control Condition: There is not a control condition in this project.
Key Measures: Predictors of reading comprehension include the oral comprehension, picture vocabulary, and letter and word identification subtests of the Woodcock-Johnson III Tests of Achievement, the inconsistency detection task, measures of inference making, and reading awareness. Researchers will measure executive function using the Inhibition subtest of the NEPSY-II (NEuroPSYchological Developmental Assessment), the Letters Backward Subtest of the Test of Memory and Learning – 2 (TOMAL-2), and the Graphophonological-Semantic Flexibility task. They will measure reading engagement using the Reading Engagement Index. The research team will measure reading comprehension using the Woodcock-Johnson Tests of Achievement-II and the Gates-McGinitie Reading Comprehension Test.
Data Analytic Strategy: The research team will use latent growth curve analyses to measure growth trajectories for each of the key constructs.