|Title:||Making Progress with Progress Monitoring: Developing Early Literacy and Language Individual and Growth Development Indicators as Progress Monitoring Tools|
|Principal Investigator:||Wackerle-Hollman, Alicia||Awardee:||University of Minnesota|
|Program:||Early Learning Programs and Policies [Program Details]|
|Award Period:||4 years (8/1/2016-7/31/2020)||Award Amount:||$1,400,000|
Co-Principal Investigators: Scott McConnell and Michael Rodriguez (University of Minnesota), Robin Hojnoski (LeHigh University), and Kristen Missall (University of Washington)
Purpose: The purpose of this project is to develop a progress monitoring tool that can be used to assess young children's language and literacy skills and identify children in need of intervention. The research team will develop and validate the Progress Monitoring Individual Growth and Development Indicators (PM-IGDIs), which will assess phonological awareness, oral language, alphabet knowledge and comprehension for 4- and 5-year-old children. The PM-IGDIs will build on and use items from existing IGDI 2.0 resources, which were developed by this research team with previous funding from IES, to create comprehensive scales of performance.
Project Activities: Researchers will develop early literacy progress monitoring measures to assess children's the language and literacy skills, conduct studies to examine the validity and reliability of the measures, examine children's growth trajectories, and evaluate the PM-IGDIs as a predictor of children's school readiness skills in kindergarten. The team will conduct analyses to examine the psychometric properties of each measure.
Products: Products include a fully developed and validated progress monitoring tool to assess the language and literacy skills of English speaking preschoolers. The team will also produce test manuals to support data-driven decision making for early childhood educators and peer reviewed publications.
Setting: This study will take place in Head Start, private and public prekindergarten programs in urban, suburban and rural locations in Minnesota, Pennsylvania and Washington.
Sample: Study participants will include up to 15 preschool teachers and 750 typically developing 4- and 5-year-old children.
Assessment: The Progress Monitoring Individual Growth and Development Indicators (PM-IGDIs) will build on and use items from existing IGDI 2.0 resources developed by the research team with previous funding from IES. IGDIs 2.0 measures were developed under the IES Center for Response to Intervention in Early Childhood (CRITEC). IGDIs 2.0 screening tools are current distributed commercially as MyIGDIs . MyIGDIs is a fee-for-service data storage and reporting website accompanying the IGDI 2.0 assessments. PM-IGDIs will utilize the same item development process used for other IGDI 2.0 items to create expanded PM-IGDI item sets. PM-IGDIs are designed to support data-based decision making by monitoring growth to support early language and literacy development in U.S. classrooms. The fully developed progressing monitoring tool will include five tasks: Picture Naming (oral language), Which One Doesn't Belong (comprehension), Rhyming and First Sounds (phonological awareness) and Sound Identification (alphabet knowledge). The brief tasks (2 to 4 minutes each) will be administered to children using tablet-based technology, IGDI-APEL, to quickly and efficiently assess performance, store student scores and report growth in easy to understand and accessible metrics for classroom teachers. Each task includes 20-40 items that are designed to be administered frequently (3 or 6 weeks) to support data-based decision making.
Research Design and Methods: This project will take place in six phases over four years. Parents will participate in each phase that includes student data collection by completing home surveys to provide student level demographics and home language environment information. In Years 1 and 2/Phases 1-3, the research team will create new items and expand the items banks to make sure they have enough items for progress monitoring, and incorporate the new items into the IGDI-APEL software application. Once the application is fully functional with tested items, the researchers will conduct two studies to address fluency, stability of scores in the context of growth and reliability of slope for each measure. In Year 3/Phase 4, the researchers will examine growth profiles of typically developing pre-k students using PM-IGDIs. They will produce a comprehensive trajectory of one year of growth for each PM-IGDI derived from tri-weekly assessments. In Year 3/Phase 5, the researchers will conduct a predictive validity study to examine the association between scores of students who participated in PM-IGDIs assessments during prekindergarten and their language and literacy skills in kindergarten. In Year 4/ Phase 6, the researchers will recruit 15 teachers for semi-annual focus groups to review materials and provide feedback on design of technical protocols and administration guides. The team will finalize PM-IGDIs, test manuals and supporting documentation, and conduct analyses to examine the predictive validity of the progress monitoring tool.
Control Condition: There is no control condition for this project.
Key Measures: Measures will include direct assessments using the IGDIs 2.0, Spanish IGDIs, the Test of Early Reading Ability-3 (TERA-3), the Peabody Picture Vocabulary Test-4, the Test of Preschool Early Literacy (TOPEL), and the Formative Assessment for Teachers (FAST) early reading. Researchers will use the Classroom Assessment Scoring System to collect observational data.
Data Analytic Strategy: The research team will use transcripts from focus groups and observational data to conduct qualitative analysis to examine the clarity and usability of test manuals and interfaces. They will conduct quantitative analyses to examine descriptive statistics of student performance on key measures and correlations between the FAST, TERA-3 and PM-IGDIs. Researchers will use Rasch modeling to identify difficulty of items, evaluate item quality, and examine differential item functioning. They will also use multi-level models to conduct analyses to evaluate predictive validity.
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