|Title:||Development of a Data-Based Decision Making System to Support Educators' Promotion of Infants and Toddlers' Cognitive Problem-Solving Skills|
|Principal Investigator:||Buzhardt, Jay||Awardee:||University of Kansas|
|Program:||Early Intervention and Early Learning [Program Details]|
|Award Period:||4 years (07/01/2017 - 06/30/2021)||Award Amount:||$1,400,000|
|Type:||Development and Innovation||Award Number:||R324A170141|
Co-Principal Investigators: Dale Walker; Dwight Irvin
Purpose: In this project, the research team will develop and test a web-based tool that supports infant-toddler service providers' use of child data to individualize services for children at risk for delay in cognition, gross motor skills, communication, or social skills. Despite evidence that using child data to inform services and curriculum decisions improves child outcomes, infant-toddler educators often lack the training and resources needed to monitor children's progress on key outcomes and individualize their services based on those outcomes. This research team developed a web application called Making Online Decisions (MOD) that guides early childhood educators through a decision-making process to individualize language intervention services. Research demonstrated that children served by home visitors using the MOD show significantly stronger language growth than children whose home visitors did not use the MOD. However, the MODis specifically designed for use in home visiting contexts and intervention recommendations only target language delays. The goal of the current project is to develop the web-based MOD Management System (MMS) for designing, developing, and deploying custom MODs to infant-toddler agencies that can target delays in cognition, gross motor skills, communication, or social skills. To increase adoption and feasibility of the MODs, they will also be customized to each agency's service-delivery model (such as home visiting or center-based) and capable of embedding evidence-based intervention/curriculum materials currently used by the agency. The MODs will use child outcomes from the existing Infant-Toddler Individual Growth and Development Indicators (IGDIs) to identify children who may be in need of more intensive intervention and to drive the data-based recommendations.
Project Activities: Through ongoing collaboration with local infant-toddler programs such as Part C and Early Head Start, the MMS will be developed and tested across four phases. In Phases 1–3, the team will develop, test, and refine the system based on feedback from service providers and their families through usability and feasibility testing. In Phase 4, they will pilot test custom MODs deployed to infant-toddler agencies using a small-scale randomized controlled trial to evaluate the effects of the system on service providers' data-based decision-making practices and infant-toddler growth in cognitive problem-solving skills.
Products: This project will result in a fully developed version of web-based MMS to rapidly develop and deploy MODs customized to an agency, district, or state's program based on the curriculum or services currently being used by a program. Products will also include peer-reviewed publications and presentations.
Setting: The research will take place in center- and home-based infant-toddler programs that serve children with identified disabilities (Part C programs) or are mandated to serve a proportion of children with disabilities (Early Head Start) in Kansas.
Sample: The target population will be center-based staff in Early Head Start or Part C programs and the infants and toddlers in their classrooms with or at risk for a disability. For usability testing, there will be an estimated 9 administrators, 16 service providers, and 6 parents. For feasibility testing, there will be an estimated 12 administrators, 18 service providers, and 18 child-parent dyads. For pilot testing, there will be an estimated 22 classrooms participating with 3 eligible children per classroom, leading to a total of 44 service providers and 66 child-parent dyads.
Intervention: The MMS will be a web-based system to develop custom MODs informed by child outcome data from the Infant-Toddler IGDIs (Early Problem Solving Indicator, Early Movement Indicator, Early Communication Indicator, or Early Social Indicator) to make recommendations for intervention/curriculum decisions for individual children. These IGDIs are 6-minute play-based assessments normed for children aged 6-42 months. The recommendations provided by MODs developed through the MMS will be driven by each child's assessment scores across the sub-domains of each IGDI. For example, the EPSI sub-domains include Looking, Exploring, Functions, and Solutions. The MMS will have the ability to be customized to each agency's service-delivery model and curriculum.
Research Design and Methods: During the first 3 years of the project, the research team will use design-based research methods to develop and test iterations of the MMS using feedback and usability data from center-based infant-toddler staff, interventionists, administrators, and parents. During the fourth year, the team will conduct a randomized controlled trial pilot study, assigning classrooms within center-based programs to the treatment or control condition. The pilot study will focus on one domain of development – problem solving – to assess the impact of the MODs deployed through the MMS on educators' knowledge and self-efficacy in using data to make curriculum decisions, their data-based decision-making practices, and children's growth in cognitive problem solving.
Control Condition: Classrooms assigned to the comparison condition will assess children's problem-solving skills quarterly with the EPSI and use their existing curriculum, similar to the experimental group; however, the control group will not have data-based decision-making support from the MOD.
Key Measures: During iterative development, the research team will measure usability, feasibility, and fidelity of the system using researcher-developed surveys and direct observation protocols, including think-aloud procedures. Early educator progress monitoring and decision-making practices will be measured using the Examining Data Informing Teaching measure. Knowledge and self-efficacy of data-based decision-making practices will be measured with researcher-developed surveys. Child growth in problem solving will be measured using the EPSI. Moderators include classroom quality (measured by the Classroom Assessment Scoring System) and child demographics.
Data Analytic Strategy: Usability and feasibility data will be analyzed descriptively. The research team will address limitations on the system based on the severity, frequency, and consistency of problems encountered by users. For the randomized controlled trial, multivariate analyses and growth curve modeling will be used to examine educator outcomes and children's problem-solving skills. Cohen's d will be used to estimate the effect sizes of statistically significant differences between the experimental and comparison groups.
Project Website: http://www.igdi.ku.edu
Related Projects: R324A120365