The Feasibility of Technology to Support Data-Driven Decision Making in Early Head Start Classrooms

Educators have struggled to implement research-based practices effectively, particularly in early childhood settings where data-driven decision making (DDDM) is important for guiding practice. Implementation of DDDM is hindered by a lack of progress monitoring assessments, guidelines for how to use data to inform intervention decisions, and professional development for educators. To address these challenges, tools like the Individual Growth and Development Indicators (IGDIs) and the Making Online Decisions (MOD) system have been developed, demonstrating significant benefits in enhancing education outcomes when used effectively. The purpose of this study was to investigate the feasibility of the MOD to enhance DDDM for young children performing below benchmark in Early Head Start. Findings indicated that the MOD increased the frequency of progress monitoring, high implementation fidelity, and sustainability after research support was discontinued. Educator satisfaction was generally high, but suggestions were made for improvements. Limitations, implications for practice, and future directions are discussed.