Technology to Guide Data-Driven Intervention Decisions: Effects on Language Growth of Young Children at Risk for Language Delay

TLDR

Data‑driven decision making in multitiered systems helps educators identify non‑responding children, tailor instruction, and monitor progress, and is increasingly adopted in early childhood settings. The authors developed the Making Online Decisions (MOD) web application to support educators in Early Head Start home‑visiting programs by guiding data‑driven decisions. In randomized trials, children at risk for language delay whose home visitors used MOD showed significantly greater growth on formative and standardized language measures, with parents’ language‑promotion strategies mediating the effect, though limitations were noted.

Abstract

Data-driven decision making (DDDM) helps educators identify children not responding to intervention, individualize instruction, and monitor response to intervention in multitiered systems of support (MTSS). More prevalent in K–12 special education, MTSS practices are emerging in early childhood. In previous reports, we described the Making Online Decisions (MOD) web application to guide DDDM for educators serving families with infants and toddlers in Early Head Start home-visiting programs. Findings from randomized control trials indicated that children at risk for language delay achieved significantly larger growth on the Early Communication Indicator formative language measure if their home visitors used the MOD to guide DDDM, compared to children whose home visitors were self-guided in their DDDM. Here, we describe findings from a randomized control trial indicating that these superior MOD effects extend to children’s language growth on standardized, norm-referenced language outcomes administered by assessors who were blind to condition and that parents’ use of language promotion strategies at home mediated these effects. Implications and limitations are discussed.