This study shows how item response theory can be used to estimate causal effects on psychological network structure, revealing intervention impacts on how skills or symptoms are interconnected beyond average score changes.
This study shows that sustaining a content literacy intervention from first to second grade improves reading comprehension by building knowledge schemas that enable students to transfer learning to new, content-rich texts.
This study shows that a short, first-grade content literacy intervention can significantly strengthen science knowledge and reading comprehension without harming foundational literacy skills or engagement.
This study shows that sustained content literacy instruction improves second-grade reading comprehension primarily by building interconnected networks of domain-specific vocabulary over time.
This study shows that a SMART-designed, adaptive K–2 literacy intervention can improve reading outcomes—especially for nonresponders—when personalized print texts and apps are paired with targeted parent text-message supports.
This meta-analysis shows that educational apps can meaningfully improve early literacy and math—especially for preschoolers and constrained skills—but their effectiveness varies widely based on age, skill type, and outcome measurement.
This study shows that parental text messaging is most effective when messages are personalized and thoughtfully framed, with certain combinations of message content producing stronger gains in student reading than others.
This study shows that modeling item-level heterogeneous treatment effects with the Explanatory Item Response Model can reveal meaningful intervention impacts on specific assessment items that are hidden by average test-score analyses.
This study shows how combining human scoring with automated text analysis can deepen understanding of writing intervention impacts by revealing the linguistic and cognitive mechanisms behind improved student writing.
