# Project II: Developing Dynamic Forecasting Intervention Algorithms for Children with Severe Literacy Disabilities

> **NIH NIH P50** · FLORIDA STATE UNIVERSITY · 2021 · $201,552

## Abstract

ABSTRACT
 Project 2
 Too many children fail to achieve proficient reading and writing skills, which has serious public health and
economic consequences. This is because reading and writing difficulties are associated with grade retention,
referral to special education, dropping out of high school, and entering the juvenile criminal justice system.
Moreover, on average, the literacy proficiency of students with learning disabilities is one standard deviation
lower than that of typically developing students, and children from low socio-economic backgrounds achieve
lower literacy proficiency than their more affluent peers. With funding from NICHD and IES, we have made
important strides in improving literacy outcomes for children through the use of dynamic forecasting
intervention (DFI) algorithms in Assessment-to-instruction (A2i) technology. A2i is a web-based teaching
support technology designed to improve teachers' effectiveness in delivering individualized (or personalized)
literacy instruction. However, it is not clear how well the A2i DFI algorithms predict optimal amounts and types
of instruction for children whose reading and writing skills fall at the lowest tail of the continuum, and who are
least likely to be responsive to general education and intensive interventions. Hence, the overarching aim of
the proposed research is to use what we have learned over the past 13 years of developing the DFI algorithms
and A2i and conducting randomized controlled trials to test their precision and efficacy; and to address the
learning needs of children with the most severe learning disabilities including those with dyslexia and
dysgraphia. The project has three specific and interrelated aims. Aim 1 is to determine, using extant
assessment and observation data, how predictive current DFI algorithms are for children with severe literacy
problems, dyslexia, and dysgraphia; develop expanded algorithms that include child characteristics found to be
important for the prediction of response to intervention and additional types of intervention such as assistive
technologies; and run simulations of the impact of various algorithms on students' gains during the school year.
Aim 2 is to incorporate the expanded DFI algorithms into the A2i technology, which will display
recommendations for specific types of instruction/intervention as well as accommodations and assistive
technology. Aim 3 is to conduct a prospective study to closely examine the nature and variability of classroom
instruction for children with severe literacy disabilities, dyslexia, and dysgraphia. Broadly implemented, these
studies have the potential to improve the literacy skills of our most vulnerable children, those with severe
learning disabilities, and to improve the quality of their lives academically and beyond.

## Key facts

- **NIH application ID:** 10238867
- **Project number:** 5P50HD052120-15
- **Recipient organization:** FLORIDA STATE UNIVERSITY
- **Principal Investigator:** Carol McDonald Connor
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $201,552
- **Award type:** 5
- **Project period:** 2006-07-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10238867

## Citation

> US National Institutes of Health, RePORTER application 10238867, Project II: Developing Dynamic Forecasting Intervention Algorithms for Children with Severe Literacy Disabilities (5P50HD052120-15). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10238867. Licensed CC0.

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