Neurocognitive Factors in EdTech Intervention Response

The overarching goal of this proposal is to address limitations inherent in current remedial approaches utilized in reading disability (RD). Specifically, the experimenters aim to evaluate the effectiveness of a technology-based intervention for children with RD, using a randomized controlled trial. Nature of the Problem. Reading Disability (RD) is the most common learning disability, affecting 10 - 15% of school age children. It incurs major functional impairments at all stages of life. A wealth of data documents lifelong disadvantages in educational and occupational attainment. Problematically, current evidence-based reading interventions largely rely on services by trained specialists, either in well-resourced classrooms or clinical settings. As such, under-resourced schools (or countries) often are unable to provide reading interventions for their students. This potentially tragic situation has fostered the proposed project that aims to evaluate an affordable, promising, technology-based, treatment, which has twofold advantages, that of being home-based and, also, to be implemented without direct expert supervision, thereby minimizing resource demands. Recent studies have supported the efficacy of technology-based reading interventions, particularly GraphoLearn, which focuses on teaching phonological skills and letter sound correspondence, though with notable variation in effect sizes. As highlighted in a recent meta-analysis, sample sizes tend to be small, and there is marked variation in key methodological components across studies, particularly gameplay dosage (which is well below expert recommendations in most studies), control group (e.g., active versus passive), and level of adult involvement in administration. Most relevant, the utility of GraphoLearn has not been adequately examined outside the confines of controlled school-settings, thus failing to inform on the value of home administration - the major positive feature of the GraphoLearn. Similarly, possible predictors of negative outcomes for home administration are unexplored (e.g., compliance with prescribed gameplay dosage, which, as noted in our preliminary data, can vary).

Overview for Aim 1. The proposed project addresses limitations inherent in remedial interventions by implementing a large-scale, randomized controlled trial (RCT) of home-administered GraphoLearn in 450 RD children (boys and girls, ages 6.0-10.0). To accomplish this goal rapidly and with minimal cost, the experimenters will recruit participants enrolled in the Healthy Brain Network [HBN], an ongoing study of mental health and learning disorders in children, ages 5.0-21.0, whose family have concerns about behavior and/or learning (target n = 10,000; current n = 3000+). The HBN includes comprehensive psychiatric, cognitive, EEG and multimodal MRI characterizations for all participants, providing the proposed study with rich descriptive data. Reading disabilities will be identified using the TOWRE (standard score < 85). Children will be assigned to a 12-week/20-hour program of GraphoLearn or an "active" control condition, group matched for IQ and reading-level. The control will consist of a math game, similar in style to GraphoLearn, to control for non-specific effects of the intervention, such as time spent on the program, attention, and other factors. Children randomized to the control group will be offered GraphoLearn following the study due to ethical considerations. Consistent with real-world conditions, non-study related interventions during the course of the study period will be permitted. They will be documented for comprehensive sample description, and for analyzing predictors of response (Aim 2).

Aim 1: Evaluate the effectiveness of GraphoLearn in a large sample of children with RD. Children's reading level will be assessed before, during (week 6), and at the end of, the active study (week 12). Extended effects will be evaluated 12 weeks post treatment (week 24). The experimenters hypothesize statistically significant differences in reading level in favor of the GraphoLearn group over the control group at weeks 12 and 24. Should the hypothesis be supported, it will have important public health implications for children with RD (there is no claim that the program will be substitute for individualized, and expert-supervised, reading remediation).

Overview for Aim 2. The identification of participant-level and environment-level factors that predict treatment response is an important topical goal but has not been considered for GraphoLearn. For specialist-based interventions, a consistent finding is that older children, and children with language or cognitive impairments, are less likely to benefit. Here, the experimenters leverage the diversity of co-morbid clinical presentations among poor readers in HBN to identify putative predictors of treatment response. In line with recent studies suggesting the added value of brain-based features for prediction of intervention outcomes, the experimenters also leverage the availability of anatomical MRI data in the development of predictive models.

Aim 2. Identify participant-related and environment-level factors that are significantly associated with GraphoLearn outcomes. The extensive phenotypic and imaging characterizations included in the HBN sample are uniquely suited for exploring an extensive set of participant and environmental factors that may predict treatment outcomes. Random Forest Regression models will be used to predict differences in intervention response to GraphoLearn, based on an a priori set of baseline demographic, cognitive, emotional, brain and home environment variables. Assuming that compliance (i.e., actual time spent playing GraphoLearn relative to prescribed) is found to be a strong predictor of response, exploratory analyses will also look at predictors of compliance to further increase real world value of the proposed work.