Effectiveness and predictors of response for a technology-based reading intervention in the home

项目摘要/摘要。 阅读障碍(RD)是最常见的学习障碍，影响到10%-15%的学龄儿童。它 在生命的所有阶段都会招致严重的功能障碍，有大量数据记录了终身的缺点 在教育和职业成就方面。因此，寻找有效和负担得起的RD治疗方法是 这是阅读研究人员、临床医生和教育工作者的高度优先事项。有问题的是，当前的循证阅读 干预在很大程度上依赖于训练有素的专家的服务，无论是在资源充足的教室里，还是在临床上 设置。因此，资源不足的学校(或国家)往往无法为 他们的学生。近年来，基于技术的阅读干预被提出作为一种手段 克服这些挑战，因为它们可以在家里管理，而不需要直接的专家监督- 从而最大限度地减少资源需求。在以阅读为重点的教育技术领域，GraphoLearn已经成为 领导者，拥有迄今为止最大的证据基础。然而，到目前为止，绝大多数研究都是进行的 在高度受控的环境中，而不是它打算用于的家庭环境-留下悬而未决的问题 关于有效性。此外，与任何干预措施类似，并不是所有患有RD的儿童都能从 治疗；然而，很少注意确定治疗反应的预测因素。 在这里，我们建议通过实施来评估家庭管理的GraphoLearn的有效性 在450名阅读障碍儿童(男孩和女孩，年龄为6.0岁- 10.0)。为了以最小的成本快速实现这一目标，我们将从健康的大脑中招募参与者 网络[HBN]，一项正在进行的关于5.0-21.0岁儿童心理健康和学习障碍的研究，其 家庭对行为和/或学习有顾虑(目标n=10,000；目前n=3000+)。是否可以使用 所有HBN的综合特征(例如，人口统计、认知、精神健康、EEG、多模式磁共振) 参与者使样本成为探索广泛的参与者和环境因素的最佳样本 这可能会影响治疗结果(即，人口、认知、情感、神经生物学、环境)。 拟议工作的具体目标是：1)在大样本中评估GraphoLearn的有效性 患有RD的儿童，以及2)确定与参与者相关的和环境水平显著的因素 与图形学习结果相关联。为了实现后一个目标，复杂的机器学习 将采用方法(随机森林回归模型)。

Project Summary/Abstract. 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, with a wealth of data documenting lifelong disadvantages in educational and occupational attainment. Therefore, identifying effective and affordable treatments for RD is a high priority for reading researchers, clinicians and educators. 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. In recent years, technology-based reading interventions have been proposed as a means of overcoming these challenges, as they can be administered in the home, without direct expert supervision - thereby minimizing resource demands. In the area of reading-focused EdTech, GraphoLearn has emerged as a leader, with the largest evidence-base to date. However, the vast majority of studies to date have been conducted in highly controlled settings, rather than the home environment it was intended for – leaving open questions about effectiveness. Additionally, similar to any intervention, not all children with RD benefit equally from treatment; however little attention has been given to identifying predictors of treatment response. Here we propose to evaluate the effectiveness of home-administered GraphoLearn through the implementation of a large-scale, randomized controlled trial (RCT) in 450 reading disabled children (boys and girls, ages 6.0- 10.0). To accomplish this goal rapidly and with minimal cost, we will recruit participants from 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 availability of comprehensive characterizations (e.g., demographic, cognitive, mental health, EEG, multimodal MRI) for all HBN participants makes the sample optimal for exploring an extensive set of participant and environmental factors that may affect treatment outcomes (i.e., demographic, cognitive, emotional, neurobiological, environmental). Specific aims of the proposed work are to: 1) Evaluate the effectiveness of GraphoLearn in a large sample of children with RD, and 2) Identify participant-related and environment-level factors that are significantly associated with GraphoLearn outcomes. To accomplish this latter aim, sophisticated machine learning approaches (Random Forest Regression models) will be employed.