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

项目二：为严重识字障碍儿童开发动态预测干预算法

• 批准号: 10238867
• 负责人: Carol McDonald Connor
• 金额: $ 20.16万
• 依托单位: FLORIDA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238867
• 关键词: Address; Adolescent; Algorithms; Attention deficit hyperactivity disorder; Characteristics; Child; Code; Collaborations; Complex; Criminal Justice; Data; Development; Drops; Dysgraphias; Dyslexia; Eagles; Education; Educational Intervention; Educational process of instructing; Effectiveness; Family; Florida; Funding; Grant; Individual; Informal Social Control; Instruction; Intervention; Knowledge; Language; Language Therapy; Lead; Learning; Learning Disabilities; Literature; Measures; Meta-Analysis; Minority Groups; Modeling; National Institute of Child Health and Human Development; Nature; Online Systems; Oral; Outcome; Population; Poverty; Prospective Studies; Public Health; Questionnaires; Randomized Controlled Trials; Reading; Recommendation; Recording of previous events; Research; Resource Development; Running; Schools; Self-Help Devices; Special Education; Speech; Students; System; Tail; Technology; Technology Assessment; Testing; Text; Videotape; Writing; base; comorbidity; design; disability; efficacy testing; executive function; falls; health economics; high school; improved; kindergarten; lexical; literacy; new technology; peer; phonology; predicting response; prediction algorithm; prototype; response; simulation; skills; social skills; socioeconomics; teacher; third grade; tool

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.

摘要 计划2 太多的儿童不能达到熟练的阅读和写作技能，这对公共健康和 经济后果。这是因为阅读和写作困难与成绩保持有关， 转介到特殊教育，高中辍学，进入少年刑事司法系统。 此外，平均而言，有学习障碍的学生的识字能力是一个标准差 低于一般发展中的学生，来自低社会经济背景的儿童 识字能力低于富裕的同龄人。在NICHD和IES的资助下，我们 通过使用动态预测，在改善儿童扫盲成果方面取得重大进展 干预（DFI）算法在评估到指导（A2 i）技术。A2 i是一个基于Web的教学 支持技术，旨在提高教师在提供个性化（或个性化） 识字教学然而，目前尚不清楚A2 i DFI算法预测最佳数量和类型的效果如何 对于那些阅读和写作技能处于连续体最低尾部的儿童， 最不可能对普通教育和强化干预作出反应。因此， 所提出的研究是利用我们在过去13年中开发DFI算法所学到的知识 和A2 i，并进行随机对照试验，以测试其精度和有效性;并解决 有最严重学习障碍的儿童，包括有阅读障碍的儿童的学习需要， 书写困难该项目有三个相互关联的具体目标。目的1是确定，使用现存的 评估和观察数据，当前DFI算法对严重识字儿童的预测性如何 问题，诵读困难和书写困难;开发扩展算法，包括儿童特征， 对于预测对干预和其他类型的干预（如辅助性干预）的反应非常重要。 技术;并模拟各种算法对学生在学年中的收益的影响。 目标2是将扩展的DFI算法纳入A2 i技术， 对具体类型的指导/干预以及住宿和辅助设施的建议 技术.目的3是进行一项前瞻性研究，以密切关注课堂的性质和变化 为有严重读写障碍、诵读困难和书写困难的儿童提供指导。广泛实施，这些 研究有可能提高我们最脆弱的儿童的识字能力， 学习障碍，并提高他们的生活质量的学术和超越。