CHS: Small: Early Dyslexia Detection and Support at Scale to Help Students Succeed in School

CHS：小型：早期诵读困难检测和大规模支持，帮助学生在学校取得成功

• 批准号: 1618784
• 负责人: Jeffrey Bigham
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1618784&HistoricalAwards=false
• 关键词: CHS; Small; Early; Dyslexia; Detection

At least 10% of the population has dyslexia, which results in difficulty with reading and writing, and often leads to school failure (40% of those who drop out of school have dyslexia). If people know they have dyslexia, they can with effort train over time to overcome its negative effects. Yet even though we know how to detect dyslexia, most children are diagnosed late because current procedures are expensive and require professional oversight. The PI's goal is for everyone to know as early as possible if they might have dyslexia; his approach to achieving this goal is to make it easy, inexpensive, and even enjoyable to find out. To these ends, he and his team plan to design personalized game activities based on the detection results to target the cognitive skills with which students need to practice most. Much of the research into detection and support activities has thus far taken place in the lab; the team plans to extend this work into the real world via wearable devices to help people with dyslexia better read and write in the context of learning activities outside of the classroom, e.g., in museums or at historic sites. The work will build on the team's Dytective software, which the PI plans to publicly release along with other tools that will be developed and refined as part of this research. The team will work with community partners like dyslexia organizations and schools, undergraduate and graduate students, and experts from related fields to disseminate their findings as widely as possible, to nurture the development of young researchers in this area, and to integrate their work with other related efforts. In addition, new course modules on Dyslexia and Language Technology will be added to the Human Factors course at CMU.Current approaches for detecting dyslexia require either a professional psychologist or expensive brain imaging equipment (and an expert to run it and interpret the results). The PI's approach to detecting and supporting dyslexia uses a scalable web-based game. The method relies on human-computer interaction metrics drawn from people playing games designed with a linguistic and empirical understanding of the errors that people with dyslexia tend to make. Machine learning over this data may allow for the detection of dyslexia much earlier (and at much less expense) than would otherwise be possible. Although the current research is informed by prior work characterizing the origin of dyslexia and its linguistic manifestation, the intellectual contribution lies instead in understanding how we can use data from game play to detect dyslexia. This approach, once demonstrated, may generalize to other areas, and the exercises that are found to be most useful in detecting or supporting dyslexia may also inform our basic understanding of dyslexia.

至少有10％的人群患有阅读障碍，这会导致阅读和写作困难，并且经常导致学校失败（辍学的人中有40％患有阅读障碍）。 如果人们知道自己患有阅读障碍，他们可以随着时间的流逝而努力训练以克服其负面影响。 然而，即使我们知道如何检测阅读障碍，大多数儿童都被诊断出来，因为当前程序很昂贵，需要专业的监督。 PI的目标是让每个人是否可能患有阅读障碍。他实现这一目标的方法是使它变得容易，便宜，甚至很有趣。 为此，他和他的团队计划根据检测结果设计个性化的游戏活动，以针对学生最需要练习的认知技能。 迄今为止，在实验室进行了大部分研究和支持活动的研究；该团队计划通过可穿戴设备将这项工作扩展到现实世界中，以帮助患有阅读障碍的人更好地在教室外的学习活动的背景下，例如在博物馆或历史遗址。 这项工作将建立在团队的垂直软件的基础上，PI计划将其与其他将作为本研究的一部分开发和完善的工具公开发布。 该团队将与阅读障碍组织和学校，本科生和研究生以及来自相关领域的专家等社区合作伙伴合作，以尽可能广泛地传播他们的发现，以培育该领域的年轻研究人员的发展，并将其工作与其他相关工作融合在一起。 此外，关于阅读障碍和语言技术的新课程模块将被添加到CMU的人为因素课程中。检测阅读障碍的通行方法需要专业的心理学家或昂贵的脑成像设备（以及运行并解释结果的专家）。 PI检测和支持阅读障碍的方法使用了可扩展的基于Web的游戏。 该方法依赖于人类计算机的互动指标，这些指标是从玩游戏设计的人们对阅读障碍者倾向于遇到的错误的游戏设计的游戏。 对这些数据的机器学习可能会使阅读障碍的检测要早得多（费用要少得多）。 尽管当前的研究是通过表征阅读障碍及其语言表现形式的先前工作来告知的，但智力贡献在于了解我们如何使用游戏中的数据来检测阅读障碍。 这种方法一旦证明，就可以推广到其他领域，并且发现在检测或支持阅读障碍最有用的练习中也可能为我们对阅读障碍的基本理解提供信息。