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

至少有10%的人口患有诵读困难症，导致阅读和写作困难，并经常导致学业失败（40%的辍学者患有诵读困难症）。 如果人们知道他们有阅读障碍，他们可以努力训练，随着时间的推移，以克服其负面影响。 然而，即使我们知道如何检测阅读障碍，大多数儿童被诊断为晚期，因为目前的程序是昂贵的，需要专业的监督。 PI的目标是让每个人都尽早知道他们是否有阅读障碍;他实现这一目标的方法是让发现变得容易，便宜，甚至令人愉快。 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。 到目前为止，大部分关于检测和支持活动的研究都是在实验室中进行的;该团队计划通过可穿戴设备将这项工作扩展到真实的世界，以帮助患有阅读障碍的人在课堂外的学习活动中更好地阅读和写作，例如，在博物馆或历史遗址。 这项工作将建立在团队的Dytective软件上，PI计划将其与其他工具一起公开发布，这些工具将作为这项研究的一部分进行开发和完善。 该团队将与社区合作伙伴，如阅读障碍组织和学校，本科生和研究生以及相关领域的专家合作，尽可能广泛地传播他们的研究成果，培养这一领域的年轻研究人员，并将他们的工作与其他相关工作相结合。 此外，CMU的人为因素课程将添加有关阅读障碍和语言技术的新课程模块。目前检测阅读障碍的方法需要专业心理学家或昂贵的大脑成像设备（以及运行它并解释结果的专家）。 PI的方法来检测和支持阅读障碍使用一个可扩展的基于网络的游戏。 该方法依赖于人机交互指标，这些指标来自于玩游戏的人，这些游戏是根据对阅读障碍患者倾向于犯的错误的语言和经验理解而设计的。 对这些数据的机器学习可以允许比其他方式更早地检测到阅读障碍（并且花费更少）。 虽然目前的研究是由先前的工作特点的阅读障碍的起源及其语言表现，智力的贡献在于理解我们如何可以使用数据从游戏中检测阅读障碍。 这种方法，一旦证明，可以推广到其他领域，和练习，被发现是最有用的检测或支持诵读困难症也可以告知我们的基本理解诵读困难症。

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.