Math Learning Disabilities among Young Adults in College: Structure, Identification, and Validation

在社区学院（CC）注册的学生比在四年制学院和大学注册的学生要多得多，在社区学院学习发展（补救）数学的学生比在四年制学院学习的学生要多得多。然而，这些课程的失败率很高，发展数学的失败是STEM参与的一个重要障碍。令人惊讶的是，关于造成这种情况的具体技能缺陷和社会人口和个人障碍的数据很少。很可能是先前的教育，入门级的数学技能，动机，自我调节和情感因素，以及工作/家庭/财务考虑。然而，这些因素并未一并考虑。这个项目将寻求确定发展数学失败的基础因素，以及这些因素如何结合在一起。 它将利用这些信息开发一种新的方法来识别数学学习障碍（MLD）。然后，该项目旨在通过监测可能定性区分MLD与其他困难的生理因素来验证这种方法。该项目旨在加强关于数学技能如何发展以及受这些因素影响的理论，并解决如何在大学水平上识别MLD的问题。研究结果可能会指出识别和补救这些困难的潜在途径，这将有助于提高学生在大学的成功。该项目将招收三种类型的第一次在大学的学生：学生在CC采取发展数学;学生在CC采取课程学分数学;和学生在四年制大学采取大学代数。人口与扩大数学困难的大学生的STEM参与高度相关，因为它发生在一个高度多样化的社会人口环境中（德克萨斯州休斯顿）。该团队将招收1050名学生（主要是CC发展数学专业的学生），并使用结构方程模型评估认知，数学，情感，动机和人口特征如何交叉的模型。数据分析计划涉及潜在的班级模型，以确定潜在的MLD学生，关键标准是（a）表现出数学弱点，如发展数学入学所示;和（B）该课程的失败。该小组将比较这种识别方法与标准识别模型，特别是低成就和差异模型。开发的模型的验证将涉及MLD和其他数学困难的学生之间的定性比较，通过观察和分析体内的数学表现，使用多模态数据捕获和分析集中在生理反应。该项目由NSF的EHR核心研究（ECR）计划支持。ECR计划强调基础STEM教育研究，产生该领域的基础知识。投资是在关键领域是必不可少的，广泛的，持久的：干学习和干学习环境，扩大参与干，干劳动力发展。该计划支持积累强有力的证据，为理解、建立理论解释、提出干预和创新建议，以应对STEM兴趣、教育、学习和参与方面的持续挑战提供信息。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Many more students are enrolled in community college (CC) than in four-year colleges and universities, and many more students take developmental (remedial) math at CC than at four-year institutions. However, failure rates in these courses are high, and failure of developmental math is a significant barrier to STEM participation. There is surprisingly little data about the specific skill deficits and sociodemographic and personal barriers that contribute to this situation. It is likely that prior education, entry level mathematical skills, motivation, self-regulation, and affective factors contribute, in conjunction with work/family/financial considerations. However, these factors have not been considered together. This project will seek to identify the factors that underlie developmental math failure and how these factors fit together. It will use this information to develop a novel approach to identify mathematics learning disability (MLD). The project then aims to validate this approach by monitoring physiological factors that may qualitatively differentiate MLD from other difficulties. This project aims to enhance theories about how math skills develop and are affected by these factors, and address how to identify MLD at the college level. The results may point to potential avenues to identify and remediate these difficulties, which would be useful for improving student success in college. This project will enroll three types of first-time-in-college students: students in CC taking developmental math; students in CC taking course-credit mathematics; and students in a four-year university taking college algebra. The population is highly relevant to broadening STEM participation among college students with math difficulty, since it takes place within a highly diverse sociodemographic setting (Houston, Texas). The team will enroll 1050 students (primarily those in CC developmental math), and evaluate a model of how cognitive, mathematical, affective, motivational, and demographic characteristics intersect, using structural equation modeling. Data analysis plans involve latent class models to identify potential MLD students, with key criteria being (a) demonstrated math weakness as indicated by enrollment in developmental math; and (b) failure of that course. The team will compare this method of identification with standard identification models, specifically low achievement and discrepancy models. Validation of developed models will involve qualitative comparisons between MLD and other students with math difficulty, by observing and analyzing in-vivo math performance using multimodal data capture and analysis focused on physiological response. This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad, and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in STEM interest, education, learning and participation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.