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

就读社区学院（CC）的学生比就读四年制学院和大学的学生要多得多，就读社区学院（CC）的学生比就读四年制大学的学生要多得多。然而，这些课程的不及格率很高，发展数学的不及格是参与STEM的重大障碍。令人惊讶的是，关于导致这种情况的具体技能缺陷、社会人口和个人障碍的数据很少。之前的教育、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/经济方面的考虑，都可能起作用。然而，这些因素并没有被放在一起考虑。该项目将试图确定导致发展性数学失败的因素，以及这些因素如何相互作用。它将利用这些信息来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在定性上将MLD与其他困难区分开来的生理因素来验证这种方法。本项目旨在加强关于数学技能如何发展和受这些因素影响的理论，并解决如何在大学层面识别MLD的问题。研究结果可能会指出识别和纠正这些困难的潜在途径，这将有助于提高学生在大学里的成功。本项目将招收三种类型的第一次进入大学的学生：在CC学习发展数学的学生；CC的学生修课程学分数学；还有四年制大学的学生上大学代数课。人口与扩大数学困难大学生的STEM参与高度相关，因为它发生在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班级模型，以识别潜在的MLD学生，主要标准是(a)通过发展数学的注册显示出数学弱点；(b)这门课程的失败。该团队将把这种识别方法与标准识别模型，特别是低成就和差异模型进行比较。对已开发模型的验证将涉及MLD和其他有数学困难的学生之间的定性比较，通过观察和分析体内数学表现，使用多模态数据捕获和分析侧重于生理反应。本项目由美国国家科学基金会EHR核心研究（ECR）项目支持。ECR项目强调在该领域产生基础知识的基础STEM教育研究。投资在至关重要、广泛和持久的关键领域：STEM学习和STEM学习环境，扩大STEM参与，以及STEM劳动力发展。该项目支持积累有力的证据，为理解、构建理论进行解释提供信息，并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。该奖项反映了美国国家科学基金会的法定使命，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.