ABSTRACT

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.