Collaborative Research: Improving Undergraduate Student Success in Introductory STEM Courses Via Campus Data Systems and Targeted Support for Self-Regulated Learning

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This collaborative project includes investigators at the University of North Carolina at Chapel Hill (Award DUE-1821594), the University of Nevada at Las Vegas (UNLV; Award DUE-1821601), and the College of Southern Nevada. The United States has an ongoing need for more STEM professionals. College students who initially major in STEM cite the coursework as a major reason for leaving STEM to pursue other interests. Instructors who move away from lectures to more engaging kinds of instruction find that their students are more likely to stay in STEM majors, but only when the students know how to learn in these new environments. Unfortunately, many students have simply not experienced engaging instruction and therefore have not developed the knowledge and skills to take full advantage of it. This project will develop models to identify struggling students in introductory STEM courses (especially biology and anatomy and physiology) and will test interventions to help these students gain the knowledge and skills they need to benefit from active-learning course formats. This work should provide knowledge that can be used to increase students' success and retention in the courses under study, and should inform similar interventions in other STEM courses.In this project, the investigators will combine: 1) identifying struggling students with an existing data-driven, web-based approach for early identification and 2) support of struggling college students with a robust initiative focused on retaining students who traditionally have not persisted in STEM fields. Specifically, a previous project at UNLV, Learning Theory and Analytics as Guides to Improve Undergraduate STEM Education (LearningTAGs), has developed a data-driven approach for identifying and directly intervening with struggling students. In addition, the Finish Line Project at UNC-Chapel Hill has found that first-generation college students benefit most from early intervention, accessible academic coaches, and active-learning STEM classrooms. The LearningTAGs methods can be expanded to better serve struggling students by integrating findings from the Finish Line Project. The researchers will (1) develop and test UNLV's LearningTAGs prediction modeling and digital intervention at UNC (another highly selective, public institution) as well as at the College of Southern Nevada (an open enrollment two-year college); (2) leverage Finish Line Project findings about academic coaching to test various support interventions (i.e. online self-regulated learning instructional modules, academic coaching, and supplemental instruction); and (3) identify whether support efficacy varies across different groups of students, including students from groups that are underrepresented in STEM and first-generation college students. The campus data infrastructure and student support platform that is tested and refined in this project should provide a model that can be replicated at other colleges and universities, using the universities' existing data from learning management systems.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.