Leveraging Machine Learning to Explore the Effects of the Design2Data Course-based Undergraduate Research Experience

This project aims to serve the national interest by engaging students in the Design to Data (D2D) program, a nationally networked biochemistry Course-based Undergraduate Research Experience (CURE). The project will help to determine which parts of the student experience positively impact graduation and access to STEM careers. D2D engages students in exploring protein science through a cutting-edge computationally driven research project. This project anchors the curriculum across a network of highly motivated, early-adopter faculty members teaching students in varied STEM disciplines and levels. The hands-on D2D learning experience prepares students for success in this new era of biology while crowdsourcing data collection for improved protein modeling artificial intelligence methods. The large, diverse D2D student body offers a rich opportunity to explore novel, machine learning-based assessment methods for CURE education research. In reaching the aims of this grant, this project will (a) pioneer a cutting-edge approach to investigating student learning in research experiences and will make these data analysis methods broadly accessible to STEM education researchers, and (b) create equitable, meaningful research experiences for thousands of students, many of whom would not have otherwise had the opportunity. D2D’s undergraduate research project anchors the program, and D2D faculty network members facilitate its implementation by integrating the project into their classes on a wide variety of campuses across the United States. Participating students functionally characterize novel enzyme mutants generating data for protein modeling stakeholders to explore with the goal of developing better functionally predictive tools for more rapid solutions to human-centered problems. Not only is there potential to meaningfully advance science through the program, but the experience enables equitable access to cutting-edge biotechnology training that is in high demand by employers. Reaching many students with this program is tractable: D2D readily integrates into lab practicum settings across the disciplines and from first-year to senior-level classes. The network includes forty institutions and will engage approximately 4,000 students over the funding period. The project objective is to use this large, diverse population in assessing mediators to psycho-social and behavioral outcomes linked to STEM persistence by collecting multi-level motivational data at scale with layered variables and benchmark cutting edge machine learning methods for the data analysis. The project activities will (a) support dedicated network continuity coordination to maintain current levels of faculty participation, and (b) assessment activities to plan and execute comprehensive data collection and machine learning (ML)-based analysis that captures and evaluates a deep set of discrete CURE-implementation variables. From these activities, meaningful professional development beyond the D2D Network will be promoted by making the cutting-edge student learning data analysis methods accessible to other education researchers. Finally, this project will put research into hands of thousands of students and enable more equitable access to CUREs, increasing the diversity of students participating in research. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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.