The Simulation Physiology Data Science Model: Engaging STEM Undergraduates in Data Science Practices

This project aims to serve the national interest by designing and studying an instructional model in data science education and workforce development. College undergraduates preparing to enter science, technology, engineering, and mathematics (STEM)-related fields need opportunities to learn how to collect, analyze, and communicate about complex, human-generated data.

This project provides STEM undergraduates with multiple opportunities to engage with these three basic data science practices, in a highly structured, hands-on learning environment that involves real-time, complex data. Learners in different collegiate learning environments experience stress, indicated by perspiration and increases in respiration and heart rate.

In this project, STEM undergraduates will be paired with learners engaged in a challenging learning environment. As each learner experiences specific challenges, each STEM undergraduate must collect and analyze the resulting human stress data. Later, the STEM undergraduate practices communicating the analyzed data back to the person from whom it was collected.

Beginning with basic preparation in human research ethics, this project then advances STEM undergraduates through seven data collection, analysis, and communication cycles. This project’s fundamental objective is guiding STEM undergraduates in learning three basic data science practices associated with human-generated data, an objective that aligns with the National Science Foundation’s emphasis on developing the 21st century data science workforce.

The National Data Science Education & Training agenda suggests “Support(ing) the Design and Development of Data Science Pedagogy and Curricula”. In direct response, the Simulation Physiology Data Science Model (SIM-Physio) serves as a pedagogical model focused on data science practices as the primary outcome. STEM undergraduates planning to enter the fields including Biology, Neuroscience, Psychology, and Data Analytics will need to actively engage and accurately collect and analyze human physiological data.

Additionally, STEM undergraduates must learn to communicate complex data to the general public. The SIM-Physio Data Science pedagogical model harnesses the physiological data that learners exhibit in a challenging learning environment to prepare STEM undergraduate students to engage in three data science practices – collecting, analyzing, and communicating human physiological data.

In this data science model, STEM undergraduates will learn and practice their collection and analysis of human physiological data (i.e., heart rate, heart rate variability, blood pressure, physical movement, and respiration rates) from learners at seven points in a semester (baseline, plus six different simulations). Learning to communicate scientific data to laypersons is critical; therefore, this pedagogical model further challenges STEM undergraduates to practice communicating those data back to learners from whom the data were collected.

Development and refinement of this data science pedagogical model holds implications for STEM undergraduate preparation and opportunities for diffusion to multiple biomedical training programs and facilities. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the 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.