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

该项目旨在通过设计和研究数据科学教育和劳动力发展的教学模型来服务于国家利益。准备进入科学，技术，工程和数学（STEM）相关领域的大学本科生需要有机会学习如何收集，分析和交流复杂的人类生成的数据。该项目为STEM本科生提供了多种机会，在涉及实时复杂数据的高度结构化的动手学习环境中参与这三种基本数据科学实践。在不同的大学学习环境中，学习者都会经历压力，表现为出汗以及呼吸和心率的增加。在这个项目中，STEM本科生将与从事具有挑战性的学习环境的学习者配对。由于每个学习者都面临特定的挑战，每个STEM本科生都必须收集和分析由此产生的人类压力数据。后来，STEM本科生练习将分析的数据传达给收集数据的人。从人类研究伦理的基本准备开始，该项目通过七个数据收集，分析和交流周期推进STEM本科生。该项目的基本目标是指导STEM本科生学习与人类生成的数据相关的三个基本数据科学实践，这一目标与美国国家科学基金会对发展21世纪世纪数据科学劳动力的重视相一致。国家数据科学教育培训议程建议“支持数据科学教学法和课程的设计和开发”。作为直接回应，模拟生理学数据科学模型（SIM-Physio）作为一种教学模型，专注于数据科学实践作为主要成果。计划进入生物学，神经科学，心理学和数据分析等领域的STEM本科生需要积极参与并准确收集和分析人体生理数据。此外，STEM本科生必须学会向公众传达复杂的数据。SIM-Physio数据科学教学模型利用学习者在具有挑战性的学习环境中展示的生理数据，为STEM本科生从事三种数据科学实践做好准备-收集，分析和交流人类生理数据。在这个数据科学模型中，STEM本科生将学习和实践他们对人体生理数据的收集和分析（即，心率，心率变异性，血压，身体运动和呼吸率）从学习者在一个学期的七个点（基线，加上六个不同的模拟）。学习向外行传达科学数据至关重要;因此，这种教学模式进一步挑战STEM本科生实践将这些数据传达给收集数据的学习者。这种数据科学教学模型的开发和完善对STEM本科生的准备和向多个生物医学培训项目和设施扩散的机会具有影响。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF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。

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.