Innovating Life Sciences Education Through Computational Modeling and Simulations

本项目旨在通过提高学生建模和定量技能的训练，服务于优秀的本科STEM教育的国家利益。从广告到生命科学研究的劳动力部门都淹没在数据中，缺乏具有批判性思维和技术技能的员工来管理、处理、分析和解释数据。《本科生物学教育的愿景与变化》呼吁增加技术和系统生物学的培训，反映了生命科学研究向结合数学和计算机科学的转变。传统方法不适用于复杂学科的教学。因此，生命科学教育的发展，使学生具备机械和定量推理的技能，并回答新出现的生命科学问题，变得越来越重要。该项目的长期目标是通过使生物学学生能够使用计算建模来获取和应用科学知识，从而改变他们学习复杂生命系统的方式。该项目将建立在以前nsf资助的工作的成功基础上，这些工作产生了一种新的基于模拟和建模的方法来学习复杂的生物过程。通过Cell Collective软件，无论设置或先前的建模经验如何，任何学生和教师都可以访问计算建模，从而促进了这项工作。该项目将扩展先前涉及Cell Collective的工作，重点是培训和吸引教师在本科生中使用Cell Collective。该项目将开发一个框架，以了解教师通过Cell Collective向学生部署计算建模和模拟课程的障碍和影响，这些学生来自不同的学生群体。通过Cell Collective软件中以用户为中心的新功能，教学和学习挑战将同时得到解决并最小化。已经确定的功能示例包括技术的可访问性，以及学生跨不同生命科学课程的建模和仿真课程的简化访问。此外，该项目将为50-80名教师提供如何克服集成计算建模的挑战以及如何在课堂上实施这些工具的专业发展。预计参与该项目的教师将更好地准备支持向更定量和系统层次的生命科学教育的转变。我们还期望，随着时间的推移，广泛采用Cell Collective来教授本科生命科学主题，将有助于改变生物学教学，使学生更好地为进入现代数据驱动的劳动力做好准备。因此，该项目具有支持美国创新和经济增长的潜力。该项目开发的资源将提供给有兴趣将计算建模纳入自己的学习技术和方法的研究人员和教师。本项目由美国国家科学基金会改善本科STEM教育计划：教育与人力资源项目支持，该项目支持研究和开发项目，以提高所有学生STEM教育的有效性。通过参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，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 in excellent undergraduate STEM education by improving students' training in modeling and quantitative skills. Workforce sectors from advertising to life science research are drowning in data and lack employees with the critical thinking and technical skills to manage, process, analyze, and interpret it. Vision and Change in Undergraduate Biology Education, calls for an increase in technical and systems biology training, reflecting the shift in life science research to incorporate mathematics and computer science. Traditional methods are inadequate for teaching complex subject matter. Thus, it is increasingly important for life sciences education to evolve to equip students with skills to reason mechanistically and quantitatively, and to answer emerging life science questions. The long-term goal of this project is to transform the way biology students learn about complex living systems by enabling them to use computational modeling, both to acquire and to apply scientific knowledge. The project will build on successes from previous NSF-funded efforts that resulted in a new simulation- and modeling-based approach to learning about complex biological processes. This effort is facilitated through Cell Collective software that makes computational modeling accessible to any student and instructor, regardless of setting or prior modeling experience. This project will extend previous work involving Cell Collective by focusing on training and engaging instructors in its use with undergraduate students.The project will develop a framework for understanding the barriers and impact of instructors deploying computational modeling and simulation lessons via Cell Collective to students across institutions with demographically diverse student populations. Teaching and learning challenges will be simultaneously addressed and minimized through new user-centric features in the Cell Collective software. Examples of features already identified include accessibility of the technology and streamlined access for students to modeling and simulation lessons across different life science courses. Furthermore, this project will provide professional development for 50-80 instructors about how to overcome challenges to integrating computational modeling and how to implement these tools in the classroom. It is expected that instructors who participate in the project will be better prepared to support the shift toward more quantitative and systems-level education in life sciences. It is also expected that the broad adoption of Cell Collective for teaching undergraduate life science topic will, over time, help to transform biology instruction so that it better prepares students to enter the modern data-driven workforce. The project thus has the potential to support United States innovation and economic growth. Resources developed in the project will be made available to researchers and instructors interested in incorporating computational modeling into their own learning technologies and methodologies. This project is supported by the NSF Improving Undergraduate STEM Education Program: Education and Human Resources, which 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.