Author(s):
Need: Cell Collective is biomedical research-grade life sciences modeling and simulation software that makes computational modeling of complex biological processes accessible to users regardless of their prior modeling experience (Helikar, 2021; Helikar et al., 2012; Helikar et al., 2015). In the course of developing and implementing our simulation and modeling lessons, we found mounting evidence of the effectiveness of simulation and modeling for promoting student learning (Helikar et al., 2012; Cutucache & Helikar, 2014; Helikar et al., 2015, Bergan-Roller et al. 2018a; Bergan-Roller et al. 2018b). As a result, we have developed and deployed computational modeling lessons to cover several biological processes such as cell respiration, gene regulation, purine biosynthesis, photosynthesis, cell cycle, and glucose homeostasis (Booth et al., 2021; Cutucache & Helikar, 2014; Bergan-Roller et al., 2017; Bergan-Roller et al., 2018a; Crowther et al., 2017; Crowther et al., 2018). Recently, we have begun studying the challenges that instructors face when adopting modeling-based lessons, including the difficulty transitioning away from lectures that encourage student memorization of static diagrams of biological processes and into the exploration of dynamic computational models with changing conditions and system interactions.
Guiding Question: This project aims to identify and implement factors that impede and accelerate pedagogical transformation via interactive modeling software at diverse institutions of higher education and then leverage this knowledge to propagate transformative teaching in life sciences education. Hence, our guiding question is: What factors impact and motivate instructors to effectively enact computational modeling and simulation in undergraduate life science courses?
Outcomes: A key outcome of this project is an increasing user base resulting from implementing findings (i.e., developing new features in Cell Collective to implement influencing instructors’ adoption and continued use of the software) derived from the guiding question. Because of this project, an increasing number of instructors are using modeling and simulation to support students in developing a deeper understanding of biological processes. Improved student learning can be considered another critical outcome (albeit indirectly through the increased number of instructors) of this project.
Broader impacts: At the faculty level, we expect instructors to be better prepared to embrace the shift toward more quantitative and systems-level thinking in life sciences. By focusing on barriers to teaching improvement that encompass institutional policy and technological factors, our findings will help others who are encountering hurdles address such issues proactively in their implementation efforts. At the student level, students will be equipped to transfer their understanding of our modeling and simulation learning approach to learning other disciplines because the behaviors of complex systems are readily transferable between subject areas. We also expect our approach will improve students’ interest in STEM careers by actively engaging students in doing science.
Note: A list of references cited in the proposal will be provided upon request.
Coauthors
Thomas Helikar, University of Nebraska-Lincoln, Nebraska; Wendy Smith, University of Nebraska-Lincoln, Nebraska; Resa Helikar, University of Nebraska-Lincoln, Nebraska
