Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics

ABSTRACT

Comprehensive understanding of complex biological systems necessitates the use of computational models because they facilitate visualisation and interrogation of system dynamics and data-driven analysis. Computational model-based (CMB) activities have demonstrated effectiveness in improving students’ understanding and their ability to use and reason with models. To maximise the effectiveness of computational modelling, this study examined an improved cognitive scaffolding and its impact on student learning of cellular respiration. This scaffolding proposes the predict-observe-revise-explain (PORE) sequence of tasks that explicitly challenge students to revise their predictions and computational models to resolve cognitive conflict. Based on revision work in a CMB activity, a sample of n = 362 undergraduate biology students were categorised into three groups – not expected to revise (NR, n = 109), required-revised (RR, n = 179), and required-did not revise (RDNR, n = 74). Students’ performance in predict, revise, and explain tasks were significantly associated with post-test performance. RR students were more than twice as likely to demonstrate a positive learning gain in the post-test (odds ratio = 2.47) compared to RDNR students. While science education has implicitly acknowledged revision as a critical cognitive process in modelling, this study presents evidence that making revision an explicit cognitive task in a CMB activity supports student learning of a complex biological system.

KEYWORDS:

• Computational model-based activity
• predict-observe-explain (POE) strategy
• undergraduate life sciences education

Acknowledgements

We extend our gratitude to the undergraduate students, graduate student teaching assistants, and professors who supported this work or who took the time to take part in this study. We thank our undergraduate research assistants, Autumn Fluent and Lauren Brickett, for their participation in data preparation, cleaning, and coding. We are also thankful to Dr. Gretchen King and Marius Dongmo for their preliminary analyses on student learning gains.

Disclosure statement

TH is the majority stakeholder in a company, Discovery Collective, Inc., that has proprietary rights to the software used in this project. No promotion of Discovery Collective products to the exclusion of other similar products should be construed.

Ethics statement

This study was approved under the exempt research category by the Institutional Review Board at the University of Nebraska-Lincoln and was conducted in established or commonly accepted educational settings involving normal educational practices.

Additional information

Funding

This work was supported by the National Science Foundation (NSF) Division of Undergraduate Education: Grant Number DUE 1432001. Ideas presented in this manuscript are those of the authors and do not necessarily reflect the views of personnel affiliated with the NSF.

Notes on contributors

Lyrica Lucas

Lyrica Lucas is a Postdoctoral Research Associate in the School of Natural Resources at the University of Nebraska-Lincoln. Dr. Lucas obtained her bachelor’s degree in Physics and Technology Education from the Philippine Normal University, and her M.A. in Physics from National Institute of Physics at the University of the Philippines-Diliman. She received her M.A. in Teaching and Learning as a Fulbright Scholar and Ph.D. in Educational Studies with a focus on science education at the University of Nebraska-Lincoln. Her current research investigates the use of quantitative modelling activities in science learning and aspects of science teacher education that facilitate the use of inquiry-based practices in classroom instruction, discourse, and assessment.

Tomáš Helikar

Tomáš Helikar is an Associate Professor in the Department of Biochemistry at the University of Nebraska-Lincoln. Dr. Helikar holds courtesy appointments as an Associate Professor in the Departments of Computer Science and Engineering, and Pharmacology and Experimental Neuroscience. Dr. Helikar obtained his B.Sc. in Bioinformatics from the Computer Science Department at the University of Nebraska at Omaha, and his Ph.D. from the Department of Pathology and Microbiology at the University of Nebraska Medical Centre. Dr. Helikar joined the University of Nebraska-Lincoln in 2013 as an Assistant Professor after completing a 3-year postdoctoral work in the Department of Mathematics at the University of Nebraska at Omaha. Dr. Helikar's research program centres around multi-scale modelling of the immune system, and technology development in an effort to make computational modelling accessible to anyone, regardless of their computational background. His group develops, Cell Collective, a modelling and simulation software that is now used by many universities and high schools to teach biology through hands-on modelling.

Joseph Dauer

Joseph Dauer is an Associate Professor of Life Science Education at the University of Nebraska-Lincoln. He has a B.S. in Biology/Mathematics from Western Washington University and M.S. and Ph.D. in Ecology from Penn State University. He conducted post-doctoral research on plant population dynamics and student learning in biology through modelling at Michigan State University. Currently his research focus includes teaching and learning of biology through quantitative modelling and an exploration of neurobiology during modelling activities. He teaches Introductory Biology, Ecology, and College Science Teaching at the University of Nebraska-Lincoln.