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• W3006966081 abstract "Abstract Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of biochemical networks. However, current approaches are not optimal to develop the needed mechanistic understanding, and misconceptions about biological processes persist even after graduation. To address these issues, we developed a computational modeling and simulation approach that employs scaffolded learning to teach biochemistry students about the regulation of metabolism. The power of the approach lies in students’ abilities to alter any component or connection in a modeled system and instantly observe the effects of their changes. We find that students who use our approach perform better on biochemistry metabolism questions compared to students in a course that did not use this approach. We also investigated performance by gender and found that our modules may have the potential to increase equity in education. We noted that students are generally positive about the approach and appreciate its benefits. Our modules provide life science instructors with a dynamic and systems-driven approach to teach metabolic regulation and control that improves learning and also equips students with important technical skills." @default.
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• W3006966081 date "2020-02-19" @default.
• W3006966081 modified "2023-09-27" @default.
• W3006966081 title "Using computational modeling to teach metabolism as a dynamic system improves student performance" @default.
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• W3006966081 doi "https://doi.org/10.1101/2020.02.18.953380" @default.
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