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IntroductionModels are a primary mode of science communication and preparing university students to evaluate models will allow students to better construct models and predict phenomena. Model evaluation relies on students’ subject-specific knowledge, perception of model characteristics, and confidence in their knowledge structures. MethodsFifty first-year college biology students evaluated models of concepts from varying biology subject areas with and without intentionally introduced errors. Students responded with ‘error’ or ‘no error’ and ‘confident’ or ‘not confident’ in their response. ResultsOverall, students accurately evaluated 65% of models and were confident in 67% of their responses. Students were more likely to respond accurately when models were drawn or schematic (as opposed to a box-and-arrow format), when models had no intentional errors, and when they expressed confidence. Subject area did not affect the accuracy of responses. DiscussionVariation in response patterns to specific models reflects variation in model evaluation abilities and suggests ways that pedagogy can support student metacognitive monitoring during model-based reasoning. Error detection is a necessary step towards modeling competence that will facilitate student evaluation of scientific models and support their transition from novice to expert scientists. 

As biological science rapidly generates new knowledge and novel approaches to address increasingly complex and integrative questions, biology educators face the challenge of teaching the next generation of biologists and citizens the skills and knowledge to enable them to keep pace with a dynamic field. Fundamentally, biology is the science of living systems. Not surprisingly, systems is a theme that pervades national reports on biology education reform. In this essay, we present systems as a unifying paradigm that provides a conceptual framework for all of biology and a way of thinking that connects and integrates concepts with practices. To translate the systems paradigm into concrete outcomes to support instruction and assessment in the classroom, we introduce the biology systems-thinking (BST) framework, which describes four levels of systems-thinking skills: 1) describing a system’s structure and organization, 2) reasoning about relationships within the system, 3) reasoning about the system as a whole, and 4) analyzing how a system interacts with other systems. We conclude with a series of questions aimed at furthering conversations among biologists, biology education researchers, and biology instructors in the hopes of building support for the systems paradigm.