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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. 

Abstract Metacognitive calibration—the capacity to accurately self-assess one’s performance—forms the basis for error detection and self-monitoring and is a potential catalyst for conceptual change. Limited brain imaging research on authentic learning tasks implicates the lateral prefrontal and anterior cingulate brain regions in expert scientific reasoning. This study aimed to determine how variation in undergraduate life sciences students’ metacognitive calibration relates to their brain activity when evaluating the accuracy of biological models. Fifty undergraduate students enrolled in an introductory life sciences course completed a biology model error detection task during fMRI. Students with higher metacognitive calibration recruited lateral prefrontal regions linked in prior research to expert STEM reasoning to a greater extent than those with lower metacognitive calibration. Findings suggest that metacognition relates to important individual differences in undergraduate students’ use of neural resources during an authentic educational task and underscore the importance of fostering metacognitive calibration in the classroom. 

Quantitative reasoning (QR) is the ability to apply mathematics and statistics in the context of real-life situations and scientific problems. It is an important skill that students require to make sense of complex biological phenomena and handle large datasets in biology courses and research as well as in professional contexts. Biology educators and researchers are responding to the increasing need for QR through curricular reforms and research into biology education. This qualitative study investigates how undergraduate biology instructors implement QR into their teaching. The study used pedagogical content knowledge (PCK) and a QR framework to explore instructors’ instructional goals, strategies, and perceived challenges and affordances in undergraduate biology instruction. The participants included 21 biology faculty across various institutions in the United States, who intentionally integrated QR in their instruction. Semi-structured interviews were used to collect data focusing on participants’ beliefs, experiences, and classroom practices. Findings indicated that instructors adapt their QR instruction based on course level and student preparedness. In lower-division courses, strategies emphasized building foundational skills, reducing math anxiety, and using scaffolded instruction to promote confidence. In upper-division courses, instructors expected greater math fluency but still encountered a wide range of student abilities, prompting a focus on correcting misconceptions in integrating math knowledge and fostering deeper conceptual understanding in biology. Many instructors reported that their personal and educational experiences, especially struggles with math, often shaped their inclusive and empathetic teaching practices. Additionally, instructors’ research backgrounds influenced instructional design, particularly in the use of authentic data, statistical tools, and real-world applications. Instructors’ teaching experiences led to refinement in lesson planning, pacing, and active learning strategies. Despite their efforts, instructors faced both internal and external challenges in implementing QR, including discomfort with teaching math, time limitations, student resistance, and institutional barriers. However, affordances such as departmental support, interdisciplinary collaboration, and curricular flexibility helped to overcome some of these challenges. This study highlights the complex relationships between instructors’ experiences, beliefs, and contextual factors in shaping QR instruction. This calls for professional development that supports reflective practice, builds interdisciplinary competence, and promotes instructional strategies that bridge biology and mathematics and will help instructors design a learning environment that better support students’ development of QR skills. These findings offer valuable guidance for professional development aimed at helping biology instructors incorporate quantitative reasoning into their teaching. Such efforts can better equip students to meet the quantitative demands of modern biology and promote their continued engagement in STEM fields through more inclusive and integrated instructional approaches.