<p dir="ltr">In 2011, the American Association for the Advancement of Science (AAAS) published a report titled “Vision & Change: A Call to Action” that called for reform in undergraduate biology education. The report proposed core competencies that educators should target so students are graduating ready to tackle 21st-century challenges. Of these core competencies is the ability to reason quantitatively, which includes graphing. However, undergraduate biology students struggle with applying essential graph knowledge. The following dissertation project addresses these challenges by exploring two graphing tasks: constructing versus evaluating graphs. We primarily focused on introductory biology students' reasoning practices in applying graph knowledge between these two tasks. As such, we used a digital performance-based assessment tool, <i>GraphSmarts</i>, to analyze students' graphing choices and their justifications in an ecology-based scenario. Chapter 2 discusses the findings of these analyses (n=301), which revealed a disconnect in graph knowledge application between students' graph construction and evaluation skills. While students tend to create basic bar graphs when constructing graphs, they prefer more sophisticated representations, such as bar graphs with averages and error bars, during evaluation tasks—suggesting that the framing of a task influences students' application of graph knowledge between their recognition of effective data representation and their ability to produce such graphs independently. While insightful, we needed to explore ‘why’ this variation exists. Chapter 3 explores the root of this variation through student interviews (n=12). Students would complete the two tasks, followed by questions that help clarify their thought processes. Through the lens of the Conceptual Dynamics framework and the Dynamic Mental Construct model, the study identified two critical cognitive patterns, ‘mode-switching’ and ‘mode-stability.’ Results reaffirm the context-dependent nature of students' graphing knowledge and the influence of task framing on their reasoning processes, as seen in Chapter 2. Results from this project can inform recommendations that biology educators can consider, including 1) having students conduct multiple types of graphing tasks beyond construction, 2) teaching statistical features more explicitly by integrating them into course content, and 3) encouraging students to reflect on their graphing practices. That would be expected to address these instructional needs and foster characteristics of quantitative reasoning and graphing that transfer out of biology. Future directions on this work include exploring other standard graphing tools (Excel, R studio) on graph knowledge, examining the transferability of graphing skills across biological sub-disciplines, and developing targeted interventions for gaps in students' graphing competencies across various graphing tasks. Overall, the work contributes toward developing evidence-based instructional strategies that will be supportive in cultivating competent, robust quantitative reasoning and graphing skills among undergraduate biology students.</p>