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The field of anatomy is often seen by nonanatomists as concerned primarily with the tasks of locating, naming, and describing structures; these tasks, in turn, are often assumed to require only lower-order cognitive skills (LOCSs), i.e., the Knowledge or Comprehension levels of Bloom's taxonomy. Many nonanatomists may thus believe that studying anatomy does not develop transferable higher-order cognitive skills. Published lists of anatomy learning objectives (LOs) might reinforce this view by focusing attention on numerous details of specific structures and regions. To explore this issue further, we have analyzed the structure of published peer-reviewed LOs by characterizing their organization (single-tiered or multi-tiered), inclusion of function, use of action verbs, and dependence on or independence of context. Our results suggest that previously published LO lists, despite their value, may not fully showcase opportunities for students to develop higher-order skills. In the hope of stimulating further discussion and scholarship, we present here a two-tiered framework of human anatomy competencies, i.e., generalizable skills beyond straightforward recognition and memorization. This framework, which is intended to be both student-facing and faculty-facing, illustrates how anatomy courses may be reframed as opportunities to think critically and develop sophisticated, professionally relevant skills.

The underrepresentation and underperformance of low-income, first-generation, gender minoritized, Black, Latine, and Indigenous students in Science, Technology, Engineering, and Mathematics (STEM) occurs for a variety of reasons, including, that students in these groups experience opportunity gaps in STEM classes. A critical approach to disrupting persistent inequities is implementing policies and practices that no longer systematically disadvantage students from minoritized groups. To do this, instructors must use data-informed reflection to interrogate their course outcomes. However, these data can be hard to access, process, and visualize in ways that make patterns of inequities clear. To address this need, we developed an R-Shiny application that allows authenticated users to visualize inequities in student performance. An explorable example can be found here: https://theobaldlab.shinyapps.io/visualizinginequities/. In this essay, we use publicly retrieved data as an illustrative example to detail 1) how individual instructors, groups of instructors, and institutions might use this tool for guided self-reflection and 2) how to adapt the code to accommodate data retrieved from local sources. All of the code is freely available here: https://github.com/TheobaldLab/VisualizingInequities. We hope faculty, administrators, and higher-education policymakers will make visible the opportunity gaps in college courses, with the explicit goal of creating transformative, equitable education through self-reflection, group discussion, and structured support.