How are students introduced to science in postsecondary settings? The decisions that instructors make about course design and instruction send messages to students about what it means to be a scientist—and about who can or should take up that professional role. Importantly, these pedagogical choices have implications for whether and how marginalized communities are considered (or not considered) part of the discipline. Because it enables identification of performance and outcome disparities, learning analytics provides instructors with opportunities to better understand how current instructional practices reinforce—or counteract—inequalities between student subgroups. For this reason, critical engagements with classroom data provide a basis on which more inclusive and equitable classrooms can be built. However, there are challenges to using classroom and student data to inform course (re)design and institutional-level decisions about student learning. How can disciplinary leaders collect, analyze, and present student data in ways that do not (un)intentionally perpetuate deficit models of students or reify stereotypes about race, gender, class, or disability? In this talk, multiple approaches are presented to demonstrate how data projects across multiple scales (e.g., from one classroom to 10 universities) might better build and support inclusive, equitable, and socially just learning in introductory science courses.
Results/Conclusions
Initial results will be presented from a subset of projects from institutional-level programs designed to support student success in introductory courses—including the University of Michigan’s Foundational Course Initiative (FCI) and the Sloan Equity and Inclusion in STEM Introductory Courses (SEISMIC) project. These projects highlight different ways that instructors, departments, and institutions can begin (and continue) to use student data to design introductory courses that are more inclusive and equitable. These approaches include a) using mixed-methods, so that qualitative data strengthen and account for limitations of quantitative data, b) measuring affective aspects of student learning in addition to student performance, and c) examining underlying assumptions of constructs, study design, and data collection, analysis, and reporting. Lessons from projects will also demonstrate how critical frameworks (e.g., critical race theory, gender studies, disability studies, intersectionality theory, decolonial studies) can be applied to the design, analysis, and presentation of science education research and classroom assessments in ways that build a more inclusive ecology education.