About

Dr. Rosemary Russ is an Associate Professor in the Department of Curriculum and Instruction at the University of Wisconsin–Madison. She earned her Ph.D. in Physics from the University of Maryland in 2006, working with the Physics Education Research Group to explore K–16+ student science learning. Her research focuses on examining who and what counts as knowledge in different contexts and with different populations, using the lens of epistemology, specifically epistemic authority and epistemic injustice, to theorize about how participants interact around knowledge and knowledge claims. Following her doctoral work, she spent five years in the Learning Sciences Program at Northwestern University as a postdoctoral fellow and Research Assistant Professor, modeling teacher and student cognition in science and mathematics. Since joining UW Madison in 2012, she has designed and studied learning environments to support pre-service elementary teachers in equitably attending to their students’ science ideas. Her work also extends to community engagement, notably with the statewide organization EXPO, which aims to educate about the harms of the carceral system and advocate for policy change. She coordinates a tutoring program in the Dane County Jail and investigates how education programs for system-impacted individuals understand and disrupt systemic inequities. Her research has been funded by organizations including the National Science Foundation and the Spencer Foundation.

Research topics

• Machine Learning
• Computer Science
• Artificial Intelligence
• Software engineering
• Sociology
• Database
• Psychology
• Philosophy
• Epistemology
• Mathematics education
• Cognitive psychology

Selected publications

• Mechanistic Reasoning In-School Versus Mechanistic Reasoning In-Life

  Contributions from science education research · 2026-01-01

  book-chapter
  PublisherDOI

• Keeping our Research Plumb: Theory-Driven Design and Analysis for the Study of Instructor Epistemologies

  Journal of Chemical Education · 2025-11-11

  articleOpen access

  The field of chemistry education is increasingly interested in thinking beyond what content our courses explicitly teach and toward thinking about implicit ways of knowing and learning (i.e., epistemologies) our courses elevate. Previous scholarship gives us reason to believe that studying epistemologies in undergraduate chemistry will provide us with important insight into how students navigate and learn in our courses. However, we are only beginning to explore the ways in which instructors' goals for knowing and learning shape (or not) students' experiences in our courses. If we want to make progress in understanding student and instructor epistemologies, then we must design research in a way that matches our theoretical model of its ontology. To assist the community in considering how theory might drive design and analysis in studies related to epistemology, we trace decisions we made while exploring the dynamics underlying instructors' epistemology around assessment. This coherence-seeking journey illustrates that alignment between theoretical commitments and study activities is far from straightforward. Additionally, our experiences suggest that it may be generative for members of our community to unpack assumptions undergirding their theoretical framework and explore how these show up across study activities.

  PublisherDOI

• Designing Research to Capture and Understand Variations in Higher Education in Prison

  The Prison Journal · 2025-09-24

  article1st authorCorresponding

  Higher Education in Prison (HEP) has expanded dramatically due to its success in effecting change in students, their families, and institutions. However, in demonstrating its effectiveness, research has lumped together a vast array of courses into a single construct of HEP. In this conceptual article, we encourage the discipline to think beyond this HEP monolith in order to capture and understand the variation that exists within HEP. We offer three ways to distinguish between HEP courses—by structure, student experience, and teaching practices. We argue that understanding how and why our courses achieve the positive outcomes requires systematic attention to teaching practices.

  PublisherDOI

• Modeling Student Negotiation of Assessment‐Related Epistemological Messages in a College Science Course

  Science Education · 2024-11-02 · 13 citations

  articleOpen access

  ABSTRACT To prepare students to use science knowledge in their later personal or professional lives, we must attend to what they believe it means to know and learn science (i.e., epistemology). Unfortunately, we have little understanding of how students' epistemologies shift and are stabilized as they navigate their science courses. Researchers have made intuitive arguments that many microscale epistemological messages sum over time to give rise to macro‐scale understandings of knowing and learning, but we have no theoretical model for how this sum unfolds. Here, we begin to build such a theoretical model. To do so, we focus on assessments and related materials in a college chemistry course as potentially consequential sources of messages about valued knowledge products and processes. We then elicited students' evolving understandings of assessment‐related epistemological messages in several one‐on‐one interviews conducted throughout the semester. Analysis of how three students experienced, negotiated, and responded to assessment‐related messages showed that interactions with the course system stabilized a consistent, well‐resolved picture of the ways of knowing and learning that counted in the focal course. Specifically, good knowledge must have specific authority‐mandated features and knowledge is justified primarily via alignment with an instructor‐authored key. Students found utility in different (reliable) processes for achieving the aim of authorized knowledge, and some of these differences were maintained throughout the semester. Implications for modeling students' experience with course‐embedded epistemological messages over time and how this work might inform practice are discussed.

  PublisherOA PDFDOI

• Beliefs <i>versus</i> resources: a tale of two models of epistemology

  Chemistry Education Research and Practice · 2023 · 11 citations

  • Sociology
  • Epistemology
  • Sociology

  Compelling evidence, from multiple levels of schooling, suggests that teachers’ knowledge and beliefs about knowledge, knowing, and learning ( i.e. , epistemologies) play a strong role in shaping their approaches to teaching and learning. Given the importance of epistemologies in science teaching, we as researchers must pay careful attention to how we model them in our work. That is, we must work to explicitly and cogently develop theoretical models of epistemology that account for the learning phenomena we observe in classrooms and other settings. Here, we use interpretation of instructor interview data to explore the constraints and affordances of two models of epistemology common in chemistry and science education scholarship: epistemological beliefs and epistemological resources. Epistemological beliefs are typically assumed to be stable across time and place and to lie somewhere on a continuum from “instructor-centered” (worse) to “student-centered” (better). By contrast, a resources model of epistemology contends that one's view on knowledge and knowing is compiled in-the-moment from small-grain units of cognition called resources . Thus, one's epistemology may change one moment to the next. Further, the resources model explicitly rejects the notion that there is one “best” epistemology, instead positing that different epistemologies are useful in different contexts. Using both epistemological models to infer instructors’ epistemologies from dialogue about their approaches to teaching and learning, we demonstrate that how one models epistemology impacts the kind of analyses possible as well as reasonable implications for supporting instructor learning. Adoption of a beliefs model enables claims about which instructors have “better” or “worse” beliefs and suggests the value of interventions aimed at shifting toward “better” beliefs. By contrast, modeling epistemology as in situ activation of resources enables us to explain observed instability in instructors’ views on knowing and learning, surface and describe potentially productive epistemological resources, and consider instructor learning as refining valuable intuition rather than “fixing” “wrong beliefs”.

  PublisherDOI

• An approachable, flexible, and practical machine learning workshop for biologists

  bioRxiv (Cold Spring Harbor Laboratory) · 2022 · 2 citations

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  ABSTRACT The increasing prevalence and importance of machine learning in biological research has created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible, or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment, or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around 3 principles: (a) focusing on preparedness over fluency or expertise, (b) necessitating minimal coding and mathematical background, and (c) requiring low time investment. It incorporates active learning methods and custom open source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on 3 workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge, and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provides valuable insight into tailoring educational resources for active researchers in different domains. The workshop materials are available from https://carpentries-incubator.github.io/ml4bio-workshop/ and the ml4bio software is available from https://github.com/gitter-lab/ml4bio .

  PublisherOA PDFDOI

• An approachable, flexible and practical machine learning workshop for biologists

  Bioinformatics · 2022 · 4 citations

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

  SUMMARY: The increasing prevalence and importance of machine learning in biological research have created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around three principles: (i) emphasizing preparedness over fluency or expertise, (ii) necessitating minimal coding and mathematical background and (iii) requiring low time investment. It incorporates active learning methods and custom open-source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on three workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provide valuable insight into tailoring educational resources for active researchers in different domains. AVAILABILITY AND IMPLEMENTATION: Workshop materials are available at https://github.com/carpentries-incubator/ml4bio-workshop and the ml4bio software is available at https://github.com/gitter-lab/ml4bio. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

  PublisherOA PDFDOI

• Student epistemological framing on paper-based assessments

  Physical Review Physics Education Research · 2020-07-07 · 14 citations

  articleOpen access

  Physics instructors often think of assessment as occurring after instruction, however, students get information from assessments that influence their understandings of how they should engage in physics learning.

  PublisherOA PDFDOI

• Making sense of carbon footprints: how carbon literacy and quantitative literacy affects information gathering and decision-making

  Environmental Education Research · 2019-03-11 · 13 citations

  articleSenior author

  Popular media often reports on the carbon footprint of certain activities, items or people. We were curious to explore how people make sense of these news pieces, and specifically, whether and how carbon literacy (CL) and quantitative literacy (QL) influences their reasoning. We interviewed and surveyed students of various backgrounds using simulated news pieces of three carbon footprints: that of Facebook, that of the US dairy industry, and that of the US chocolate industry. We found that being highly carbon or quantitatively literate influenced participants’ reaction – but only while they were gathering information about the prompts. The effect of literacies disappeared when they were asked to decide whether the carbon footprint was worrisome or which they would tackle first as a policy-maker. We describe and categorize the strategies students used to make sense of carbon footprints, and link the frequency of using particular strategies to their carbon and quantitative literacy. Implications for future research and environmental education are discussed.

  PublisherDOI

• Supporting the Scientific Practices through Epistemologically Responsive Science Teaching

  Journal of Science Teacher Education · 2019-11-25 · 44 citations

  article

  Meaningfully engaging students in the NGSS scientific practices requires that student ideas become the driving force of classroom activity. However, in order for student ideas to take on this new role, teachers must engage in responsive teaching in which they elicit, notice, and respond to the substance of student thinking. In this work, we explore a variety of types of responsive teaching and elaborate a specific type of responsive teaching—what we call epistemologically responsive science teaching. Using arguments drawn from existing literature on how to support the scientific practices, we present a theoretical argument that epistemologically responsive science teaching has the potential to scaffold student participation in the scientific practices. We present data from elementary-school pre-service teachers (PSTs) in science methods classes to demonstrate their abilities for engaging in this type of responsiveness and use this data to lend intuitive plausibility to our theoretical argument. This work has implications for how we support PSTs in learning about and enacting the scientific practices.

  PublisherDOI

Frequent coauthors

• Miriam Gamoran Sherin

  11 shared

• Bruce Sherin

  8 shared

• Tor Ole B. Odden

  7 shared

• Adam A. Colestock

  New York University Shanghai

  5 shared

• Leema K. Berland

  University of Wisconsin–Madison

  5 shared

• Anthony Gitter

  4 shared

• Chris S. Magnano

  University of Wisconsin–Madison

  4 shared

• Steven R. Wangen

  3 shared

Education

• Ph.D., Curriculum and Instruction

  University of Wisconsin–Madison

  1990

• M.S., Curriculum and Instruction

  University of Wisconsin–Madison

  1986

• B.A., Education

  University of Wisconsin–Madison

  1983

Awards & honors

• National Science Foundation funding
• Spencer Foundation/National Academy of Education funding