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Michael D. Eisner College of Education

College of Education

Personnel Directory

Julie Gainsburg

Julie  Gainsburg
Department Chair
Department of Secondary Education
Office:ED 1208
Phone:818 677 6155



Ph.D. Curriculum and Teacher Education, Stanford University, 2003

Teaching Interests

Methods of teaching secondary math, research on mathematics education, current issues in education.

Research Interests

The mathematics and problem solving of engineers, teaching math through and for real-world applications; teacher development and practice.


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Gainsburg, J., Rodriguez-Lluesma, C., & Bailey, D. (2010). A “knowledge profile” of an engineering occupation: Temporal patterns in the use of engineering knowledge. Engineering Studies, 2(3), 197-219.


"Each engineering occupation is distinguished by the body of specific knowledge it has built up over time. Some scholars argue that the instrumentality of this historically established knowledge in the solution of everyday design problems renders formal education more important than experience; others counter that engineering work primarily demands practice-generated knowledge that individuals construct in the course of everyday activities. We address this argument by documenting the frequency with which engineers apply different types of knowledge with different derivations. Through field observations of structural engineers, we constructed a “knowledge profile” that indicated that two-thirds of the knowledge engineers employed was practice generated. Knowledge profiles like this could help differentiate among engineering occupations and serve as the foundation for conceptualizing occupations in a world of “knowledge work.” In addition, knowledge profiles could help university engineering education programs better target and mirror the knowledge demands of the profession."

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Gainsburg, J. (2011). Book Review: Hoyles, C., Noss, R., Kent, P., & Bakker. A. (2010). Improving mathematics at work: The need for techno-mathematical literacies. Educational Studies in Mathematics, 76(1), 117-122.


"As mathematics educators, we constantly read about the failure of our schools to prepare graduates for the mathematical requirements of the modern workplace. Most of us accept this idea without question, perhaps because calls for more mathematics education keep us employed. Yet it is surprising that more of us don’t question this “failure,” given the conventional wisdom that adults rarely use the mathematics they learned in school and that, when mathematics is needed in the workplace, computers handle it. In this era of national standard setting and high-stakes mathematics examinations for school students, it seems to behoove us to understand what are the mathematical requirements of today’s jobs and how well today’s workers meet them. Celia Hoyles, Richard Noss, Phillip Kent, Arthur Bakker, and other colleagues have led this area of research for years through major projects exploring the mathematics used by entry-level, intermediate, and professional employees in a range of fields. Improving Mathematics at Work is the most recent and possibly most extensive report on their ethnographic and design-based workplace studies."

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Gainsburg, J. (2013). Learning to model in engineering. Mathematical Thinking and Learning, 15(4), 259-290.


Policymakers and education scholars recommend incorporating mathematical modeling into mathematics education. Limited implementation of modeling instruction in schools, however, has constrained research on how students learn to model, leaving unresolved debates about whether modeling should be reified and explicitly taught as a competence, whether it should be taught holistically or atomistically, and whether students’ limited domain knowledge is a barrier to modeling. This study used the theoretical lens of legitimate peripheral participation to explore how learning about modeling unfolds in a community of practice—civil engineering—known to develop modeling expertise among its members. Twenty participants were selected to represent various stages of engineering education, from first-year undergraduates to veteran practitioners. The data, comprising interviews, “think-aloud” problem-solving sessions, and observations of engineering courses, were analyzed to produce a description of how this professional community organizes learning about mathematical models and resolves general debates about modeling education.

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