Interdisciplinary degree through the Department of Mathematics
Campus: Virginia Tech Blacksburg Campus
Instructions: Residential/On Campus
We believe that all teacher educators must be prepared to provide equity and access for all learners at all educational levels, kindergarten through doctoral studies.
Intensive Research Opportunities
Our Mathematics Education program
The Mathematics Doctoral Program: Education Option is a multidisciplinary doctoral degree through the Department of Mathematics. The Education Option differs from a traditional Mathematics Ph.D. with its focus on mathematics education research. It differs from the School of Education's Mathematics Education Program with its advanced mathematics fluency requirements.
Why Study Mathematics Education Here?
Our math education faculty focus on preparing highly-qualified math teachers while engaging in innovative research on teaching and learning. With some members residing in the Department of Teaching & Learning and others in the Math Department, our faculty take advantage of opportunities to collaborate with additional Virginia Tech faculty members across the School of Education and the College of Science.
What You’ll Study
Hour Requirement: 90 hours
Mathematics Education Courses (12 credit hours)
All Mathematics Education PhD students at Virginia Tech take the following four seminars (3 credit hours each):
- Modeling Mathematical Knowledge & Learning (MATH 5624)
- Research in Undergraduate Mathematics Education (MATH 5634)
- Critical Analysis of Mathematics Education Research (EDCI 5784)
- Advanced Topics in Mathematics Education (EDCI 5784)
Additional Mathematics Courses (at least 21 credit hours)
- MATH 4225, 4226, 4124, and 4445 (or their equivalents) should be taken if not in the student's background.
- At least 21 hours of MATH courses numbered 5000 or above must be completed (courses taken for a Ph.D. in Mathematics may count toward this total).
Mathematics Education Related Courses
- Quantitative and Qualitative Methods courses should be taken if not in the student's background. We expect all students to develop proficiency in both qualitative and quantitative methods, regardless of which methods they plan to use in their dissertation studies.
- Qualitative Research 1 & II (EDRE 6504 & EDRE 6524)
- Quantitative Research Methods I & II (EDRE 6605 & EDRE 6606)
- Other methods courses potentially relevant to students' programs:
- Advanced Issues in Qualitative Research (EDRE 6784)
- Advanced Statistics in Education (EDRE 6634)
- Hierarchical Linear Modeling (EDRE 6694)
- Mixed Method Research Design (EDRE 6744)
- Additional courses (e.g., Methods, Psychology, Philosophy, Education) should be taken as they relate to and support the student's dissertation research. Such courses would be determined by the student and her/his program committee and might include the following:
- Cognitive Processes & Educational Practice (EDEP 6114)
- Sociocultural Influences on Learning & Instruction (EDEP 5784)
- Constructivism in Education (EDEP 6224)
- Motivation & Cognition (EDEP 6444)
- STEM Education Research (EDCI 5834)
- Education & Anthropology (EDCI 6034)
- Cognitive Development (PSYC 5544)
- History of the Philosophy of Science (PHIL 6314)
Research/Dissertation Hours (30 - 60 credit hours)
30-60 hours of MATH 7994, research and dissertation hours
Preliminary Written Examinations (3)
- at least one Preliminary Examination in traditional Mathematics
- one Preliminary Examination in Mathematics Education
- up to one Preliminary Examination requirement can be filled by completion of a Master's Thesis in Mathematics at Virginia Tech
- foreign language requirement
- comprehensive oral examination of dissertation topic
- final written thesis
- final oral examination (defense)
Career and Professional Development
PhD program is primarily designed to prepare leaders in mathematics education research.. The possible career paths could be but not limited to faculty jobs, researcher etc.
This degree program is offered at the Blacksburg campus. The Blacksburg campus offers students the full services of the university, including an extensive library, technology support, and the Graduate Life Center.
- Jay Wilkins
- Megan Wawro
- Catherine (Katy) Ulrich
- Anderson Norton
- Bettibel Carson Kreye
- Estrella Johnson
- Susan Hagen
- Nate Gildersleeve
- Mathematics Education
- Quantitative Literacy
- Mathematics Beliefs and Attitudes
- Children’s development of fractions concepts
- Teaching and learning of probability and statistics
Associate Professor, Mathematics
- Students' understanding of linear algebra
- Development of mathematical meaning over time (for both individual students and the classroom as a collective community) and explores ways to coordinate the analyses at the individual and collective levels
- Students' development of formal ways of reasoning about mathematical concepts via authentic participation in discipline-specific mathematical activities
- Building models of how middle school students construct their integer (signed number) concepts
- Incorporating results from learning research into mathematics education policy and instruction
- Students' mathematical development using teaching experiments, along with quantitative methods
- Students' mathematical conjectures formed in the context of learning fractions, algebra, and geometry
- Education and training of mathematics specialists
- Professional development for in-service K-12 teachers
- Instructional strategies for teacher education
Assistant Professor, Mathematics
- Investigating classroom instructional practices
- Developing and researching the impacts of instructional support
- Identifying factors and influences that shape pedagogical practice
Mathematics Education Research Highlights
- Norton, A., Wilkins, J. L. M., & Xu C. Z. (2018). A progression of fraction schemes common to Chinese and U.S. students. Journal for Research in Mathematics Education, 49(2), 210-226.
- MacDonald, B. L., & Wilkins, J. L. M. (2017). Seven types of subitizing activity characterizing young children's mental activity. In S. Marx & S. L. Gregory (Eds.), Qualitative research in STEM, (pp. 256-286). New York, NY: Routledge.
- Sloane, F. C., & Wilkins, J. L. M. (2017). Aligning statistical modeling with theories of learning in mathematics education research. In J. Cai (Ed.), Compendium for Research in Mathematics Education (pp. 183-207). Reston, VA: National Council of Teachers of Mathematics.
- Ulrich, C., & Wilkins, J. L. M. (2017). Using written work to investigate stages in sixth-grade students' construction and coordination of units. International Journal of STEM Education, 4:23.
- Wilkins, J. L. M., & Ulrich, C. (2017). The role of skip counting in children's reasoning. Virginia Mathematics Teacher, 43(2), 8-14.
- Skaggs, G., Wilkins, J. L. M., & Hein, S. F. (2017). Estimating an observed score distribution from a cognitive diagnostic model. Applied Psychological Measurement, 41(2), 150-154.
- Kreye, B., & Tilley-Lubbs, G. A. (2014) Collaboration for authentic preservice teacher experiences: Mathematics and English as a second language. International Journal of Teaching and Learning in Higher Education, Vol. 25, No. 3.
Students interested in applying to the Mathematics, Education Option (Ph.D.) should contact the program leader, Dr. Andy Norton, by email at email@example.com.
Visit our office at 434 McBryde Hall, Blacksburg, VA 24061
Spring: January 1
Summer: May 1
*Fall: August 1
Spring: September 1
Summer: January 1
*Fall: April 1
*Deadline for early decision admission with full funding consideration for Fall: January 15.