Li Tan

Assistant Professor

Engineering Education Systems & Design
Polytechnic School, Arizona State University

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Li Tan

About

I am an Assistant Professor of Engineering Education Systems & Design in the Polytechnic School at Arizona State University. My research centers on understanding pathways to academic success for students of all demographic and socioeconomic backgrounds, with a focus on postsecondary success in engineering fields.

My research approaches heavily involve causal identification with observational data and quantitative methods with longitudinal datasets. More recently, I have been exploring the role of artificial intelligence in engineering education and the engineering workforce.

Prior to joining ASU, I was a Visiting Assistant Professor and Postdoctoral Researcher at Purdue University's School of Engineering Education. I hold a Ph.D. in Economics from the University of Missouri—Columbia.

Education

2018
Ph.D. in Economics University of Missouri—Columbia
2012
M.S. in Financial Management Australian National University
2009
B.E. in Industrial Engineering Tsinghua University

Research

What I Study

  • Learning Environments, Skill Formation, and Team-Based Learning in Engineering
  • Academic and Career Pathways in Engineering
  • AI Literacy and AI-Enhanced Pedagogy in Engineering Education
  • Impact of Artificial Intelligence on Engineering Education and the Engineering Workforce

How I Study It

  • Bayesian Statistical Modeling
  • Causal Inference
  • Large Language Models and Machine Learning as Educational Research Methods
  • Longitudinal and Person-Centered Quantitative Analysis

Publications

Peer-Reviewed Journal Articles

  1. Revise & Resubmit Bian, Y., Tan, L., Xu, X., & Coley, B. The impact of state-level anti-DEI policies on undergraduate engineering enrollment and completion by race/ethnicity and gender: A difference-in-differences analysis. Journal of Engineering Education.
  2. Accepted Xu, X., & Tan, L. Examining college student mental health in engineering and computer science education: Relevant factors, relative importance, and demographic differences. European Journal of Engineering Education.
  3. Dai, A., & Tan, L. (2025). Examining student retention dynamics in 4-year engineering and computer science programs using random forest analysis. Journal of Engineering Education, 114(3), e70016. DOI
  4. Tan, L., Xu, X., Wei, S., & Morphew, J. (2025). The advantage of regression and covariate utilization over ANOVA in engineering education research. European Journal of Engineering Education, 50(4), 878–908. DOI
  5. Wei, S., Tan, L., Zhang, Y., & Ohland, M. (2024). The effect of the emergency shift to virtual instruction on student team dynamics. European Journal of Engineering Education, 49(1), 139–163. DOI
  6. Main, J. B., Cox, M. F., McGee, E. O., Tan, L., & Berdanier, C. (2023). Trends in the underrepresentation of women faculty of color in engineering. Journal of Diversity in Higher Education, 16(5), 589–606. DOI
  7. Main, J. B., Wang, Y., & Tan, L. (2022). Preparing industry leaders: The role of doctoral education and early career management training in the leadership trajectories of female STEM PhDs. Research in Higher Education, 63(3), 400–424. DOI
  8. Tan, L. (2021). Imputing top-coded income data in longitudinal surveys. Oxford Bulletin of Economics and Statistics, 83(1), 66–87. DOI
  9. Tan, L., Main, J. B., & Darolia, R. (2021). Using random forest analysis to identify student demographic and high school-level factors that predict college engineering major choice. Journal of Engineering Education, 110(3), 572–593. DOI
  10. Main, J. B., Wang, Y., & Tan, L. (2021). The career outlook of engineering PhDs: Influence of postdoctoral research positions on early career salaries and the attainment of tenure track faculty positions. Journal of Engineering Education, 110(4), 977–1002. DOI
  11. Main, J. B., Tan, L., Cox, M. F., McGee, E. O., & Katz, A. (2020). The correlation between undergraduate student diversity and the representation of women of color faculty in engineering. Journal of Engineering Education, 109(4), 843–864. DOI
  12. Koedel, C., Li, J., Springer, M. G., & Tan, L. (2019). Teacher performance ratings and professional improvement. Journal of Research on Educational Effectiveness, 12(1), 90–115. DOI
  13. Tan, L., & Koedel, C. (2019). The effects of differential income replacement and mortality on U.S. social security redistribution. Southern Economic Journal, 86(2), 613–637. DOI
  14. Parsons, E., Koedel, C., & Tan, L. (2019). Accounting for student disadvantage in value-added models. Journal of Educational and Behavioral Statistics, 44(2), 144–179. DOI
  15. Koedel, C., Li, J., Springer, M. G., & Tan, L. (2017). The impact of performance ratings on job satisfaction for public school teachers. American Educational Research Journal, 54(2), 241–278. DOI

Teaching

Arizona State University

EGR 280
Engineering Statistics Fall 2022, Fall 2023, Fall 2024, Fall 2025, Spring 2026
EGR 574
Engineering Education Systems in Context Fall 2024
EGR 673
Applications of Quantitative Methods for Engineering Education Research Spring 2023, Spring 2024

Purdue University

ENGR 131
Transforming Ideas to Innovation I Fall 2021, Summer 2022
ENGR 132
Transforming Ideas to Innovation II Spring 2022
IDE 360
Multidisciplinary Engineering Statistics Spring 2019