Faculty & Staff

Yue Yin, PhD

Associate Professor

Dr. Yue Yin is an associate professor in the Department of Educational Psychology whose research interests and expertise include assessments, science education, research design, survey design, and applied measurement/statistics.

Before moving to UIC, she was an Assistant Professor in the Department of Educational Psychology at the University of Hawaii at Manoa for three years. She obtained her Ph.D. in Science Education and MA in Psychology at Stanford University. She also obtained her MA in Education and B.S. in Chemistry at Peking University.  Dr. Yin has taught educational assessment, research methodology, educational measurement, and statistics (introductory statistics, regression, ANOVA, and Multivariate Analysis) at the undergraduate and/or graduate levels.


  • 2005 - PhD, Stanford University, Science Education and Assessment
  • 2003 - MA, Stanford University, Psychology
  • 2000 - MA, Peking University, Education
  • 1997 - BS, Peking University, Applied Chemistry

Research & Teaching Interests

Dr. Yin is particularly interested in classroom assessment and applied measurement. She has conducted research on performance assessment, concept mapping assessment, formative assessment, concept inventory development and validation, and computational thinking assessment. The subject contents in her research have involved physics, chemistry, biology, mathematics, and statistics, ranging from K-12 to higher education. In her research, she used learning theory as a foundation, measurement and statistics as tools, to explore and examine ways of using assessments to improve students' learning.

Her teaching interests include measurement and applied statistics. At UIC she regularly teaches Analysis of Variance, Regression Analysis, Multivariate Analysis, and Educational Measurement.

Selected Publications

Yin, Y., Olson, J., Slovin, H., Olson, M., & Brandon, P. (2015). Comparing two versions of professional development for teachers using formative assessment in networked mathematics classrooms. Journal of Research on Technology in Education, 47(1), 41-70.

Yin, Y., Tomita, M. K., & Shavelson, R. J. (2014). Using formal embedded formative assessment aligned with learning progressions to promote conceptual change in science. International Journal of Science Education, 36(4), 531-552

Briggs, D. C., Ruiz-Primo, M. A., Furtak, E. M., Shepard, L. A., & Yin, Y. (2012). Meta-analytic methodology and inferences about the efficacy of formative assessment. Educational Measurement: Issues and Practice, 31(4), 13-17.

Pan, T., & Yin, Y. (2012). The relationship between mean square differences and standard error of measurement: Comment on Barchard (2012). Psychological Methods, 17(2), 309-311. 

Yin, Y. (2012). Applying scientific principles to solve misconception problems. Science Scope, 35(8), 48-53.

Yin, Y. (2012). Using tree diagrams as an assessment tool in statistics education. Educational Assessment, 17(1), 22-49. 

Im, S., & Yin, Y. (2009). Diagnosing students' statistical inference skills by using the rule-space model. Studies in Educational Evaluation, 35(4), 193-199.

Yin, Y., & Shavelson, R. J. (2008). Application of generalizability theory to concept map assessment research. Applied Measurement in Education, 21(3), 273-291.

Yin, Y., Tomita, K. M., & Shavelson, R.J. (2008). Diagnosing and dealing with student misconceptions about モSinking and Floating. Science Scope, 31(8), 34-39. 

Yin, Y., Shavelson, R. J., Ayala, C. C., Ruiz-Primo, M. A., Tomita, M., Furtak, E. M., Brandon, P. R., & Young, D. B. (2008). On the measurement and impact of formative assessment on students' motivation, achievement, and conceptual change. Applied Measurement in Education, 21(4), 335-359

Ayala, C. C., Shavelson, R. J., Brandon, P. R., Yin, Y., Furtak, E. M., Ruiz Primo, M. A., Young, D. B., & Tomita, M. (2008). From formal embedded assessments to reflective lessons: The development of formative assessment suites. Applied Measurement in Education, 21(4), 315-334

Vanides, J., Yin, Y., Tomita, M. T., & Ruiz-Primo, M. A. (2005). Using concept maps in the science classroom. Science Scope, 28(8), 27-31.

Yin, Y., Vanides, J., Ruiz-Primo, M. A., Ayala, C. C., & Shavelson, R. J. (2005). Comparison of two concept-mapping techniques: Implications for scoring, interpretation, and use. Journal of Research in Science Teaching, 42(2), 166-184. 

Ayala, C. A., Shavelson, R. J., Yin, Y., & Schultz, S. E. (2002). Reasoning dimensions underlying science achievement: The case of performance assessment. Educational Assessment, 8(2), 101-122. 

Honors & Awards

  • 2015  Co-Principal Investigator, Development of Assessment Protocols for Assessing Computational Thinking in Physics and Engineering Making Activities (PI: Roxana Hadad). National Science Foundation. Award Id: 1543124; Amount: $898,564.
  • 2012  Teaching Recognition Program award from the University of Illinois at Chicago, Council for Excellence in Teaching and Learning.
  • 2009  Co-PI, Identifying critical characteristics of effective feedback practices in science and mathematics education (Min LI, Maria Ruiz-Primo, & Yue Yin). National Science Foundation, DRK12. Award ID: DRL-0822373; Amount: $271,327. 

Courses Taught

546 Educational Measurement

  • ED 503 or EPSY 503 or the equivalent or consent of the instructor.

Contemporary models for the analysis of data arising from multiple-choice tests, rating-scale questionnaires, or experts' judgments of examinee performance. Test equating is also covered.

563 Advanced Analysis of Variance in Educational Research


EPSY 503

Detailed coverage of the principles of analysis of variance and the analysis of data collected from research employing experimental designs.

547 Multiple Regression in Educational Research


 EPSY 503

Introduction to multiple correlation and regression techniques as tools for the analysis and interpretation of educational and behavioral science data.

583 Multivariate Analysis of Educational Data


 EPSY 505 or EPSY 547 or EPSY 563

Introduction to multivariate statistical methods in education including data screening, canonical correlation, MANOVA/MANCOVA, DFA, profile analysis, component/factor analysis, confirmatory factor analysis, and structural equation modeling.