New faculty Q&A: Chun Wang

September 25, 2018

From an early age, standardized tests loomed large for Chun Wang. In China, how Wang and her classmates performed on high-stakes tests would determine not only which university they could go to, but what they’d study.

Today, the new ��ߺ�� ߺ�� faculty member is working to create more reliable and fair tests.

Wang comes to UW from the University of Minnesota and has received early careers awards for her scholarship from the American Educational Research Association Division D (Quantitative Research Methodology), International Association for Computerized Adaptive Testing and Psychometric Society. In 2014, she was named a National Academy of Education/Spencer Foundation Post-doctoral Fellow and received support for a multidimensional analysis of student growth using item response theory.

She has recently published papers on detecting aberrant behavior in computer-based testing in the and .

In the Q&A below, Wang discusses her research, courses she’ll be teaching and more.

What drew you to education?

I received my bachelor’s degree in psychology and was a quantitative psychologist by training. However, after graduate school, my research was deeply influenced by the real challenges of educational measurement—in particular high-stakes standardized testing—mainly because of my advisor.

My advisor, Professor Hua-Hua Chang, is a practitioner-turned-professor, and he always encouraged me to work on “significant” research questions that are directly related to real life challenges. Education is a field where advanced measurement research could have a long-lasting impact and could potentially benefit hundreds and thousands of students.

Describe your research agenda. What makes this work meaningful to you?

My research focus is broadly situated in the field of educational and psychological measurement, with specific devotion to methodology advancement that leads to better assessment with higher reliability/fidelity, fairness and security. My passion is about the improvement of methods for measuring a wide range of educational and psychological variables, as well as developing, refining and extending methods for analyzing multivariate data that are widely used in education.

Several keywords that describe my work include: item response theory (IRT) or latent variable models in general, computerized adaptive testing (CAT) and cognitive diagnostic models/applications. I am currently working on innovative statistical models and methods to extract meaningful information from rich educational big data from heterogeneous populations.

I also partner with core testing companies such ETS and Pearson to promote and apply cutting edge methods in educational assessments. In addition, I work closely with psychologists and health researchers to apply sophisticated IRT models and CAT methodologies. Some of my ongoing projects include applications in vision research and rehabilitation research.

This work is meaningful to me because I truly believe quantitative methods are the backbone to most scientific inquiries.

What attracted you to UW ��ߺ��?

First of all, UW ��ߺ�� is a reputable college with world-class researchers and programs. One professor that I respect a lot was a former graduate of its measurement and statistics program. Second, Seattle is a city where my two-body problem can be finally resolved and I greatly appreciate the opportunity. I am also very excited to collaborate with colleagues at the College and contribute as much as I can to move the education literature forward.

What's a course you're particularly excited to teach?

I am excited to teach graduate seminars on “Computerized Adaptive Testing” and “Cognitive Diagnostic Modeling.” It is gratifying to know that some measurement and statistics students are waiting to learn these topics from me before they graduate. I look forward to getting to know students at the UW!

What's something that students and colleagues should know about you?

Besides working on methodology projects, I am open to collaborations at all levels (e.g., proposals and papers).

I have high standards for my students, but I also care about them. I try to provide mentoring and support as much as possible to help them grow personally and professionally.