Xiaoming Zhai

Assistant Professor
Mathematics and Science Education

Biography

I have more than 10 years of experience teaching at the K-12 level and teacher preparation both in the US and China, along with significant experience conducting research with direct classroom application.

My extensive experience with classroom teaching both as a teacher and mentor informs my life-long career goal—developing equitable and inclusive science teaching for every student. I saw first-hand the discrepancy in outcomes for students based on gender or socioeconomic status, and how science teachers struggled to address these issues. This motivated me to continue my education so that I could further explore science teaching and learning and offer help to more science teachers to assist them with their instructional practices. I completed my doctoral study in the physics education group at Beijing Normal University, China’s top educational university specializing in teacher preparation, and the quantitative research methods group at University of Washington. I received substantial training in the design of inclusive physics curriculum materials, science teacher preparation, and development and use of innovative assessment in my Ph.D. program, as well as in which where I was a Postdoctoral Research Associate at Stanford University (2017-2018) and Michigan State University (2019-2020), and a Visiting Scholar/Research Specialist at University of Illinois at Chicago (2018-2019). My learning experience has helped me support teachers in developing equitable and inclusive science teaching—thereby giving all students, regardless their culture, gender, or socioeconomic status, greater opportunities for success in science.

Areas of Expertise

Science education with a focus on physics
Assessment and measurement theory
Applied quantitative methods and data sciences in science education
Technology integration theory in science education

Interests

AI/Machine learning-based innovative assessment practices in science
Learning progression
Mobile learning in science
Science teacher education and career motivation

Academic Affiliations

Science Education

Education

Ph.D. in Curriculum and Instruction (physics), 2017
Beijing Normal University

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Contact

Aderhold Hall, 105J

706-542-4548 (office)

Research Summary

My teaching experience shapes my research philosophy: I believe that understanding students is paramount to any pedagogical strategies to create effective, equitable, and inclusive science learning. However, teachers usually struggle with effective assessment practices, especially those targeting the NGSS learning goals. I thus focus my research on developing and using innovative assessment to help teachers better understand their students. My ultimate goal is improving equitable and inclusive science education via valid and innovative assessment so that all students can thrive in sciences. Based on this goal, my research interests focus on two areas: (a)The first looks at using innovative assessment to examine complex constructs in science learning and teaching as one means of supporting teachers, approaching the topic through a variety of methodologies and technologies such as machine learning. (b) My second research area of interest concerns the application of assessment results in science teaching and learning. My research has broadly appeared on high-impact journals such as Journal of Research in Science Teaching (JRST), Studies in Science Education, International Journal of Science Education, Journal of Science Education and Technology (JOST), Research in Science Education, Computers& Education, British Journal of Educational Technology, International Journal of Educational Research, Studies in Educational Evaluation, etc.

I am serving as the Guest Editor of a Special Issue of JOST: Applying Machine Learning in Science Assessment: Opportunity and Challenge. I am also serving on the Editorial Board of JRST and JOST.

Announcement: I am currently accepting doctoral students with an interest in applying machine learning/AI in science education. The candidates are expected to take courses both in science education and computer science.

Grants

Collaborative Research: ArguLex - Applying Automated Analysis to a Learning Progression for Argumentation. Co-Principal Investigator. National Science Foundation, EHR Core, Award ID: DUE- 1561159.

Affordable Course Materials Grant, Principal Investigator, University of Georgia

Publications

Zhai, X. (2021). Practices and theories: How can machine learning assist in innovative assessment practices in science education. Journal of Science Education and Technology. DOI: 10.1007/s10956-021-09901-8

Maestrales, S., Zhai, X., Touitou, I., Baker, Q., Krajcik, J., Schneider, B. (In press). Using machine learning to score multi-dimensional assessments of chemistry and physics. Journal of Science Education and Technology. DOI: 10.1007/s10956-020-09895-9

Zhai, X. (2021). Advancing automatic guidance in virtual science inquiry: From ease of use to personalization. Educational Technology Research and Development. DOI: 10.1007/s11423-020-09917-8

Zhai, X., Krajcik, J., Pellegrino, J. (2021). On the validity of machine learning-based Next Generation Science Assessments: A validity inferential network. Journal of Science Education and Technology. DOI: 10.1007/s10956-020-09879-9

Zhai, X., Shi, L. Nehm, R. (2020). A Meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements. Journal of Science Education and Technology. DOI: 10.1007/s10956-020-09875-z

Zhai, X., Schneider, B., Krajcik, J. (2020). Motivating preservice physics teachers to low-socioeconomic status schools. Physics Review Physics Education Research. DOI: 10.1103/PhysRevPhysEducRes.16.023102

Zhai, X., Haudek, K., Shi, L., Nehm, R., Urban-Lurain, M. (2020). From substitution to redefinition: A framework of machine learning-based science assessment. Journal of Research in Science Teaching, 57(9), 1430-1459. DOI: 10.1002/tea.21658

Zhai, X., Haudek, K., Stuhlsatz, M., Wilson, C. (2020). Evaluation of construct-irrelevant variance yielded by machine and human scoring of a science teacher PCK constructed response assessment. Studies in Educational Evaluation, 67, 1 -12. doi.org/10.1016/j.stueduc.2020.100916

Zhai, X., Shi, L. (2020). Understanding how the perceived usefulness of mobile technology impacts physics learning achievement: A pedagogical perspective. Journal of Science Education and Technology. 1-15. DOI 10.1007/s10956-020-09852-6

Lin, Q., Yin, Y., Tang, X., Hadad., R., Zhai., X. (2020). Assessing learning in technology-rich maker activities: A systematic review of empirical research. Computers & Education. doi.org/10.1016/j.compedu.2020.103944

Zhai, X., Yin, Y., Pellegrino, J., Haudek, K., Shi., L. (2020). Applying machine learning in science assessment: A systematic review. Studies in Science Education. 56(1), 111-151.

Tang, X., Yin, Y., Lin, Q., Hadad, R., Zhai., X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education. doi.org/10.1016/j.compedu.2019.103798

Zhai, X., Li, M., & Chen, S. (2019). Examining the uses of student-led, teacher-led, and collaborative functions of mobile technology and their impacts on physics achievement and interest. Journal of Science Education and Technology. 28, 310-320.

Zhai, X., Zhang, M., Li, M., & Zhang, X. (2019). Understanding the relationship between levels of mobile technology use in high school physics classrooms and the learning outcome. British Journal of Educational Technology. 50( 2), 750-766.

Zhai, X. (2019). Becoming a teacher in rural areas: How curriculum influences government- contracted pre-service physics teachers’ motivation. International Journal of Educational Research. 94, 77-89.

Gao, Y., Zhai, X., Andersson, B., Xin, T, & Zeng, P. (2018). Developing a learning progression of buoyancy to model conceptual change: A latent class and rule space model analysis. Research in Science Education. 50, 1369–1388. doi:10.1007/s11165-018-9736-5

Zhai, X., Zhang, M., & Li, M. (2018). One-to-one mobile technology in high school physics learning: Understanding its use and outcome. British Journal of Educational Technology. 49(3), 516-532.

Zhai, X., Li, M., & Guo, Y. (2018). Teachers’ use of learning progression-based formative assessment to inform teachers’ instructional adjustment: a case study of two physics teachers’ instruction. International Journal of Science Education. 40(15),1832-1856. doi.org/10.1080/09500693.2018.1512772

Awards and Accolades

AERA SIG TACTL Early Career Award

American Educational Research Association, 2021

Jhumki Basu Scholar Award

National Association of Research in Science Teaching, 2020