Xiaoming Zhai

Assistant Professor
Department of Mathematics, Science, and Social Studies Education

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

Google Scholar Page Download CV

Contact

Aderhold Hall, 105J

706-542-4548 (office)

@xiaoming_zhai

https://www.ai4stem.org

Research Summary

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 in high-impact journals such as Journal of Research in Science Teaching, Studies in Science Education, International Journal of Science Education, Journal of Science Education and Technology, Research in Science Education, Computers& Education, British Journal of Educational Technology, International Journal of Educational Research, Studies in Educational Evaluation, etc.

I served as the Guest Editor of a Special Issue in the Journal of Science Education and Technology: Applying Machine Learning in Science Assessment: Opportunity and Challenge. I am serving on the Editorial Board: Journal of Research in Science Teaching, Journal of Science Education and Technology, and Disciplinary and Interdisciplinary Science Education Research.

Grants

Lead PI: Collaborative Research: Supporting Instructional Decision Making: The Potential of An Automatically Scored Three-dimensional Assessment System (PASTA). National Science Foundation, DRK-12, $2,999,996.

This is collaborative research in four institutions. The project is sponsored by NSF with a total amount of $2,999,996 for four years. The Lead PI, Xiaoming Zhai at the University of Georgia, has the Award ID: 2101104 ($903,421). The PIs in other institutions are Joe Krajcik ($ 890,000), Yue Yin ($ 596,575), Gary Weiser ($ 610,000).

Any questions or inquiries for this project should direct to Dr. Xiaoming Zhai at xiaoming.zhai@uga.edu

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

PI: AI-based Assessment in STEM Education Conference: Potential, Challenge, and Future. Award ID: 2138854

PI: Affordable Course Materials Grant, 2020-2021, $5,000, University of Georgia

Publications

Special Issue: Applying Machine Learning in Science Assessment: Opportunity and Challenge. Journal of Science Education and Technology, 2021, (2).

Editor: Xiaoming Zhai

Gao, Y., Zhai, X., Cui, Y., Chen, F., Xin, T. (2021). Re-examining a learning progression of buoyancy: A longitudinal study. Research in Science Education. DOI: 10.1007/s11165-021-10021-x

Zhai., X., Haudek, K. C., Wilson, C., & Stuhlsatz, M. A. (2021). A Framework of Construct-Irrelevant Variance for Contextualized Constructed-Response Assessment. Frontiers in Education. DOI: 10.3389/feduc.2021.751283

Zhai, X. & Jackson, F. D. (In press). A Pedagogical Framework for Mobile Learning in Science Education. In Tierney, R., Rizvi, F., Ercikan, K., & Smith, G. (Eds.), International Encyclopedia of Education (4th ed., Vol. The Rise of STEM Education. Liu, X., & Wang L. (Eds.)). Published by Elsevier.

Zhai, X., Li, M. (2021). Validating a partial-credit scoring approach for multiple-choice science items: An Application of Fundamental Ideas in Science. International Journal of Science Education. https://doi.org/10.1080/09500693.2021.1923856

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. (2021). 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.