Xiaoming Zhai

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

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 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 am serving as the Guest Editor of a Special Issue of 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

Collaborative Research: Supporting Instructional Decision Making: The Potential of Automatically Scored Three-dimensional Assessment (PASTA). National Science Foundation, DRK-12, $2,999,996. PI: Xiaoming Zhai ($903,421) Award ID: 2101104; PI: Joe Krajcik ($ 890,000); PI: Yue Yin ($ 596,575); PI: Gary Weiser ($ 610,000).
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, 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
Zhai, X., Li, M. (In press). 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.
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

NAEd/Spencer Research Development Award

National Academy of Education, 2021

AERA SIG TACTL Early Career Award

American Educational Research Association, 2021

Jhumki Basu Scholar Award

National Association of Research in Science Teaching, 2020