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PUBLICATION

Papers Under Review or in Revision​
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  1. Zhai, X., Shi, L. Nehm, R. (Under review) A Meta-Analysis of Machine Learning-Based Science Assessments: Factors Impacting Machine-Human Score Agreements. Journal of Science Education and Technology.

  2. Zhai, X. (Under review). Advancing automatic guidance in virtual science inquiry: From ease of use to personalization. Educational Technology Research and Development.

  3. Zhai, X., Li, M., & Liu, X. (After-review revision). Assessing high-school students’ modeling performance on Newtonian mechanics. Journal of Research in Science Teaching.

  4. Maestrales, S., Zhai, X., Touitou, I., Baker, Q., Krajcik, J., Schneider, B. (Under review) Using Machine Learning to Evaluate Multi-Dimensional Assessments of Chemistry and Physics Tests. Journal of Science Education and Technology.

  5. Gao, Y., Zhai, X., Bulut, O., Cui, Y. (Under review). Understanding Problem-Solving Style in Technology-Rich Environments: An Application of Log Data Analysis. Computers in Human Behavior.

  6. Zhai, X., Li, M. (Resubmission & under review). Validating a partial-credit scoring approach for multiple-choice science items. Applied Measurement in Education.

  7. Zhai., X., Haudek, K., Wilson, C., Stuhlsatz, M. (Under review). Examining construct-irrelevant variances of contextualized constructed-response assessment: A many-facet Rasch modeling approach. Disciplinary and Interdisciplinary Science Education Research.

  8. Yin, Y., Khaleghi, S., Hadad., R., Zhai., X. (After-review revision). Improving and assessing computational thinking using Ardurio activities. Computers & Education. 

  9. Zhai, X., Barbara, S., Krajcik, J. (Under review). Motivating pre-service science teachers to serve low-SES communities. Physics Review Physics Education Research.

  10. Zhai, X. (Under review). Extending the usability of many-facet Rasch model in educational research. Studies in Educational Evaluation.

  11. Zhai, X., Yin, Y. (After-review revision). Making sense of an educative learning progression of scientific modeling competence: Impacts on novice teachers’ critiquing of lesson plans. Science Education.

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2020

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  1. 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. DOI: 10.1007/s10956-020-09852-

  2. Zhai, X., Krajcik, J., Pellegrino, J. (Invited). On the validity of machine learning-based science assessments. Journal of Science Education and Technology.

  3. Gao, Y., Cui, Y., Zhai, X., Chen, F., Xin, T. (Accepted pending minor revision). Re-examining a learning progression of buoyancy. Research in Science Education.

  4. 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 

  5. Zhai, X. (2020). Mechanical vibration and mechanical wave. Teaching sources for physics teachers. Beijing, CN: Beijing Normal University Press. (In Chinese)

  6. Zhai, X., Haudek, K., Wilson, C., Stuhlsatz, M. (2020). Comparison of construct-Irrelevant variances yielded by machine engine and human experts. Studies in Educational Evaluation. doi.org/10.1016/j.stueduc.2020.100916

  7. 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. DOI: 10.1002/tea.21658

  8. 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. https://doi.org/10.1080/03057267.2020.1735757 (SSCI)

  9. 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 (SSCI)

  10. Zhai, X., Haudek, K., Wilson, C. (2020) Applying Machine Learning to Automatically Assess Middle-School Students’ Argumentation. Paper submitted to the 2020 annual conference of the American Association of Physics Teachers, Grand Rapids, MI. (Conference canceled)

  11. Zhai, X., Haudek, K., Wilson, C., Chuek, T., Osborne, J. (Submitted) Diagnosing Middle-School Students’ Cognition in Argumentation Practices Using Machine Learning. Paper submitted to the 2020 annual conference of the American Association of Physics Teachers, Grand Rapids, MI.

  12. Wilson, C., Stuhlsatz, M., Donovan, B., Bracey, Z., Gardner, A., Osborne, J., Cheuk, T., Haudek, K., Santiago, M., Zhai, X. (2020). Using automated analysis to assess middle school students’ competence with scientific argumentation. Paper presented on the 2020 annual conference of the American Educational Research Association, California.

  13. Zhai, X., Haudek, K., Shi, L., Nehm, R., Urban-Lurain, M. (2020). A Framework to Conceptualize Machine Learning-based Science Assessments. Paper presented to the 2020 annual conference of the National Association of Research in Science Teaching, Portland, OR. (Conference canceled)

  14. Shi, L., Zhai., X. (2020). Understanding the perceived usefulness of mobile technology in physics learning: A pedagogical perspective. Paper presented to the 2020 annual conference of the National Association of Research in Science Teaching, Portland, OR. (Conference canceled)

  15. Zhai, X. (2020, Organizer and presenter). Automated Scoring Complex Performance. Symposium presented to the 2020 annual conference of the National Association of Research in Science Teaching, Portland, OR. (Conference canceled). 

  16. Zhai., X., Haudek, K., Stuhlsatz, M., Wilson, C. (2020). An Approach to Investigating Construct-Irrelevant Variance for Contextualized Constructed-Response Assessment. Paper presented on the 2020 annual conference of the American Educational Research Association, California. (Conference canceled)

  17. Gane, B., Zaidi, S., Zhai., X., Pellegrino, J. (2020). Using Machine Learning to Score Tasks that Assess Three-dimensional Science Learning. Paper presented on the 2020 annual conference of the American Educational Research Association, California. (Conference canceled)

  18. Zhai, X. (2020, Organizer and Presider). Applying Machine Learning in Next Generation Science Assessment. Session will be presented on the 2020 annual conference of the American Educational Research Association, California.

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2019

 

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  1. Zhai, X., Li, M., & Chen, S. (2019) Examining the usages of student-led, teacher-led and collaborative functions of mobile technology and their effects on high-school physics achievement and interest. Journal of science education and technology. 

  2. Zhai, X., Haudek, K., (2019). Comparison of construct-irrelevant variances yielded by machine and human experts on constructed responses to a science teacher PCK assessment. Paper presented at the 2019 CREATE for STEM mini-conference, Lansing, MI. 

  3. Hernandez, P., Ruiz-Primo, M. A., Zhai, X., Li, M., Kanopka, K. (2019) Validity Study of Linked-items to Determine Student Fundamental Ideas. Paper presented at the annual conference of the American Educational Research Association, Toronto, Canada.

  4. Ruiz-Primo, M. A., Li, M., Minstrell, J., Zhai, X., Dong, D., Kanopka, K., Hernandez, P. (2019). Testing the generalization to the domain inference: The use of contextualized clusters of items. Paper presented at the NCME annual conference, Toronto, Canada.

  5. Ruiz-Primo, M. A., Zhai, X., Li, M., Hernandez, P., Kanopka, K., M., Dong, D., & Minstrell, J. (2019). Contextualized science assessments: Addressing the use of information and generalization of inferences of students’ performance. Paper presented at the annual conference of the American Educational Research Association, Toronto, Canada.

  6. Zhai, X., Ruiz-Primo, M. A., Li, M., Dong, D., Kanopka, K., Hernandez, P., & Minstrell, J. (2019). Using many-facet Rasch model to examine student performance on contextualized science assessment. Paper presented at the annual conference of the American Educational Research Association, Toronto, Canada.

  7. Zhai, X., Ruiz-Primo, M. A., Li, M., Kanopka, K., Hernandez, P., Dong, D., & Minstrell, J. (2019). Students’ involvement in contextualized science assessment. Paper presented at the annual conference of the National Association of Research in Science Teaching, Baltimore, MD.

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2018

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  1. Zhai, X. (2018) To Become a Teacher in Rural Areas: How curriculum influences government contracted student teachers’ motivation. International Journal of Educational Research. 

  2. Zhai, X., Li, M., & Guo, Y. (2018). How learning progression-based formative assessment influences teachers’ instructional design: a case study of two teachers’ energy instruction. International Journal of Science Education. 

  3. Zhai, X., Zhang, M., Li, M., & Zhang, X. (2018). Understanding the Relationship between Levels of Mobile Technology Use in High School Physics Classrooms and the Learning Outcome. British Journal of Educational Technology. 

  4. Gao, Y., Zhai, X., Andersson, B., & Xin, T. (2018). Developing a learning progression of buoyancy to model conceptual change: a latent class and rule space model analysis. Research in Science Education. 

  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.

  6. Guo, Y., & Zhai, X. (In press). Teaching Sources for Physics Teachers. Beijing, CN: Beijing Normal University Press.

  7. Dong, D., Hsiao, Y., Weihs, L., Li, M., Minstrell, J., Ruiz-Primo, M. A., & Zhai, X. Causal impact of exam problem context on student performance: a generalized linear mixed model approach. Paper present at 2018 AERA annual meeting.

  8. Zhai, X., & Li, M. Does a higher extent of mobile-technology-integrated physics learning indicate greater effects? Paper presented at 2018 NARST annual meeting.

  9. Zhai, X.,Li, M., Zhang, X., Chen, S., & Dong, D. The usages and effects of student-teacher led functions of mobile technology toward high-school physics learning. Paper presented at 2018 AERA annual meeting.

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2017

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  1. Zhai, X., & He, C. (2017). The aspects of scientific modeling competence in physics for middle school students--the elaborations from the perspectives of both the physicists and students. The Physics Teacher. (12), 2-7. (Chinese)

  2. Zhai, X., Guo, Y., & Li, M. (2017). Impact of a professional development project in terms of LPoSMC on novice physics teachers: results of a randomized controlled trial. Paper presented at 2017 NARST annual meeting, San Antonio, TX, US. 

  3. Li, M., Ruiz-Primo, M. A., Dong, D., Minstrell, J., Zhai, X., & Thummaphan, P. (2017). Issues for developing science contextualized items. Paper presented at 2017 NCME, San Antonio, TX, US. 

  4. Li, M., Ruiz-Primo, M. A., Dong, D., Minstrell, J., & Zhai, X.(2017). Examining the relationship between context characteristics and student performance on context-based items. Paper presented at 2017 NARST, San Antonio, TX, US.

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2016

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  1. Zhai, X., Sun, W., Guo, Y., & Zhang, M. (2016). Smart classroom: an evaluation of its implementations and impacts -- based on the longitude data of physics learning in a high school. China Educational Technology. (9), 121-127. (Chinese)

  2. Zhai, X., Guo, Y., & Li, M. (2016). Detect the components of scientific modeling competence. Paper presented at NARST annual meeting. Baltimore, US.

  3. Dong, D., Li, M., Zhai, X.,& Chen, S. (2016). Examining student thinking through learner-generated drawings. Paper presented on NARST annual meeting. Baltimore, US.

  4. Zhai, X.,Guo, Y., Li, M., & Thummaphan, P. (2016). Detect the relationship of scientific modeling competence, conceptual understanding and relative abilities for high school students. Paper presented at AERA annual meeting: Washington DC, US.

  5. Zhai, X.,Li, M., Guo, Y., & Chen, S. (2016). How do physics items with graphs influence students’ performance: using fixed effects model. Paper presented at International Test Commission (ITC) Conference. Vancouver, Canada. 

  6. Zhai, X., Alonzo, A. C., Guo, Y., & Li, M., Thummaphan, P. (2016). Implementing formative assessment against a fine-grained learning progression into instruction design in physics.  Paper presented on International Test Commission (ITC) 2016 Conference. Vancouver, Canada.

  7. Thummaphan, P., Li, M., Popović, Z., Duisberg, R., Szeto, R., Zhai, X., & Gorsky, G. (2016). Examining the relationship of characteristics of word problems and item parameters in the context of an online math game. Paper presented at 2016 AERA: Washington DC. US.

  8. Zhai, X., Zhang, M., & Guo, Y. (2016). Smart classroom: the impacts brought to traditional physics learning. Paper presented at 2016 AAPT annual meeting: California. US.

  9. Zhai, X., & Sun, Wei. (2016).The change brought by smart-classroom: implementing, integration & impact—— based on the longitude data of physics learning in a high school. Paper presented at Global Chinese Conference on Innovation & Applications in Inquiry Learning, Shenzhen, China.

  10. Zhu, Q., Giliberto, J. P., Carlson, S.,Zhai, X., & Meyer, T. K. A. Voice range profile with duration as a third variable. Paper presented at the 2016 Fall Voice Conference. Scottsdale, Arizona, US.

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2015

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  1. Zhai, X., & He, C. (2017). The aspects of scientific modeling competence in physics for middle school students--the elaborations from the perspectives of both the physicists and students. The Physics Teacher. (12), 2-7. 

  2. He, C., & Zhai, X. (2016). Modeling-based instruction of “The implementation of Newton's Law of Universal Gravitation”. Reference to Middle School Physics. 45(Z1):13-16. 

  3. Zhai, X., Sun, W., Guo, Y., & Zhang, M. (2016). Smart classroom: an evaluation of its implementations and impacts -- based on the longitude data of physics learning in a high school. China Educational Technology. (9), 121-127.

  4. Zhai, X., & Guo, Y., Li, M. (2015). Developing learning progressions: nature and instruction practice strategies. Educational Science. 31(2), 47-51. 

  5. Alonzo, A. C., & Zhai, X. (2015). Learning progressions: an effective way to describe students’ understanding. The Physics Teacher. 36(11): 73-76. 

  6. Zhai, X., & Guo, Y. (2015). Progression of physics core competence for one hundred years—from the perspective of analyzing the objectives of physics curriculum standards and teaching syllabus.Curriculum, Teaching Materials and Method. (9), 59-67. 

  7. Zhai, X., & Guo, Y. (2015). A review of scientific modeling competence: connotation, models and evaluation. Journal of Educational Studies.11(6), 75-82, 106. 

  8. Zhai, X., & Guo, Y. (2015). Overview and implications of scientific modeling research in recent 30 years in US. Global Education. 341(12), 81-95. 

  9. Zhai, X., & Guo, Y. (2015). Analyzing international physics education research hotspots in recent 10 years and its enlightenment. Global Education.44(5),107-119. 

  10. Zhai, X., & Guo, Y., Xiang, Y. (2015). A case study of modeling-based inquiry. The Physics Teacher. 36（7）: 31-35.

  11. Zhai, X., & Xiang, H. (2015). S-WebQuest based on theme inquiry model—A case research on depth of integration of information technology into physics teaching. China Educational Technology. 340(5): 130-134. 

  12. Zhai, X., & Li, C. (2015). Refining the apparatuses based on experimental theory. Reference to Middle School Physics.44(11):69-71.

  13. Zhai, X., Guo, Y., & Zhang, Y. (2015). The relationship between teachers’ learning progression-based instruction design determination levels and instruction results-3 different teachers’ case study. Proceedings of The Fourth International Conference of East-Asian Association for Science Education (EASE), Beijing, China, 2015, (7): 38-39.

  14. Zhang, X., & Zhai, X.(2015). Theory and Practice of Scientific Method in Physics Education. Beijing, CN: Beijing Normal University Press.

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 Before 2014

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  1. Zhai, X., Wang, N., & Xiang, H. (2011). Application and enlightenment for "spotlight" tool of electronic whiteboard in physics teaching. China Educational Technology.  11, 35-40. 

  2. Li, J., Zhang, J., Guo, F., & Zhai, X., Zhang, J. (2011). Analysis of teachers’ blog survey based on the perspective of value philosophy. China Educational Technology. (1), 86-89. 

  3. Zhai, X., & Xiang, H. (2011). The morphology of scientific inquiry and the implications for science education. Teaching Reference to Middle School Physics. (11), 2-5.

  4. Zhai, X., & Xiang, H. (2011). Analyzing and modeling middle school physics "research learning" activities. Journal of Middle School Math& Physics and Chemistry. 2, 39-41. 

  5. Zhai, X., & Xiang, H. (2011). The present situation and countermeasures of research learning. Reference to Middle School Physics. 20 (Z1), 11-13. 

  6. Zhang, X., & Zhai. X. (2011). Several interesting "three" in physics experiment. Reports of Hechi College. 31(05), 1-5. 

  7. Zhai, X., Luo, Z., & Lu, C. (2010). Physics teaching strategies based on cognitive conflict. Reference to Middle School Physics.  39 (5), 4-9.

  8. Zhai, X., Luo, Z., & Lu, C. (2010). Toyota car "Brake Event" events and BOS system.  Reference to Middle School Physics. 39(06), 39-40.

  9. Zhai, X.(2008). Inquiry teaching--” verify the law of conservation of mechanical energy. Reference to Middle School Physics.(12), 21-24. 

  10. Zhang, X., & Zhai, X.(2013). Scientific Method for Physics Education Video Tutorial. Guang Zhou, CN: Guangdong Education Press.

  11. Lu, C., & Zhai, X.,etc. (2008). Innovative Design for Senior High School Physics. Beijing, CN: Yanbian Press.

  12. Lu, C., & Zhai, X.,etc. (2007). Optimization Design of High School Physics. Beijing, CN: Yanbian Press.

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