2023
Zhai, X.& Lu, M.(2023). Machine Learning Applications in Educational Studies. Frontiers in Education.8, https://doi.org/10.3389/feduc.2023.1225802
Zhai, X., Neumann, K., & Krajcik (2023). AI for Tackling STEM Education Challenges. Frontiers in Education.8,https://doi.org/10.3389/feduc.2023.1183030
Zhai, X. & Nehm, R. (2023). AI and Formative Assessment: The Train Has Left the Station.Journal of Research in Science Teaching. DOI: 10.1002/tea.21885.
Bewersdorff, A., Zhai, X., Roberts, J., Nerdel, C. (2023). Myths, mis- and preconceptions of artificial intelligence: A review of the literature. Computers and Education: Artificial Intelligence, 2023, 100143, https://doi.org/10.1016/j.caeai.2023.100143.
Zhai, X. (2023). ChatGPT: Reforming Education on Five Aspects. Shanghai Education. 16-17 (in Chinese)
Zhai, X. (2023). ChatGPT for Next Generation Science Learning. XRDS: Crossroads. 29(3), 42-46. https://doi.org/10.1145/3589649
Wilson, C., Haudek, K., Osborne, J., Stuhlsatz, M., Cheuk, T., Donovan, B., Bracey, Z., Mercado, M., & Zhai, X. (2023). Using automated analysis to assess middle school students’ competence with scientific argumentation. Journal of Research in Science Teaching. 1– 32. https://doi.org/10.1002/tea.21864 (SSCI, IF = 4.832)
Liu, Z., He, X., Liu, L., Liu, T., & Zhai, X*. (2023). SciEdBERT-M: A Pretrained Language Model for Science Education. N. Wang et al. (Eds.): AI in Education 2023, CCIS 1831, pp. 1–9, 2023., Springer: Switzerland AG. https://doi.org/10.1007/978-3-031-36336-8_103
Wu, X., He, X., Liu, T., Liu N., & Zhai, X*. (2023). Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-shot Prompt Learning for Automatic Scoring in Science Education. N. Wang et al. (Eds.): AI in Education 2023, LNAI 13916, pp. 1–13, 2023., Springer: Switzerland AG. https://doi.org/10.1007/978-3-031-36272-9_33
Zhai, X., & Wiebe, E. (2023). Technology-based innovative assessment. In C. J. Harris, E. Wiebe, S. Grover, & J. W. Pellegrino (Eds.), Classroom-based STEM assessment: Contemporary issues and perspectives (pp. 83-94). Community for Advancing Discovery Research in Education, Education Development Center, Inc.https://cadrek12.org/
He, P., Zhai, X., Shin, N., & Krajcik, J. (2023). Using Rasch Measurement to Assess Knowledge-in-Use in Science Education. Liu, X. & Boone, W. (Eds.). Advances in Applications of Rasch Measurement in Science Education. Springer Nature.
Gao, Y., Zhai, X., Bae, A., & Ma, W. (2023). Rasch-CDM: Applying Rasch and Cognitive Diagnosis Models to Assess Learning Progression. In Liu, X. & Boone, W. (Eds.) (pp. xx-xx). Advances in Applications of Rasch Measurement in Science Education. Springer Nature.
Zhai, X. & Pellegrino, J. W. (2023). Large-Scale Assessment in Science Education. In N. G. Lederman, D.L. Zeidler, & J.S. Lederman (Eds.), Handbook of Research on Science Education, Volume III (pp. 1045- 1098). New York, NY: Routledge.
Cao, C., Ding, Z., Jiao J., & Zhai, X., (2023). Demystifying STEM Concepts through Generative AI: A Multimodal Exploration of Analogical Reasoning. IJCAI2023 Multi Reasoning (Multimodal Reasoning: Techniques, Applications, and Challenges). https://arXiv preprint arXiv:2308.10454
Liu, Z., He, X., Liu, L., Liu, T., & Zhai, X. (2023). Context Matters: A Strategy to Pre-train Language Model for Science Education. In N. Wang & e. al. (Eds.), AI in Education 2023 (Vol. CCIS 1831, pp. 1-9). Springer. https://doi.org/10.1007/978-3-031-36336-8_103
Wu, X., He, X., Liu, T., Liu, N., Zhai, X. (2023). Matching Exemplar as Next Sentence Prediction (MeNSP): Zero-Shot Prompt Learning for Automatic Scoring in Science Education. In: Wang, N., Rebolledo-Mendez, G., Matsuda, N., Santos, O.C., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2023. Lecture Notes in Computer Science, vol 13916. Springer, Cham. https://doi.org/10.1007/978-3-031-36272-9_33
Zhai, X. & Krajck, J. (2023). Using AI to Promote Equitable Science Teaching and Learning. Session presented at the CADRE PI meeting. Washington DC.
Zhai, X. & Crippen, K. (2023, Chair and Presider). Research in Artificial Intelligence-involved Science Education. Symposium presented at the Annual conference of National Association of Research in Science Teaching, Chicago, IL.
Zhai, X. (2023, Chair and Presider). Structural Poster Session. AI-Augmented Assessment Tools to Support Instruction in STEM. Session presented at the 2023 annual conference of the American Educational Research Association, Chicago.
Zhai, X., (2023). ChatGPT for Next Generation Science Learning.. Paper presented at ESERA annual conference, Cappadocia, Turkey.
Krajcik, J., He, P., Shine, N., & Zhai, X., (2023). Using Artificial Intelligence to Support Teachers’ Use of Instructional Supports to Improve Students’ Useable Knowledge: A Conceptual Framework. Paper presented at ESERA annual conference, Cappadocia, Turkey.
Zhai, X. & Shine, N. (2023). The Potential of An Automatically Scored Three-dimensional Assessment System. In the Session: Using AI to Promote Equitable Science Teaching and Learning. CADRE PI meeting. Washington DC.
He, P., Shin N., Amerman, H., Zhai, X., Krajcik, J. (2023). Designing Artificial Intelligence Instructional Support System for Improving Student Knowledge-in-Use in Science Classrooms. Paper presented at the annual meeting of the International Society of the Learning Sciences, Montreal, Canada.
He, X., Zhai, X., Latif, E., & Krajcik, J. (2023). Developing artificial intelligence-based automatic assessment report. Paper presented at the annual meeting of the International Society of the Learning Sciences, Montreal, Canada.
He, P., Shin N., Amerman, H., Zhai, X., Krajcik, J. (2023). Human and artificial intelligence-based automatic scoring of student performance on knowledge-in-use assessment tasks. Paper presented at the annual meeting of the International Society of the Learning Sciences, Montreal, Canada.
Amerman, H., Zhai, X., Latif, E., He, P., & Krajcik, J. (2023). Does Transformer Deep Learning Yield More Accurate Sores on Student Written Explanations than Traditional Machine Learning? Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Panjwani, S. & Zhai, X. (2023). AI for Students with Learning Disabilities: A Systematic Review. Paper presented at the Annual Conference of National Association of Research in Science Teaching, Chicago, IL.
Latif, E., Amerman, H., & Zhai, X. (2023). Hybrid Neural Network for Automated Multi-Perspective Textual Response Assessment. Paper presented the Annual Conference of National Association of Research in Science Teaching, Chicago, IL.
He, X. & Zhai, X. (2023). Design Matter: How do Teachers Interpret AI-based Assessment Reports? Paper submitted to the Annual Conference of National Association of Research in Science Teaching, Chicago, IL.
Amerman, H. & Zhai, X. (2023). Teacher Acceptance of Artificial Intelligence Technologies for Teaching and Learning: A Systematic Review. Paper presented at the Annual Conference of National Association of Research in Science Teaching, Chicago, IL.
Zhai, X. (2023). Machine Learning-based Assessment for Evidentiary Inference. Paper presented at the Annual Conference of National Association of Research in Science Teaching, Chicago, IL.
He, P., Shin, N., Zhai, X., & Krajcik, J. (2023). Guiding Teacher Uses of Artificial Intelligence Based Classroom Assessment to Improve Instructional Decisions: A Conceptual Framework. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
He, X., Chen, Y., & Zhai, X. (2023). Automatically Generated Assessment Reports for Teachers’ Formative Uses: A Review of Dashboard Design. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Shin, N., He, P., Nilsen, K., Amerman, H., Krajcik, J., & Zhai, X. (2023). Design Model for Pedagogical Content Knowledge Supports based on AI-Automated Scores. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
He, P., Shin, N., Amerman, H., Zhai, X., & Krajcik, J. (2023). Designing and Applying Scoring Rubrics for Automatically-Scored Knowledge-in-Use Assessment Tasks for Instructional Decisions. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Zhai, X., He, X., Latif, E., He., P., Krajcik, J., Yin, Y., Harris, C.. (2023). Teacher Interpretation of AI-Augmented Assessment Reports. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Zhai, X., He, X., Amerman, H., & Panjwani, S. (2023). Validity Issues of Teachers’ Interpretation and Use of Automatic Scores. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Latif, E., & Zhai, X. (2023). AI-SCORER: Principles for Designing Artificial Intelligence-Augmented Instructional Systems. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Zhai, X., & Wiebe, E. (2023) Technology-based Assessment in STEM+C. Paper presented at the Annual Meeting of the American Educational Research Association, Chicago.
Zhai, X. & Krajcik, J. (forthcoming). Uses of Artificial Intelligence in STEM Education. Oxford University Press.
Lu, C., Liu, H., Zhai, X., Qian, D. (in press). Examining the Impact of Modeling-based Inquiry with Smart Classroom on High School Students’ Physics Learning. Chinese Journal of ICT in Education (in Chinese), 11, xx-xx.
Zhai, X. (in press). AI and Machine Learning for Next Generation Science Assessments. Jiao, H., & Lissitz, R. W. (2023, in progress). Machine learning, natural language processing and psychometrics. Charlotte, NC: Information Age Publisher.
Zhai, X. & Krajcik, J. (in press). Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.
Panjwani-Charani, S. & Zhai, X. (in press). AI for Students with Learning Disabilities: A Systematic Review. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.
Herdliska, A. & Zhai, X. (in press). AI-based scientific inquiry. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.
Wang, C., Zhai, X., & Shen J. (in press). Applying Machine Learning to Assess Paper-Pencil Drawn Models of Optics. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.
He, P., Shin, N., Zhai, X., & Krajcik, J., (in press). Guiding Teacher Use of Artificial Intelligence- Based Knowledge-in-Use Assessment to Improve Instructional Decisions: A Conceptual Framework. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xx-xx). Oxford, UK: Oxford University Press.
2022
He, X., Chen, Y., Zhai, X., and Yin, Y. (2022). Reviewing automatically generated assessment reports for teachers’ formative uses. Paper presented at the International Conference for AI-based Assessments in STEM Education, Athens, Georgia. (Click Here for Presentation)
Latif, E., Zhai, X., He, X., & Amerman, H. (2022). AI-scorer: An AI-augmented teacher feedback platform. Paper presented at the International Conference for AI-based Assessments in STEM Education, Athens, Georgia. (Click Here for Presentation)
Panjwani, S. & Zhai, X. (2022). AI for students with learning disabilities: A systematic review. Paper presented at the International Conference for AI-based Assessments in STEM Education, Athens, Georgia. (Click Here for Presentation)
Zhai, X., He, P., & Krajcik, J. (2022) Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching. DOI:10.1002/tea.21773 (SSCI, IF = 4.832).
Yin, Y., Khaleghi, S., Hadad., R., & Zhai., X. (2022).Developing effective and accessible activities to improve and assess computational thinking and engineering learning. Educational Technology Research and Development. https://doi.org/10.1007/s11423-022-10097-w (SSCI, IF = 3.565).
Zhai, X. (2022). Assessing high-school students’ modeling performance on Newtonian mechanics. Journal of Research in Science Teaching. DOI: 10.1002/tea.21758 (SSCI, IF = 4.832).
Gao, Y., Cui, Y., Bulut, O., Zhai., X. & Chen, F. (2022) Examining adults’ web navigation patterns in multi-layered hypertext environments. Computers in Human Behavior. 129 (107142). https://doi.org/10.1016/j.chb.2021.107142 (SSCI, IF = 6.829).
Zhai, X. & Pellegrino, J. W. (Forthcoming). Large-Scale Assessment in Science Education. Handbook of Research in Science Education (Vol. III.). Routledge.
Ma, W., Sorrel, M. A., Ge, Y., & Zhai, X. (2022, April). A dual-purpose model for estimating ability and misconceptions. Paper presented at the Annual Meeting of the National Council on Measurement in Education, San Diego.
Zhai, X. (2022). Symposium: AI-based Innovative Assessments in Science. Symposium presented at the 2022 annual conference of National Association of Research in Science Teaching, Vancouver, BC, Canada.
Zhai, X. (2022). Machine Learning Scoring Bias on Students that are Underrepresented in STEM. Paper presented at the 2022 annual conference of National Association of Research in Science Teaching, Vancouver, BC, Canada.
Minstrell, J., Hernandez, P., Li, M., Anderson, R., Ruiz-Primo, M., Zhai, X., Dong, D., & Kanopka, K. (2022).Mining the Potential of “Wrong Answers” in Item Pairs to Describe Students’ Alternative Thinking. Paper presented at the 2022 annual conference of National Association of Research in Science Teaching, Vancouver, BC, Canada.
Amerman, H., Zhai, X. Krajcik, J. (2022). Science Teachers’ Perception of AI-based Learning Technologies in Classroom. Paper presented at the 2022 annual conference of National Association of Research in Science Teaching, Vancouver, BC, Canada.
Zhai, X., Heil, A., (2022). How Linguistic Features of Written Arguments are Associated with Machine Scoring Confidence? Paper presented at the 2022 annual conference of National Association of Research in Science Teaching, Vancouver, BC, Canada.
Wang, C., Zhai, X., Shen, J., Herdiska, A. (2022). Applying Machine Learning to Assess Paper-Pencil Drawings of Optics. Pape presented at the 2022 annual conference of National Association of Research in Science Teaching, Vancouver, BC, Canada.
2021
Gao, Y., Cui, Y., Bulut, O., Zhai, X., & Chen, F. (2021). Examining adults’ web navigation patterns in multi-layered hypertext environments. Computers in Human Behavior, 129. https://doi.org/10.1016/j.chb.2021.107142 (Click Here for PDF)
Gao, Y., Zhai, X., Cui, Y., Xin,T., & Bulut,O. (2021). Re-validating a Learning Progression of Buoyancy for Middle School Students: A Longitudinal Study. Research in Science Education. https://doi.org/10.1007/s11165-021-10021-x (Click Here for PDF)
Zhai, X., Haudek, K. C., Wilson, C., & Stuhlsatz, M. (2021). A Framework of Construct-Irrelevant Variance for Contextualized Constructed Response Assessment. Frontiers in Education, 6. https://doi.org/10.3389/feduc.2021.751283 (Click Here for PDF)
Gao, Y., Zhai, X., Cui, Y., Chen, F., Xin, T. (Accepted). Re-examining a learning progression of buoyancy: A longitudinal study. Research in Science Education. (SSCI, IF = 5.439)
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 (SSCI, IF = 2.241)
Zhai, X. (2021). Practices and theories: How can machine learning assist in innovative assessment practices in science education. Journal of Science Education and Technology. 30(2), 139-149. DOI: 10.1007/s10956-021-09901-8 (SSCI, IF = 2.315)
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. 30(2), 239-254. DOI: 10.1007/s10956-020-09895-9 (SSCI, IF = 2.315)
Zhai, X. (2021). Advancing automatic guidance in virtual science inquiry: From ease of use to personalization. Educational Technology Research and Development. 69(1), 255-258. DOI: 10.1007/s11423-020-09917-8 (SSCI, IF = 3.565)
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. 30(2), 298-312. DOI: 10.1007/s10956-020-09879-9 (SSCI, IF = 2.315)
Zhai, X., Shi, L. Nehm, R. (2021). A Meta-analysis of machine learning-based science assessments: Factors impacting machine-human score agreements. Journal of Science Education and Technology. 30(3), 361-379. DOI: 10.1007/s10956-020-09875-z (SSCI, IF = 2.315)
Maestrales, S., Zhai, X., Touitou, I., Baker, Q., Schneider, B., Krajcik, J. (2021). Using Machine Learning to Score Multi-dimensional Assessments of Chemistry and Physics. Paper submitted to the 2021 annual meeting of the World Education Research Association, Galicia, Spain.
Zhai, X., Haudek, K. (2021). Applying Cognitive Diagnostic Modeling to Examine Middle-School Students’ Argumentation Practices. Paper submitted to the 2021 annual conference of the American Educational Research Association, Virtual meeting.
Zhai., X., Haudek, K., Stuhlsatz, M., Wilson, C. (2021). Using Many Facet Rasch Measurement to Investigate Construct-Irrelevant Variance for Contextualized Constructed-Response Assessment. Paper submitted to the 2021 annual conference of the American Educational Research Association, Virtual meeting.
Zhai, X., Yang, J., Li, T., He, P., Krajcik, J. (2021). Applying machine learning to automatically evaluate student scientific modeling competence. Paper submitted to the 2021 annual conference of the National Association of Research in Science Teaching, Orlando, Florida.
Morell, L., …, Zhai, X. (2021). Automated Assessment of Argumentation in School Science: Developments and Challenges. Paper submitted to the 2021 annual conference of the National Association of Research in Science Teaching, Orlando, Florida.
Haudek, K., Zhai, X. (2021). Exploring the Effect of Construct Complexity on Machine Learning Assessments of Argumentation. Paper submitted to the 2021 annual conference of the National Association of Research in Science Teaching, Orlando, Florida.
2020
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 (SSCI, IF = 8.538)
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, IF = 8.538)
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 (SSCI, IF = 4.832)
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 (SSCI, IF = 1.953)
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. (SSCI, IF = 3.417)
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 (SSCI, IF = 2.315)
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 (SSCI, IF = 2.412)
Zhai, X. (2020). The strategy for teaching physics ideas. Beijing, CN: Beijing Normal University Press. (In Chinese)
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)
Zhai, X., Haudek, K., Wilson, C., Chuek, T., Osborne, J. (2020) 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. (Conference canceled)
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 at the 2020 annual conference of the American Educational Research Association, California.
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)
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)
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). Papers included:
- Paper. Construct-Irrelevant Variance Yielded in an Automatically Scored Science Teacher PCK Assessment, by Xiaoming Zhai, CREATE for STEM Center, Michigan State University
Zhai., X., Haudek, K., Stuhlsatz, M., Wilson, C. (2020). An Approach to Investigating Construct-Irrelevant Variance for Contextualized Constructed-Response Assessment. Paper presented at the 2020 annual conference of the American Educational Research Association, California. (Conference canceled)
Gane, B., Zaidi, S., Zhai., X., Pellegrino, J. (2020). Using Machine Learning to Score Tasks that Assess Three-dimensional Science Learning. Paper presented at the 2020 annual conference of the American Educational Research Association, California. (Conference canceled)
Zhai, X. (2020, Organizer and Presider). Applying Machine Learning in Next Generation Science Assessment. Session presented at the 2020 annual conference of the American Educational Research Association, California. (Conference canceled).
2019
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. (SSCI, IF = 2.241)
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. (SSCI, IF = 1.976)
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. (SSCI, IF = 4.929)
Zhai, X., Haudek, K., (2019). Comparison of construct-irrelevant variance 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.
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.
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.
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.
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.
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.
2018
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. (SSCI, IF = 4.929)
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 (SSCI, IF = 5.439)
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 (SSCI, IF = 2.241)
Dong, D., Hsiao, Y., Weihs, L., Li, M., Minstrell, J., Ruiz-Primo, M. A., & Zhai, X. (2018). Causal impact of exam problem context on student performance: A generalized linear mixed model approach. Paper presented at the annual conference of the American Educational Research Association, New York, NY.
Zhai, X., & Li, M. (2018). Does higher extent of mobile-technology-integrated physics learning indicate greater effects? Paper presented at the National Association of Research in Science Teaching, Atlanta, GA.
Zhai, X., Li, M., Zhang, X., Chen, S., & Dong, D. (2018). The usages and effects of student-teacher led functions of mobile technology toward high-school physics learning. Paper presented at the American Educational Research Association, New York, NY.
2017
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, 38(12), 2-7. (In Chinese)
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 the National Council on Measurement in Education, San Antonio, TX.
Li, M., Ruiz-Primo, M. A., Dong, D., Minstrell, J., Zhai, X., & Thummaphan, P. (2017). Issues for developing science contextualized items. Paper presented at the National Council on Measurement in Education, San Antonio, TX.
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 the National Association of Research in Science Teaching, San Antonio, TX.
2016
He, C., & Zhai, X. (2016). Modeling-based instruction of “The implementation of Newton’s Law of Universal Gravitation”. Reference to Middle School Physics, 45(1-2),13-16. (In Chinese)
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, 356(9), 121-127. (In Chinese)
Dong, D., Li, M., Zhai, X., & Chen, S. (2016). Examining student thinking through learner-generated drawings. Paper presented on the National Association of Research in Science Teaching, Baltimore, MD.
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 the American Educational Research Association, Washington, DC.
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 at the International Test Commission (ITC), Vancouver, Canada.
Zhai, X., Guo, Y., & Li, M. (2016). Detecting the components of scientific modeling competence. Paper presented at the National Association of Research in Science Teaching, Baltimore, MD.
Zhai, X., Guo, Y., Li, M., & Thummaphan, P. (2016). Detecting the relationship of scientific modeling competence, conceptual understanding and relative abilities for high school students. Paper presented at the American Educational Research Association, Washington, DC.
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 the International Test Commission (ITC), Vancouver, Canada.
Zhai, X., & Sun, W. (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 the Global Chinese Conference on Innovation & Applications in Inquiry Learning, Shenzhen, China.
Zhai, X., Zhang, M., & Guo, Y. (2016). Smart classroom: The impacts brought to traditional physics learning. Paper presented at the American Association of Physics Teachers, Sacramento, CA.
Zhu, Q., Giliberto, J. P., Carlson, S., Zhai, X., & Meyer, T. K. A. (2016). Voice range profile with duration as a third variable. Paper presented at the Fall Voice Conference, Scottsdale, AZ.
2015
Alonzo, A. C., & Zhai, X. (2015). Learning progressions: An effective way to describe students’ understanding. The Physics Teacher, 36(11), 73-76. (In Chinese)
Zhai, X., & Guo, Y. (2015). Analyzing international physics education research hotspots in recent 10 years and its enlightenment. Global Education, 44(5),107-119. (In Chinese)
Zhai, X., & Guo, Y. (2015). Overview and implications of scientific modeling research in recent 30 years in the US. Global Education, 341(12), 81-95. (In Chinese)
Zhai, X., & Guo, Y., Chen, 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, 35(9), 59-67. (In Chinese)
Zhai, X., & Guo, Y. (2015). A review of scientific modeling competence: Connotation, models and evaluation. Journal of Educational Studies, 11(6), 75-82, 106. (In Chinese)
Zhai, X., & Guo, Y., Li, M. (2015). Developing learning progressions: Nature and instruction practice strategies. Educational Science, 31(2), 47-51. (In Chinese)
Zhai, X., & Guo, Y., Xiang, Y. (2015). A case study of modeling-based inquiry. The Physics Teacher, 36(7), 31-35. (In Chinese)
Zhai, X., & Li, C. (2015). Refining the apparatuses based on experimental theory. Reference to Middle School Physics, 44(11), 69-71. (In Chinese)
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. (In Chinese)
Zhang, X.,… & Zhai, X. (2015). The theory and practice of using scientific methods in physics education. Beijing, CN: Beijing Normal University Press. (In Chinese)
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. Paper presented at the International Conference of East-Asian Association for Science Education (EASE), Beijing, China.
Before 2014
Zhang, X.,… & Zhai, X. (2013). Scientific methods in physics education (video tutorial). Guang Zhou, CN: Guangdong Education Press. (In Chinese)
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, 276(1), 86-89. (In Chinese)
Zhai, X., Wang, N., & Xiang, H. (2011). Application and enlightenment for “spotlight” tool of electronic whiteboard in physics teaching. China Educational Technology, Special issue (11), 35-40. (In Chinese)
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. (In Chinese)
Zhai, X., & Xiang, H. (2011). The morphology of scientific inquiry and the implications for science education. Teaching Reference to Middle School Physics. 40(11), 2-5. (In Chinese)
Zhai, X., & Xiang, H. (2011). The present situation and countermeasures of research-based learning. Reference to Middle School Physics, 20(1-2), 11-13. (In Chinese)
Zhang, X., & Zhai. X. (2011). Several interesting “three” in physics experiments. Reports of Hechi College, 31(05), 1-5. (In Chinese)
Zhai, X., Luo, Z., & Lu, C. (2010). Physics teaching strategies based on cognitive conflict. Reference to Middle School Physics, 39(5), 4-9. (In Chinese)
Zhai, X., Luo, Z., & Lu, C. (2010). Toyota car “Brake Event” events and BOS system. Reference to Middle School Physics, 39(6), 39-40. (In Chinese)
Zhai, X. (2008). Inquiry teaching–verify the law of conservation of mechanical energy. Reference to Middle School Physics, 37(12), 21-24. (In Chinese)