Mengxiao Zhu

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Dr. Mengxiao Zhu is a Distinguished Research Fellow in the Department of Science and Technology Communication and School of Humanities and Social Sciences, at University of Science and Technology of China. She earned her Ph.D. Degree from the Department of Industrial Engineering and Management Sciences at Northwestern University. She holds degrees in Communication (M.A.) from the University of Illinois at Urbana-Champaign, Computer Science (M.E. & B.E.) and Science and English (B.S.) from the University of Science and Technology of China. Before joining USTC, she worked as a Research Scientist in the Research and Development division at Educational Testing Service (ETS) for over seven years. Prior to that, she also worked as a Post-doctoral Research Associate in the School of Communication and Information at Rutgers University, and a Graduate Research Assistant in the Science of Networks in Communities (SONIC) Research Group led by Professor Noshir Contractor. She has been involved in several NSF and NIH -funded projects focusing on computer-mediated communication in emergency response teams, and on the development of knowledge networks and the dynamics of collaborations both in real world, such as research institutions, and in virtual worlds, such as Second Life and online role-playing games. Her current research focuses on psychometrics for the new generation of assessments, including psychometric models for collaborative problem solving, data mining techniques applied on assessment data, simulations and games in assessment, and integration of cognitive science with psychometrics.

Current Projects

Big Data in Education

Leveraging the power of data science and learning analytics to assist education.

Collaborative Problem Solving

Understanding, assessing and training of collaborative problem solving skills for teams and individuals.

Social Media Studies

Exploring how the impact of social media evolves over time, and the role of social media in organizations.

Recent Publications

User/Student Behavior Analytics

Models: network analysis; sequence analysis
Data sources: process data from scenario-based tasks; writing keystroke logs; eye tracking; forum participation and online learning

  1. Zhu, M. (in press). Chapter 11. Social Networks Analysis. In A. A. von Davier, B. Mislevy, & J. Hao (Eds.), Computational Psychometrics: New Methods for a New Generation of Educational Assessment. Springer.
  2. Zhu, M., Zhang, M., & Deane, P. (2019). Analysis of Keystroke Sequences in Writing Logs. ETS Research Report Series RR-19-11.
  3. Zhang, M., Zhu, M., Deane, P., & Guo, H. (2019). Identifying and Comparing Writing Process Patterns Using Keystroke Logs. In Quantitative Psychology: The 83nd Annual Meeting of the Psychometric Society, New York. New York: Springer.
  4. Shu, Z., Bergner, Y., Zhu, M., & Hao, J. (2017). An item response theory analysis of problem-solving processes in scenario-based tasks. Psychological Test and Assessment Modeling, 59(1), 109–131.
  5. Zhu, M., Shu, Z., & von Davier, A. A. (2016). Using networks to visualize and analyze process data for educational assessment. Journal of Educational Measurement, 53(2), 190–211.
  6. Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., Paquette, L., & Barnes, T. (2016). Longitudinal Engagement, Performance, and Social Connectivity: a MOOC Case Study Using Exponential Random Graph Models. In Proceeding of the 6th International Learning Analytics and Knowledge Conference (LAK ’16). Edinburgh, UK.
  7. Zhu, M., & Feng, G. (2015). An exploratory study using social network analysis to model eye movements in mathematics problem solving. In Proceeding of the 5th International Learning Analytics and Knowledge Conference (LAK ’15). Poughkeepsie, NY: ACM.

Collaborative Problem Solving

Models: network analysis; agent-based modeling
Data sources: simulation-bases task; complex group-based assessment; simulated data

  1. Zhu, M., Andrews-Todd, J., & Zhang, M. (in press). Application of Network Analysis in Understanding Collaborative Problem Solving Processes and Skills. In H. Jiao & R. W. Lissitz (Eds.), Innovative Psychometric Modeling and Methods (pp. xxx–xxx). Charlotte, NC: Information Age Publisher.
  2. Zhu, M., & Todd, J. A. (2019). Understanding the Connections of Collaborative Problem Solving Skills in a Simulation-based Task through Network Analysis. In The Proceedings of the International Conference on Computer Supported Collaborative Learning (CSCL 2019). Lyon, France.
  3. Zhu, M., & Bergner, Y. (2017). Network Models for Teams with Overlapping Membership. In A. A. von Davier, M. Zhu, & P. Kyllonen (Eds.), Innovative Assessment of Collaboration. Springer.
  4. Zhu, M., & Zhang, M. (2017). Network analysis of conversation data for engineering professional skills assessment. ETS Research Report RR-17-59.
  5. von Davier, A. A., Zhu, M., & Patrick, K. (2017). Innovative Assessment of Collaboration. Springer.
  6. Bergner, Y., Andrews, J. J., Zhu, M., & Gonzales, J. E. (2016). Agent-based modeling of collaborative problem solving. Educational Testing Service Research Report Series (No. RR-16-27). Princeton, NJ.
  7. Zhu, M., Kuskova, V., Wasserman, S., & Contractor, N. (2016). Correspondence Analysis of Multirelational Multilevel Network Affiliations: Analysis and Examples. In E. Lazega & T. Snijders (Eds.), Multilevel Network Analysis for the Social Sciences - Theory, Methods and Applications. Springer. 145–172.
  8. Zhu, M., & Zhang, M. (2016). Examining the patterns of communication and connections among engineering professional skills in group discussion: A network analysis approach. In Proceedings of the 2016 IEEE Integrated STEM Education Conference. Princeton, NJ.
  9. Zhu, M., Huang, Y., & Contractor, N. S. (2013). Motivations for self-assembling into project teams. Social Networks, 35(2), 251-264.

Automated Feedback

Models: feature development; cluster analysis; sequence analysis
Data sources: online learning module with automated scoring and feedback

  1. Zhu, M., Liu, O. L., & Lee, H.-S. (in press). Using Cluster Analysis to Explore Students’ Interactions with Automated Feedback in an Online Earth Science Task. The International Journal of Quantitative Research in Education.
  2. Zhu, M., Liu, O. L., & Lee, H.-S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143, 103668.
  3. Zhu, M., Lee, H.-S., Wang, T., Liu, O. L., Belur, V., & Pallant, A. (2017). Investigating the impact of automated feedback on students’ scientific argumentation. International Journal of Science Education, 39(12), 1648–1668.
  4. Zhu, M., Liu, O. L., Mao, L., & Pallant, A. (2016). Use of Automated Scoring and Feedback in Online Interactive Earth Science Tasks. In Proceedings of the 2016 IEEE Integrated STEM Education Conference. Princeton, NJ.

Contact Info

Mengxiao Zhu

Email: mxzhu at


Office: Fusion Building Room 315, North Campus of USTC