Wanli Xing

Wanli Xing 

Associate Professor

Wanli Xing





PO Box 117048
Gainesville, FL 32611


Wanli Xing's research focuses on how emerging technologies can deeply transform STEM education and online learning. He creates learning environments using cutting-edge technologies, such as computer simulations and modeling, artificial intelligence, internet of things, and augmented reality to support learning in diverse classrooms and online environments. He also designs and applies data mining and machine learning to understand, assess, and optimize the learning process and the environments in which it occurs. His school-based research projects have produced new theories, principles, and methods on how to design effective educational technology that teaches the knowledge and skills for the future STEM workforce.


  • School of Teaching and Learning
  • Institute for Advanced Learning Technologies

Research Interests

Assessment and Evaluation, Autism Spectrum Disorders, Behavior, Cognition, Collaboration and Teaming, Data Collection and Analysis, Dropout Prevention, Educational / Instructional Design, Emerging Technologies, Mathematics Education, Online and Distance Education, Quantitative Research, Science Education, Statistics / Applied Stats, Technology Trends and Issues


  • Ph.D. in Information Science and Learning Technologies, 2016 University of Missouri, Columbia
  • B.Ed. in Educational Technology, 2009 Jilin Normal University

Professional Appointments

  • Assistant Professor, Educational Technology, School of Teaching and Learning, University of Florida, 2019 - Present
  • Assistant Professor, Educational Technology, Department of Educational Psychology and Leadership, Texas Tech University 2017- 2019

Activities and Honors

  • Best Journal Article Award for the Association for Educational Communications and Technology (AECT) 2019 conference, Research and Theory Division, 2019
  • Best Poster Award, 2015 The Consortium for the Science of Sociotechnical Systems Researchers, 2015

Selected Grants

A Logic Programming Approach to Integrate Computing with Middle School Science Education

  • Co-PI
Funding Agency
  • National Science Foundation, STEM + Computing Program
Project Period
  • 2019 - 2022
Award Amount
  • $388,955

Design Artificial Intelligence and Analytics for Deep STEM Learning

  • PI
Funding Agency
  • National Science Foundation, Discovery Research PreK-12 Program
Project Period
  • 2018 - 2020
Award Amount
  • $279,999

Dashboards with Temporal Scaffolds: Using Educational Data Mining to Increase Temporal Participation in Online Courses

  • Co-PI
Funding Agency
  • PSU Center for Innovation in Online Learning
Project Period
  • 2017 - 2018
Award Amount
  • $40,000

Selected Publications

  • Xing, W., Popov, V., Zhu, G., Horwitz, P., & McIntyre, C. (2019). The effects of transformative and non-transformative discourse on individual performance in collaborative-inquiry learning. Computers in Human Behavior, 98, 267-276.
  • Zheng, J., Xing, W., Zhu, G., Chen, G., Zhao, H., & Xie, C. (2019). Profiling self-regulation behaviors in STEM learning of engineering design. Computers & Education, 143, 103669.
  • Xing, W. (2019). Large-scale path modeling of remixing to computational thinking. Interactive Learning Environments, 1-14.
  • Pei, B., Xing, W., & Lee, H. S. (2019). Using automatic image processing to analyze visual artifacts created by students in scientific argumentation. British Journal of Educational Technology, 1-14.
  • Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. The Internet and Higher Education, 43, 100690.
  • Xing, W. (2019). Exploring the influences of MOOC design features on student performance and persistence. Distance Education, 40(1), 98-113.
  • Zhu, G., Xing, W., Costa, S., Scardamalia, M., & Pei, B. (2019). Exploring emotional and cognitive dynamics of Knowledge Building in grades 1 and 2. User Modeling and User-Adapted Interaction, 1-32.
  • Xing, W., & Du, D. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 547-570.
  • Zhu, G., Xing, W., & Popov, V. (2019). Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning. The Internet and Higher Education, 41, 51-61.
  • Xing, W., & Gao, F. (2018). Exploring the relationship between online discourse and commitment in Twitter professional learning communities. Computers & Education, 126, 388-398.
  • Xing, W., Goggins, S., & Introne, J. (2018). Quantifying the effect of informational support on membership retention in online communities through large-scale data analytics. Computers in Human Behavior, 86, 227-234.
  • Wang, X., Xing, W., & Laffey, J. M. (2018). Autistic youth in 3D game?based collaborative virtual learning: Associating avatar interaction patterns with embodied social presence. British Journal of Educational Technology, 49(4), 742-760.
  • Tang, H., Xing, W., & Pei, B. (2018). Exploring the temporal dimension of forum participation in MOOCs. Distance Education, 39(3), 353-372.
  • Wang, X., Laffey, J., Xing, W., Galyen, K., & Stichter, J. (2017). Fostering verbal and non-verbal social interactions in a 3D collaborative virtual learning environment: A case study of youth with Autism Spectrum Disorders learning social competence in iSocial. Educational Technology Research and Development, 65(4), 1015-1039.
  • Xing, W., Chen, X., Stein, J., & Marcinkowski, M. (2016). Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, 119-129.
  • Goggins, S., & Xing, W. (2016). Building models explaining student participation behavior in asynchronous online discussion. Computers & Education, 94, 241-251.
  • Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168-181.
  • Xing, W., Wadholm, R., Petakovic, E., & Goggins, S. (2015). Group learning assessment: Developing a theory-informed analytics. Journal of Educational Technology & Society, 18(2), 110-128.

Selected Links