Wanli Xing

Wanli Xing 

Associate Professor

Wanli Xing





PO Box 117048
Gainesville, FL 32611


Wanli Xing is an associate professor of Educational Technology in the School of Teaching and Learning at the University of Florida. He is the Director of Learning Engineering Virtual Institute and Advanced and Inclusive Computing for Education Lab. Dr. Xing’s research dedicated to pioneering strategies, frameworks, and technologies that revolutionize STEM education and online learning. He creates learning environments using cutting-edge technologies, such as artificial intelligence, computer simulations and modeling, internet of things, and augmented reality to support learning in diverse classrooms and online environments. He also designs and applies machine learning and data mining to understand, assess, and optimize the learning process and the environments in which it occurs. Dr. Xing’s research initiatives 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. His commitment to advancing learning sciences and technologies is reflected in the ample support received from federal agencies, state entities, and foundations.


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

Research Interests

Collaboration and Teaming, Computer Science Education, Data Collection and Analysis, Design and Development of Educational Technology Interventions, Educational / Instructional Design, Knowledge and Theory, Mathematics Education, Online and Distance Education, Quantitative Research, Science Education, 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 - 2023
  • Assistant Professor, Educational Technology, Department of Educational Psychology and Leadership, Texas Tech University 2017- 2019

Activities and Honors

  • Best Paper Award, The 22nd Annual ACM Interaction Design and Children Conference, 2023
  • Best Paper Award, The 13th International Learning Analytics and Knowledge Conference, 2023
  • 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

AI Across the Curriculum for Virtual Schools

  • Co-PI
Funding Agency
  • Department of Education EIR
Project Period
  • 2024 - 2028
Award Amount
  • $3,999,322

VETS-HASTE: Veterans SkillBridge through Industry based Hardware Security Training and Education

  • PI
Funding Agency
Project Period
  • 2023 – 2026
Award Amount
  • $1,000,000

Large Language Model Hub: AI Cyberinfrastructure for Learning Engineering Research & Development

  • PI
Funding Agency
  • Schmidt Futures
Project Period
  • 2022 – 2024
Award Amount
  • $450,000

LogicDS: Fostering Virtual Learning of Data Science with Mathematical Logic for Rural High School Students

  • PI
Funding Agency
  • NSF DRK-12
Project Period
  • 2022-2026
Award Amount
  • $1,050,000,

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

Selected Publications

  • Xing, W., Huang, X., Li, C., & Xie, C. (2023). Teaching thermodynamics with augmented interaction and learning analytics. Computers & Education, 196, 104726.
  • Xing, W., Zhu, G., Arslan, O., Shim, J., & Popov, V. (2023). Using learning analytics to explore the multifaceted engagement in collaborative learning. Journal of Computing in Higher Education, 35(3), 633-662.
  • Du, H., Xing, W., & Pei, B. (2023). Automatic text generation using deep learning: providing large-scale support for online learning communities. Interactive Learning Environments, 31(8), 5021-5036.
  • Xing, W., & Du, H. (2023). Mining large open online learning networks: Exploring community dynamics and communities of performance. Journal of Educational Computing Research, 61(2), 390-415.
  • Xing, W., & Wang, X. (2022). Understanding students’ effective use of data in the age of big data in higher education. Behaviour & Information Technology, 41(12), 2560-2577.
  • Li, C., Xing, W., & Leite, W. (2022). Using fair AI to predict students’ math learning outcomes in an online platform. Interactive Learning Environments, 1-20.
  • Li, C., Xing, W., & Leite, W. (2022). Building socially responsible conversational agents using big data to support online learning: A case with Algebra Nation. British Journal of Educational Technology, 53(4), 776-803.
  • Arslan, O., Xing, W., Inan, F. A., & Du, H. (2022). Understanding topic duration in Twitter learning communities using data mining. Journal of Computer Assisted Learning, 38(2), 513-525.
  • Pei, B., & Xing, W. (2022). An interpretable pipeline for identifying at-risk students. Journal of Educational Computing Research, 60(2), 380-405.
  • Li, C., & Xing, W. (2021). Natural language generation using deep learning to support MOOC learners. International Journal of Artificial Intelligence in Education, 31, 186-214.
  • Xing, W., Lee, H. S., & Shibani, A. (2020). Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet Allocation. Educational Technology Research and Development, 68, 2185-2214.
  • Zheng, J., Xing, W., Zhu, G., Chen, G., Zhao, H., & Xie, C. (2020). Profiling self-regulation behaviors in STEM learning of engineering design. Computers & Education, 143, 103669.
  • Li, S., Du, H., Xing, W., Zheng, J., Chen, G., & Xie, C. (2020). Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A network approach. Computers & Education, 158, 103987.
  • 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., 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