Xing, Wanli

Category: All Faculty, Assessment & Evaluation, Autism Spectrum Disorders, Behavior, Cognition, Collaboration & Teaming, Computer Science Ed, Data Collection & Analysis, Dropout Prevention, Educational / Instructional Design, Emerging Technologies, Institute for Advanced Learning Technologies, Mathematics Education, Online & Distance Education, Quantitative Research, School of Teaching & Learning, Science Education, Statistics / Applied Stats, Technology Trends & Issues

Assistant Professor
School of Teaching and Learning

College of Education
University of Florida
2-215 Norman Hall
PO Box 117048
Gainesville, FL 32611
Phone: 352-273-3259
wanli.xing@coe.ufl.edu

Research Biography

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.

Degrees

Ph.D. in Information Science and Learning Technologies, 2016
University of Missouri, Columbia

B.Ed. in Educational Technology, 2009
Jilin Normal University

Key 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

Grants

Co-Principal Investigator (2019 – 2022) A Logic Programming Approach to Integrate Computing with Middle School Science Education. National Science Foundation, STEM + Computing Program $388,955

Principal Investigator (sole) (2018 – 2020) Design Artificial Intelligence and Analytics for Deep STEM Learning. National Science Foundation, Discovery Research PreK-12 Program Sub $279,999

Co-Principal Investigator (2017 – 2018) Dashboards with Temporal Scaffolds: Using Educational Data Mining to Increase Temporal Participation in Online Courses, PSU Center for Innovation in Online Learning $40,000

Selected Publications

Journal Articles

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.