Wanli Xing, Ph.D.
Wanli Xing is an assistant professor in the Instructional Technology program at Texas Tech University. His research interests are learning analytics and learning sciences especially using data to enhance learning, assessment, and collaboration. The key idea behind his work is to draw insights from rich learning theory models and pair them with computational models that automatically capture the essence of what is happening in a learning situation for achieving impact on learning. He is pursuing this research program in multiple parallel contexts including computer supported collaborative learning (CSCL), game-based learning, social media, and massive open online courses (MOOCs). His research findings have appeared in Computers and Education, Educational Technology Research and Development, Computers in Human Behavior, ACM Learning Analytics, and Knowledge, and other decent journals.
- Bachelor of Education in Educational Technology: Jilin Normal University, Jilin, China
- Doctorate in Information Science and Learning Technologies: University of Missouri-Columbia
Areas of Expertise
- Learning Analytics
- Educational Data Mining
- Computer-supported Collaborative Learning
- Social Media
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, 1-25.
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.
Wang, X., Laffey, J., Xing, W., Ma, Y., & Stichter, J. (2016). Exploring embodied social presence of youth with Autism in 3D collaborative virtual learning environment: A case study. Computers in Human Behavior, 55, 310-321.
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. P. (2015). Group learning assessment: Developing a theory-informed analytics. Educational Technology & Society, 18(2), 110-128.
Goggins, S., Xing, W., Chen, X., Chen, B., & Wadholm, B. (2015). Learning analytics at “small” scale: Exploring a complexity-grounded model for assessment automation. Journal of Universal Computer Science. 21(1), 66-92.
Xing, W., & Goggins, S. (2015). Learning analytics in outer space: a Hidden Naïve Bayes model for automatic student off-task behavior detection. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK ’15 (pp. 176-183). New York, NY, USA: ACM. doi:10.1145/2723576.2723602
Xing, W., Wadholm, B., & Goggins, S. (2014). Learning analytics in CSCL with a focus on assessment: An exploratory study of activity theory-informed cluster analysis. In Proceedings of the Fourth International Conference on Learning Analytics And Knowledge- LAK ’14 (pp. 59-67). ACM. New York, NY, USA: ACM. doi: 10.1145/2567574.2567587