Zhang, F., Li, C., Henkel, O., Xing, W. et al. (2024). Math-LLMs: AI Cyberinfrastructure with Pre-trained Transformers for Math Education. International Journal of Artificial Intelligence in Education, 1-24. https://doi.org/10.1007/s40593-024-00416-y [IF: 4.7]
Xing, W., Pei, B., Zhu, W., Li, H., & Guo, R. (2024). The dynamics of role evolution in online learning communities. Distance Education, 1–25. https://doi.org/10.1080/01587919.2024.2401085 [IF: 3.7, SSCI]
Song, Y., Xing, W., Li, C., Tian, X., & Ma, Y. (2024). The relationship between Math Literacy and Linguistic Synchrony in Online Mathematical Discussions. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13444 [IF: 6.7, SSCI]
Xing, W., Li, H., Kim, T. g, & Li, C. (2024). Investigating the behaviors of core and periphery students in an asynchronous online discussion community using network analysis and topic modeling. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13038-7 [IF: 4.8, SSCI]
Lyu, B., Li, C., Li, H., Zhu, W., & Xing, W. (2024). Explaining technical, social, and discursive participation in online mathematical discussions. Distance Education, 1–24. https://doi.org/10.1080/01587919.2024.2399151 [IF: 3.7, SSCI]
Zhu, W., Xing, W., Kim, E., Li, C., Wang, Y., Lee, J., & Liu, Z. (Accepted). Integrating Image-Generative AI into Conceptual Design in Computer-Aided Design Education: Exploring Student Perceptions, Prompt Behaviors, and Artifact Creativity. Educational Technology & Society. 1-24. [IF: 4.6, SSCI]
Wusylko, C., Weisberg, L., Opoku, R. A. g, Abramowitz, B. g, Williams, J., Xing, W., ... & Vu, M. (2024). Using machine learning techniques to investigate learner engagement with TikTok media literacy campaigns. Journal of Research on Technology in Education, 56(1), 72-93. https://doi.org/10.1080/15391523.2023.2266518 [IF: 5.1, SSCI]
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. https://doi.org/10.1007/s12528-022-09343-0 [IF: 5.6, SSCI]
Schmidt, M., Glaser, N., Palmer, H., Schmidt, C., & Xing, W. (2023). Through the lens of artificial intelligence: A novel study of spherical video-based virtual reality usage in autism and neurotypical participants. Computers & Education: X Reality, 3, 100041.
Li, Y., Du, H., Martin, I., Hidalgo, E., Jiang, Y., Xing, W., & Popov, V. (2023). Exploring adolescents' occupational possible selves: The role of gender and socioeconomic status. The Career Development Quarterly, 71(3), 189-205. [IF: 2.1, SSCI]
Xing, W., Huang, X., Li, C., & Xie, C. (2023). Teaching thermodynamics with augmented interaction and learning analytics. Computers & Education, 196, 104726. https://doi.org/10.1016/j.compedu.2023.104726. [IF: 12, SSCI]
Du, H., Xing, W., & Zhu, G. (2023). Mining teacher informal online learning networks: Insights from massive educational chat tweets. Journal of Educational Computing Research, 61(1), 127-150. [IF: 4.8, SSCI]
Xing, W., & Du, H. (2023). Mining Large Open Online Learning Networks: Exploring Community Dynamics and Communities of Performance. Journal of Educational Computing Research. [IF: 4.345, SSCI]
Du, H., Xing, W., & Pei, B. (2023). Leveraging explainability for discussion forum classification: Using confusion detection as an example. Distance Education, 1-16. [IF: 7.3, SSCI]
Pei, B., Xing, W., Zhu, G., Zlatkovic, K., & Xie, C. (2023). Integrating infrared technologies in science learning: An evidence-based reasoning perspective. Education and Information Technologies. [IF: 5.5, SSCI]
Zhu, G., Xing, W., Popov, V., Li, Y., Xie, C., & Horwitz, P. (2022). Using Learning Analytics to Understand Students’ Discourse and Behaviors in STEM Education. Artificial Intelligence in STEM Education: The Paradigmatic Shifts in Research, Education, and Technology
Li, C., Xing, W., & Leite, W. (2022). Using fair AI to predict students’ math learning outcomes in an online platform. Interactive Learning Environments. [IF: 5.4, SSCI]
Leite, W., Xing, W., Fish, G., & Li, C. (2022). Teacher strategies to use virtual learning environments to facilitate algebra learning during school closures. Journal of Research on Technology in Education. [IF: 5.1, SSCI]
Xing, W. (2022). Does the early bird catch the worm? A large-scale examination of the effects of early participation in online learning. Distance Education. [IF: 2.952, SSCI]
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. [IF: 4.929, SSCI]
Li, C., & Xing, W., Leite, W. (2022). Towards Building a Fair Peer Recommender to Support Help-Seeking in Online Learning. Distance Education. [IF: 2.952, SSCI]
Liu, B.g, Xing, W., Zeng, Y., & Wu, Y. (2022). Linking cognitive processes and learning outcomes: The influence of cognitive presence on learning performance in MOOCs. British Journal of Educational Technology. [IF: 4.929, SSCI]
Tang, H., Arslan, O., Xing, W., & Kamali‑Arslantas, T. (2022). Exploring collaborative problem solving in virtual laboratories: a perspective of socially shared metacognition. Journal of Computing in Higher Education. [IF: 2.627, SSCI]
Xing, W., Du, D., Bakhshi, A., Chiu, K. C., & Du, H. (2021). Designing a Transferable Predictive Model for Online Learning Using a Bayesian Updating Approach. IEEE Transactions on Learning Technologies, 14(4), 474-485. [IF: 3.720, SCI & SSCI]
Arslan, O., Xing, W., Inan, F. A., & Du, H. (2021). Understanding topic duration in Twitter learning communities using data mining. Journal of Computer Assisted Learning. 1-13. [IF: 3.862, SSCI]
Xing, W., Li, C., Chen, G., Huang, X., Chao, J., Massicotte, J., & Xie, C. (2021). Automatic Assessment of Students’ Engineering Design Performance Using a Bayesian Network Model. Journal of Educational Computing Research, 59(2), 230-256. [IF: 3.088, SSCI]
Li, C., & Xing, W. (2021). Natural Language Generation Using Deep Learning to Support MOOC Learners. International Journal of Artificial Intelligence in Education, 1-29.
Xing, W., & Wang, X. (2021). Understanding students’ effective use of data in the age of big data in higher education. Behaviour & Information Technology, 1-18. [IF: 3.086, SCI & SSCI]
Du, H., Xing, W., & Pei, B. (2021). Automatic text generation using deep learning: providing large-scale support for online learning communities. Interactive Learning Environments, 1-16. [IF: 3.928, SSCI]
Pei, B., & Xing, W. (2021). An Interpretable Pipeline for Identifying At-Risk Students. Journal of Educational Computing Research, 07356331211038168. [IF: 3.088, SSCI]
Zhu, G., Zeng, Y., Xing, W., Du, H., & Xie, C. (2021). Reciprocal Relations between Students’ Evaluation, Reformulation Behaviors, and Engineering Design Performance Over Time. Journal of Science Education and Technology, 1-13. [IF: 2.315, SSCI]
Tang, H., & Xing, W. (2021). Massive open online courses for professional certificate programs? Perspectives on professional learners’ longitudinal participation patterns. Australasian Journal of Educational Technology, 136-147. [IF: 3.067, SSCI]
Pei, B.g, Xing, W., & Wang, M. (2021). Academic development of multimodal learning analytics: a bibliometric analysis. Interactive Learning Environments, 1-19. [IF: 3.928, SSCI]
Liu, B.g, Xing, W., Zeng, Y., & Wu, Y. (2021). Quantifying the Influence of Achievement Emotions for Student Learning in MOOCs. Journal of Educational Computing Research, 59(3), 429-452. [IF: 3.088, SSCI]
Wang, X., & Xing, W. (2021). Supporting Youth with Autism Learning Social Competence: A Comparison of Game-and Nongame-Based Activities in 3D Virtual World. Journal of Educational Computing Research, 07356331211022003. [IF: 3.088, SSCI]
Liu, B.g, Wu, Y., Xing, W., Cheng, G., & Guo, S. (2021). Exploring behavioural differences between certificate achievers and explorers in MOOCs. Asia Pacific Journal of Education, 1-13. [IF: 1.057, SSCI]
Zhu, G., Raman, P., Xing, W., & Slotta, J. (2021). Curriculum design for social, cognitive and emotional engagement in Knowledge Building. International Journal of Educational Technology in Higher Education, 18(1), 1-19. [IF: 4.944, SSCI]
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 & Development, 68(5). [IF: 3.565, SSCI]
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. [IF: 8.538, SSCI]
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. [IF: 8.538, SSCI]
Zheng, J., Xing, W., Huang, X., Li, S., Chen, G., & Xie, C. (2020). The role of self-regulated learning on science and design knowledge gains in engineering projects. Interactive Learning Environments, 1-13. [IF: 3.928, SSCI]
Li, S., Chen, G., Xing, W., Zheng, J., & Xie, C. (2020). Longitudinal clustering of students’ self-regulated learning behaviors in engineering design. Computers & Education, 153, 103899. [IF: 8.538, SSCI]
Xing, W., Pei, B., Li, S., Chen, G., & Xie, C. (2019). Using learning analytics to support students’ engineering design: the angle of prediction. Interactive Learning Environments, 1-18. [IF: 1.938, SSCI]
Xing, W., Tang, H., & Pei, B. (2019). Beyond positive and negative emotions: Looking into the role of achievement emotions in discussion forums of MOOCs. Internet and Higher Education, 43, 100690. [IF: 6.566, SSCI]
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, 50(6), 3391-3404. [IF: 2.951, SSCI]
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. [IF: 5.003, SSCI]
Xing, W., & Du, D. P. (2019). Dropout prediction in MOOCs: Using deep learning for personalized intervention. Journal of Educational Computing Research, 57(3), 755-776. [IF: 2.180, SSCI]
Xing, W. (2019). Large-scale path modeling of remixing to computational thinking. Interactive Learning Environments, 1-14. [IF: 1.938, SSCI]
Xing, W. (2019). Exploring the influences of MOOC design features on student performance and persistence. Distance Education, 40(1), 98-113. [IF: 1.702, SSCI]
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 (UMUAI), 29(4), 789-820. [IF: 4.682, SCI]
Tang, H., Xing, W., & Pei, B. (2019). Time really matters: Understanding the temporal dimension of online learning using educational data mining. Journal of Educational Computing Research, 57(5), 1326-1347. [IF: 2.180, SSCI]
Liu, B., Xing, W., Wu, Y., Li, R., Tian, Y., & Ma, X. (2019). Students’ Interaction and Perceptions in a Large Enrolled Blended Seminar Series Course. The Turkish Online Journal of Educational Technology. 18(03), 88-96 [SSCI]
Zheng, J., Xing, W., & Zhu, G. (2019). Examining sequential patterns of self-and socially shared regulation of STEM learning in a CSCL environment. Computers & Education, 136, 34 – 48. [IF: 5.296, SSCI]
Wang, X., & Xing, W. (2019). Understanding elementary students’ use of digital textbooks on mobile devices: A structural equation modeling approach. Journal of Educational Computing Research, 57(3), 755-776. [IF: 2.180, SSCI]
Zhu, G., Xing, W., & Popov, V. (2019). Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning. Internet and Higher Education, 41, 51-61. [IF: 6.566, SSCI]
Xing, W., & Gao, F. (2018). Exploring the relationship between online discourse and commitment in Twitter professional learning communities. Computers & Education, 126, 388-398. [IF: 5.627, SSCI]
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. [IF: 4.306, SSCI]
Tang, H., Xing, W., & Pei, B. (2018). Exploring the temporal dimension of forum participation in MOOCs. Distance Education, 39(3), 353-372. [IF: 1.729, SSCI]
Wang, X., & Xing, W. (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. [IF: 2.588, SSCI]
Tawfik, A., Law, V., Xun, G. Xing, W., & Kyung K. (2018). The effect of sustained vs. faded scaffolding on students’ argumentation in ill-structured problem solving. Computers in Human Behavior, 87, 436-449. [IF: 4.306, SSCI]
Wang, X., & Xing, W. (2018). Exploring the influence of parental involvement and socioeconomic status on teen digital citizenship: a path modeling approach. Educational Technology & Society. 21(1), 186-199. [IF: 2.133, SSCI]
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 & Development 65(4), 1015-1039. [IF: 2.115, SSCI]
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. [IF: 3.435, SSCI]
Goggins, S., & Xing, W. (2016). Building models explaining student participation behavior in asynchronous online discussion. Computers & Education, 94, 241-251. [IF: 3.819, SSCI]
Wang, X., Laffey, J., Xing, W., & Ma, Y. (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. [IF: 3.435, SSCI]
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. [IF: 3.435, SSCI]
Xing, W., Wadholm, B., & Goggins, S. (2015) Group learning assessment: Developing theory-informed analytics. Educational Technology & Society. 18(2), 110-128. [IF: 1.34, SSCI]
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. [IF: 1.066, SCI]
Liu, Z., Guo, R., & Xing, W. (2024, Sep). Understanding students' in-video dropout behavior in large online math learning platforms (Work in Progress). Abstract accepted for presentation at the 2024 IEEE Frontiers in Education Conference (FIE).
Zhang, F., Guo, R., Xing, W., & Zhu, W. (2024, Sep). From tweets to trends: Tracing the public's perception of AI in education post-ChatGPT. Abstract accepted for presentation at the 2024 IEEE Frontiers in Education Conference (FIE).
Li, H., Xing, W., Li, C., Zhu, W., & Heffernan, N. (2024, July). Positive affective feedback mechanisms in an online mathematics learning platform. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24) (pp. 371–375). https://doi.org/10.1145/3657604.3664666
Lyu, B., Li, C., Li, H., & Xing, W. (2024, July). Roles of joining time, technology use, and social interaction in sustaining student participation in an online mathematics discussion board. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24) (pp. 398–402). https://doi.org/10.1145/3657604.3664672
Li, H., Guo, R., Li, C., & Xing, W. (2024, July). Automated quality assessment of multimodal mathematical stories generated by generative artificial intelligence. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24) (pp. 110–121). https://doi.org/10.1145/3657604.3662029
Lyu, B., Li, C., Li, H., & Xing, W. (2024, July). Interplay among students' technical, social, and content-related participation patterns in an online mathematical discussion board. In Proceedings of the Eleventh ACM Conference on Learning @ Scale (L@S '24) (pp. 466–470). https://doi.org/10.1145/3657604.3664696
Liu, Z., Jiao, X., Li, C., & Xing, W. (2024, July). Fair Prediction of Students' Summative Performance Changes Using Online Learning Behavior Data. In Proceedings of the 17th International Conference on Educational Data Mining, 686-691. https://doi.org/10.5281/zenodo.12729918
Liu, Z., Xing, W., & Li, C. (2024, July). Explainable analysis of AI-generated responses in online learning discussions. In Educational Data Mining 2024 Workshop: Leveraging Large Language Models for Next-Generation Educational Technologies. https://doi.org/10.13140/RG.2.2.24309.38881
Zhu, W., Guo, R., Zhang, L., Xing, W., & Kim, E. (2024, June). Does GAI Enhance Students' Creativity? Integrating Image-generative AI into Conceptual Design in a CAD Class. In Proceedings of the American Society for Engineering Education (ASEE 2024) Annual Conference and Exposition. Portland, Oregon, United States.
Oh, H., Guo, R., Xing, W., Li, C., Song, Y., & Liu, Z. (2024, June). Work-in-Progress: The Seamless Integration of Machine Learning into High School Mathematics Classrooms. In Proceedings of the American Society for Engineering Education (ASEE) Annual Conference and Exposition. Portland, Oregon, United States.
Liu, Z., Guo, R., Jiao, X., Gao, X., & Xing, W. (2024, June). How AI assisted K-12 computer science education? A systematic review. In Proceedings of the 2024 American Society for Engineering Education Annual Conference & Exposition (ASEE), Portland, Oregon, June 23-26.
Kim, G., Feng, P., & Xing, W. (2024, June). Work-in-Progress: The Seamless Integration of Machine Learning into High School Mathematics Classrooms. In Proceedings of the American Society for Engineering Education (ASEE) Annual Conference and Exposition. Portland, Oregon, United States.
Zhang, Y., Du, H., and Xing, W. (2024). A New Approach to High School Data Science: Set Theory and Logic. In Proceedings of the 18th International Conference of the Learning Sciences-ICLS 2024, pp. 2101-2102. Buffalo, New York, United States.
Zhu, W., Li, C., Zhu, G., Li, Y., Xing, W., & Shao, K. (2024). ISEA: Instructor-in-the-loop Student Engagement Analytics via FAccT AI Clustering. In Proceedings of the International Conference of the Learning Sciences-ICLS 2024, pp. 1598-1601. Buffalo, New York, United States.
Song, Y., Li, C., Xing, W., Li, S., & Lee, H. H. (2024, March). A Fair Clustering Approach to Self-Regulated Learning Behaviors in a Virtual Learning Environment. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 771-778). Kyoto, Japan.
Xing, W., & Li, C. (2024). Fair Artificial Intelligence to Support STEM Education: A Hitchhiker’s Guide. In X. Zhai, & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education. Oxford University Press.
Li, C., Zhu, W., Xing, W. & Guo, R. (2024). Analyzing Student Attention and Acceptance of Conversational AI for Math Learning: Insights from a Randomized Controlled Trial. In LAK’24: Proceedings of the 14th Learning Analytics and Knowledge Conference, 836-842.
Li, H., Li, C., Xing, W., Baral, S., & Heffernan, N. (2024, March). Automated Feedback for Student Math Responses Based on Multi-Modality and Fine-Tuning. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 763-770).
Zhang, F. g, Xing, W., & Li, C. g (2023, March). Predicting Students’ Algebra I Performance using Reinforcement Learning with Multi-Group Fairness. In LAK23: 13th International Learning Analytics and Knowledge Conference (pp. 657-662). Dallas, Texas.
Du, H., Xing, W., Pei, B., Zeng, Y., Lu, J., & Zhang, Y. (2023, August). A Descriptive and Historical Review of STEM + C Research: A Bibliometric Study. In book: Computer Supported Education
Aleven, V., Baraniuk, R., Brunskill, E., Crossley, S., Demszky, D., Fancsali, S., ... & Xing, W. (2023, June). Towards the Future of AI-Augmented Human Tutoring in Math Learning. In International Conference on Artificial Intelligence in Education (pp. 26-31). Cham: Springer Nature Switzerland.
Song, Y. g, Xing, W., Barron A., Oh, H. g, Li, C. g, & Minces, V. (2023). M-flow, a Flow-based Music Creation Platform Improves Underrepresented Children's Attitudes toward Computer Programming. In Proceedings of Interaction Design and Children (IDC ’23), June 19–23, 2023, Chicago, IL, USA. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3585088.3589383 [Best Paper Award]
Song, Y. g, Xing, W., Tian, X., & Li, C. g (2023). Are We on the Same Page? Modeling Linguistic Synchrony and Math Literacy in Mathematical Discussions. In LAK23: 13th International Learning Analytics and Knowledge Conference (pp. 599-605). Dallas, Texas. [Best Paper Award]
Zhu, G., Xing, W., Popov, V., Li, Y., & Xie, C. (2022, December). Using Learning Analytics to Understand Students' Discourse and Behaviors in STEM Education. In book: Artificial Intelligence in STEM Education.
Du, H., Xing, W., & Zhang, Y. (2022, August). Misconception of Abstraction: When to Use an Example and When to Use a Variable? In ICER 2022: ACM Conference on International Computing Education Research. Lugano, Switzerland
Hansen, Z., Du, H., Xing, W., Eckel, R., Lugo, J., & Zhang, Y. (2022, August). A Preliminary Data-driven Analysis of Common Errors Encountered by Novice SPARC Programmers. In 38th International Conference on Logic Programming. Haifa, Israel
Li, C., & Xing, W., (2022, June). Revealing Factors Influencing Students' Perceived Fairness: A Case with a Predictive System for Math Learning. In Learning@Scale: 2022 ACM Conference on Learning at Scale. New York City, New York
Li, C., Xing, W., & Leite, W. (2022, March). Do Gender and Race Matter? Supporting Help-Seeking with Fair Peer Recommenders in an Online Algebra Learning Platform. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 432-437). Virtual. [Acceptance rate: 29.5%]
Du, H. Xing, W., & Zhang, Y. (2021). A Debugging Learning Trajectory for Text-Based Programming Learners.In Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 2.
Li, C. Xing, W., & Leite, W. (2021). Using Fair AI with Debiased Network Embeddings to Support Help Seeking in an Online Math Learning Platform.International Conference on Artificial Intelligence in Education.
Li, C. Xing, W., & Leite, W. (2021). Yet Another Predictive Model? Fair Predictions of Students’ Learning Outcomes in an Online Math Learning Platform.LAK21: 11th International Learning Analytics and Knowledge Conference.
Nguyen, V. T., Zhang, Y., Jung, K., Xing, W., & Dang, T. (2020, January). VRASP: A Virtual Reality Environment for Learning Answer Set Programming. In International Symposium on Practical Aspects of Declarative Languages (pp. 82-91). Springer, Cham.
Huang, X., Xing, W., Zhao, H., Chao, J., Schimpf, C., Chen, G. & Xie, C. (2020, June). Understanding science learning through writings on engineering design. In Proceedings of the 2020 International Conference of the Learning Sciences (ICLS, 2020), (Vol. 1, pp. 1771-1773), Nashville, TN: International Society of the Learning Sciences.
Du, H., Nguyen, L., Yang, Z., Abu-Gellban, H., Zhou, X., Xing, W., ... & Jin, F. (2019, July). Twitter vs News: Concern Analysis of the 2018 California Wildfire Event. In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 207-212). IEEE.
Zheng, J., Xing, W., Zhu, G., Chen, G., Zhao, H. g, & Huang, X. (2019). Person-oriented approach to profiling learnings’ self-regulation in STEM learning. In Proceedings of the 9th International Conference on Learning Analytics and Knowledge - LAK ’19 (pp. 245-246). Tempe, Arizona.
Tang, H. & Xing, W. (2019). Achievement emotions and attritions in Massive Open Online Courses: Using machine learning models, In Proceedings of the 9th International Conference on Learning Analytics and Knowledge - LAK ’19 (368-373). Tempe, Arizona.
Pei, B., Zhao, H., Xing, W., & Lee, H. S. (2019). The Exploration of Automated Image Processing Techniques in the Study of Scientific Argumentation. In Cognitive Computing in Technology-Enhanced Learning (pp. 175-190). IGI Global.
Popov, V., Xing, W., Zhu, G., Horwitz, P., & McIntyre, C. (2018, June). The influence of students’ transformative and non-transformative contributions on their problem solving in collaborative inquiry learning. In J, Key, & R. Luckin (Eds.), rethinking Learning in the Digital Age. Making the Learning Sciences Count: In Proceedings of The International Conference of the Learning Sciences (ICLS, 2018), (Vol. 3, pp. 855-862). London, UK: International Society of the Learning Sciences. [Acceptance rate: 32%; ~5000 words]
Arslan, O.g, Xing, W., Horwitz, P, & McIntyre, C. (2018). Examining the influence of socially shared metacognition on group problem solving. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge - LAK ’18 (pp. 1-3). Sydney, Australia.
Aragon, C., Huang, Y., Neff, G., & Xing, W.,. Developing a research agenda for Human-centered data scienc. In Proceedings of the 2016 Computer Supported Cooperative Work and Social Computing (CSCW). San Francisco, CA: ACM.
Xing, W., & Goggins, S. (2015). Student assessment in small groups: A spectral clustering model. In iConference 2015 Proceedings (pp. 1-5). Newport Beach, CA: IDEALS.
Xing, W., Wadholm, B., & Goggins, S. (2014). Assessment analytics in CSCL: Activity theory based method. Learning and Becoming in Practice: Proceedings of the 11th International Conference of the Learning Sciences (ICLS 2014), (Vol. 3, pp. 1535-1536). Boulder, CO: International Society of the Learning Sciences.
Xing, W., Goggins, S. (2014). Automated CSCL group assessment: activity theory based computational method. In LAK Workshops: Computational Approaches to Connecting Levels of Analysis in Networked Learning Communities.
Ma, Y., Friel, C., & Xing, W.,(2014).Instructional activities in a discussion board forum of an e-leaning management system. In HCI International 2014-Posters' Extended Abstracts (pp. 112-116). Crete, Greece: Springer International Publishing.
Ma, Y.,Xing, W.,& Friel, C. (2013). Factors and cues impacting user information selection and processing performance in kiosk touch screen interfaces. In HCI International 2013-Posters' Extended Abstracts (pp. 56-60). Las Vegas, Nevada: Springer Berlin Heidelberg.
Guo, Y., Xing, W., & H., Lee. Identifying students’ mechanistic explanations in textual responses to science questions with association rule mining. In Proceedings of the 2015 International Conference on Data Mining (ICDM). Atlantic City, NJ: IEEE.
Xing, W., Kim, S. & Goggins, S. (2015). Modeling performance in asynchronous CSCL: An exploration of social ability, collective efficacy and social interaction. Exploring the Material Conditions of Learning: Proceedings of The Computer Supported Collaborative Learning (CSCL 2015), (Vol, pp. 276-283). Gothenburg, Sweden: International Society of the Learning Sciences. [Acceptance rate: 36%; ~5500 words]
Xing, W., & Goggins, S. (2015). Learning analytics in outer space:a Hidden Naive 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. [Acceptance rate: 27%; ~7000 words]
B. Chen, X. Chen,& Xing, W.,(2015), "Twitter Archeology" of learning analytics and knowledge conferences In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge - LAK'15 (pp. 340-349). New York, NY, USA: ACM. [Acceptance rate: 27%; ~8000 words]
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.[Acceptance rate: 30%; ~7000 words]
Xing, W., & Wu, Y. (2014). Assessment intelligence in small group learning.IADIS international conference on cognition and exploratory learning in the digital age (CELDA 2014) (pp. 47-54). Porto, Portugal: IADIS. [Acceptance rate: 30%; ~5000 words]
Xing, W., Guo, R., Richardson, B., & Kochtanek, T. (2014). Google Analytics spatial data visualization: Thinking outside of the box. In S. Yamamoto (Ed.), Human Interface and the Management of Information: Information and Knowledge Design and Evaluation (pp. 120-127). New York, NY: Springer. ISBN 978-3-319-07730-7.
Xing, W., Guo, R., Lowrance, N., & Kochtanek, T. (2014). Decision support based on time-series analytics: A cluster methodology. In Human Interface and the Management of Information: Information and Knowledge in Applications and Services (pp. 217-225). New York, NY: Springer. ISBN 978-3-319-07862-5.