Community Analytics

PROJECT DATES: 2019 – present

To systematically investigate topic development and collaboration dynamics over time in educational research communities, this project proposes a three-level analytical framework utilizing natural language processing and social network analysis on time-evolving co-authorship networks. Based on bibliometric data of all papers published in major conferences in a subfield in a certain time frame, we conduct the macro-level analysis displaying the structural and topical comparisons of the research communities, meso-level analysis of the evolution of topics among cliques within each community, and micro-level analysis of core authors’ demographics within in each community.  

Our first analysis evaluated all conference papers published from 2013 to 2020 in four major conferences related to Artificial Intelligence in Education: the International Conference on Artificial Intelligence in Education (AIED), the International Conference on Educational Data Mining (EDM), the International Conference on Learning Analytics and Knowledge (LAK), and the ACM Conference on Learning at Scale (L@S). Our analysis provided insights on recent research foci and the level of openness of research communities within AIEd and formulated strategies on how to promote diverse ideas and further collaborations in the field [pdf].

Our second analysis examined all conference papers published in the proceedings of the International Conference of Learning Sciences (ICLS) and the International Conference on Computer-Supported Collaborative Learning (CSCL) from 1995 to 2020 to explore the changes of research foci in the two research communities over the years. We found three distinctive spreading patterns of keywords in the two communities: outside-in ascending pattern, inside-out ascending pattern, and outside-out descending pattern. Our analysis also revealed how different co-authorship cliques and core authors have influenced such patterns and pointed to possible actions for both research communities (The paper is submitted to ISLS2023 and is under review).

Zheng, Y., Zhou, Z., & Blikstein, P. (2022). Towards an Inclusive and Socially Committed Community in Artificial Intelligence in Education: A Social Network Analysis of the Evolution of Authorship and Research Topics over 8 Years and 2509 Papers. In International Conference on Artificial Intelligence in Education (pp. 414-426). Springer. 

Zheng, Y., Chen, J., Zhou, Z., Friedman, Z., & Blikstein, P. (2021). Peeking into the AI Hype: Investigating Research Trends and Collaboration Dynamics in Artificial Intelligence in Education. In Proceedings of the 15th International Conference of the Learning Sciences-ICLS 2021. International Society of the Learning Sciences.


Paulo Blikstein

Yipu Zheng

Karen Zhuqian Zhou

Jie Chen


Zach Friedman


For more information, please contact Yipu Zheng (