This is Bodong Chen.
I am an associate professor in Learning Sciences and Technologies at the University of Pennsylvania Graduate School of Education. My research is at the intersection of the learning sciences, AI, learning analytics, and network science. As a learning scientist and educational technologist, I strive to make learning a meaningful part of social participation for people of all backgrounds and circumstances. My scholarly inquiry integrates knowledge media design, software engineering, and data science methods to continually improve infrastructures for learning. Guided by design-based research and participatory design approaches, I aim to generate justice-oriented pedagogical designs, technological innovations, and empirical understandings of learning in authentic settings.
I am currently an Associate Editor of ijCSCL and sit on the editorial boards of a number of journals, including Journal of the Learning Sciences and Journal of Learning Analytics. I was elected to the Executive Committee of the Society for Learning Analytics Research (SoLAR) and the Computer-Supported Collaborative Learning (CSCL) Committee of the International Society of the Learning Sciences (ISLS), and co-chaired of the CSCL Conference in 2022 and 2025.
Before joining Penn GSE, I was an associate professor in the College of Education and Human Development and founding co-director of the Learning Informatics Lab at the University of Minnesota.
Random Thoughts
Preserving Human Idiosyncrasy: Generative Learning in the Age of Generative AI
Publications
A framework for infrastructuring sustainable innovations in education
Abstract Learning scientists have historically been interested in understanding how learning happens and in creating innovations to facilitate learning in real-world situations. Recently, the field has recognized that advancing standalone innovations is not enough to address systemic problems in education; instead, the focus must be broadened to sustain these innovations. Drawing on an interdisciplinary body of literature on infrastructure, this paper presents a framework—the IMPROV framework—that offers theoretical, methodological, and practical tools for infrastructuring innovations in the learning sciences.
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