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, 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 sit on the editorial boards of a number of journals, including The Internet and Higher Education, and 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). I also served as a program chair of the CSCL Conference of the 2022 ISLS Annual Meeting, and co-chaired the ISLS Membership Committee and the SoLAR Website Working Group.
Before joining Penn GSE, I was an associate professor in the College of Education and Human Development and co-director of the Learning Informatics Lab at the University of Minnesota.
Random Thoughts
The Many Faces of Artificial Intelligence (AI)
(Photo Credit: ChatGPT 4o) AI is often the elephant in the room, surfacing in everything from high-stakes policymaking and academic research to casual conversations with my barber. It permeates so many aspects of life that it’s hard to escape its presence. Yet, because AI is so vast and varied in its applications, it remains a “hyper-object”—something so complex and multi-dimensional that it feels impossible to fully grasp. It impacts education, healthcare, law enforcement, entertainment, and more, each in dramatically different ways.
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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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