Bodong Chen

Professional Biography

Bodong Chen is a learning scientist and educational technologist who strives to make learning a meaningful part of social participation for people of all backgrounds and circumstances. His 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, he aims to generate justice-oriented pedagogical designs, technological innovations, and empirical understandings of learning in authentic settings.

Ph.D. (Curriculum & Pedagogy) University of Toronto, 2014
M.Ed. (Educational Technology) Peking University, 2009
B.S. (Educational Technology) Beijing Normal University, 2006

Learning sciences
Learning analytics
Collaborative learning
Educational technology
Network science
Data science education

Teaching, Learning, and Leadership, M.S.Ed.

Teaching, Learning, and Teacher Education, Ed.D.

Teaching, Learning, and Teacher Education, Ph.D.

Learning Analytics and Artificial Intelligence, M.S.Ed.

Learning Sciences and Technologies, M.S.Ed.

Learning Sciences and Technologies, Ph.D.

Research Interests and Current Projects

Dr. Chen’s research draws on multiple theoretical perspectives of learning and is interdisciplinary in nature. One line of his scholarship attempts to empower students’ epistemic agency and collective responsibility by devising pedagogical designs and computer environments for K–12 classrooms. Treating information technology as an enabling factor, his work in this strand attempts to bring student learning into closer alignment with problem-solving by experts and knowledge workers. By designing computer tools and pedagogical interventions, he engages elementary school students in developing higher order competencies such as identifying promising ideas and debugging collaborative discourse; by customizing web technologies, he bridges high school science inquiry in energy and chemistry with public discourse on the Green New Deal; in online college classes, he promotes sophisticated collaboration among students using classroom-orchestration tools. These projects all integrate technology to transform learning into a more generative, collaborative, and personally meaningful experience.

In another line of inquiry, Dr. Chen applies data science methods to rich educational data to derive actionable insights and inform decision making in education practice. His work addresses all steps of the “Learning Analytics Cycle” that includes data generation, gathering, analysis, interpretation, and action taking. On the computational side, Dr. Chen applies a wide range of computational methods, such as network analysis and natural language processing (NLP), to research problems at different granularity levels. For instance, while NLP is used in his projects to examine content of student dialogues, it is also used to measure cultural relevance of math teaching materials recorded in large-scale learning platforms. Moving toward action taking in practice, Dr. Chen is keen to develop learning analytics applications that promote higher order competencies. He has developed student-facing dashboards to support student reflection and self-regulation in online discussions. In this line of inquiry, he infuses ideas from participatory design and value-sensitive design methods to build trust and rigor into the enacted design to make an impact in teaching and learning.

Dr. Chen sits on the editorial boards of a number of journals, including The Internet and Higher Education, and he 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). He served as a program chair of the CSCL Conference of the 2022 ISLS Annual Meeting, and he co-chaired the ISLS Membership Committee and the SoLAR Website Working Group.

Currently funded research includes:

InkSpire: Generative AI for Reading in Science Disciplines (2025–2026), a project funded by the Alfred P. Sloan Foundation to develop and test an AI-enhanced web application that helps college instructors create scaffolded reading assignments to improve student engagement in science fields.
Knowledge Building Innovation Network is a Penn Global Research and Engagement project (2022–2025) that involves members of the international knowledge building community to continuously innovate on infrastructures for learning and knowledge building.

Previously funded research includes:

DataX: Exploring Justice-Oriented Data Science with Secondary School Students is an NSF DRK-12 project (2021–2023) that aims to develop an integrated, justice-oriented curriculum and a digital platform for teaching secondary students.
Learning engineering: Instrumenting a large-scale Student Information System for research (2020–2023) is a research project funded by Schmidt Futures to partner with Infinite Campus, a K–12 EdTech company, to answer research questions about curriculum alignment, socio-emotional learning, and school improvement using large-scale educational data.
CRII: Cyberlearning: Connecting Web Annotations and Progressive Online Discourse in Science Classrooms is an NSF CRII project (2017–2020) that aims to make knowledge building in science classrooms more connected with public discourse and open science.

Chen, B. (in press). A framework for infrastructuring sustainable innovations in education. Journal of the Learning Sciences.

Chen, B., Zhu, X., & Díaz del Castillo H., F. (2023). Integrating generative AI in knowledge building. Computers & Education: Artificial Intelligence, 5, Article 100184. https://doi.org/10.1016/j.caeai.2023.100184

Chen, B., & Chen, Z. (2023). Why do students reply? Uncovering the socio-semantic entanglement in web annotation activities. Education and Information Technologies. Advance online publication. https://doi.org/10.1007/s10639-023-12187-5

Zhu, X., Shui, H., & Chen, B. (2023). Beyond reading together: Facilitating knowledge construction through participation roles and social annotation in college classrooms. The Internet and Higher Education, 59, Article 100919. https://doi.org/10.1016/j.iheduc.2023.100919

Kashi, S., Hod, Y., Lee, A. V. Y., Yuan, G., Cohen, E., Bielaczyc, K., Chen, B., & Zhang, J. (2023). Infrastructuring for knowledge building: Advancing a framework for sustained innovation. Qwerty - Open and Interdisciplinary Journal of Technology, Culture and Education, 18(1), 139–156. https://doi.org/10.30557/QW000068

Chen, B., Shui, H., & Håklev, S. (2022). Orchestrating the flow and advancement of knowledge artifacts in an online class. Instructional Science, 50(6), 903–931. https://doi.org/10.1007/s11251-022-09596-3

Chen, B., & Poquet, O. (2022). Networks in learning analytics: Where theory, methodology, and practice intersect. Journal of Learning Analytics, 9(1), 1–12. https://doi.org/10.18608/jla.2022.7697

Chen, B., & Teasley, S. (2022). Collaboration analytics. In Handbook of learning analytics (2nd Ed.). Society for Learning Analytics Research (SoLAR).

Chen, B., Håklev, S., & Rosé, C.P. (2021). Massive-scale collaborative learning. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning (pp. 163–181). Springer International Publishing. https://doi.org/10.1007/978-3-030-65291-3_9

Chen, B., Fan, Y., Zhang, G., Liu, M., & Wang, Q. (2020). Teachers’ networked professional learning with MOOCs. PLOS ONE, 15(7), e0235170. https://doi.org/10.1371/journal.pone.0235170

Zhu, X., Chen, B., Avadhanam, R. M., Shui, H., & Zhang, R. Z. (2020). Reading and connecting: Using social annotation in online classes. Information and Learning Sciences, 121(5/6), 261–271. https://doi.org/10.1108/ILS-04-2020-0117

Chen, B., & Huang, T. (2019). It is about timing: Explaining prestige in an online social learning environment. Journal of Computer Assisted Learning. https://doi.org/10.1111/jcal.12355

Chen, B. (2019). Designing for networked collaborative discourse: An UnLMS Approach. TechTrends, 63(2), 194–201. https://doi.org/10.1007/s11528-018-0284-7

Chen, B., Chang, Y.-H., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37, 21–30. https://doi.org/10.1016/j.iheduc.2017.12.002

Chen, B., Resendes, M., Chai, C. S., & Hong, H.-Y. (2017). “Two tales of time”: Uncovering the significance of sequential patterns among contribution types in knowledge-building discourse. Interactive Learning Environments, 25(2), 162–175. https://doi.org/10.1080/10494820.2016.1276081

Chen, B. (2016). Fostering scientific understanding and epistemic beliefs through judgments of promisingness. Educational Technology Research and Development, 65(2), 255–277. https://doi.org/10.1007/s11423-016-9467-0

Chen, B., & Hong, H.-Y. (2016). Schools as knowledge-building organizations: Thirty years of design research. Educational Psychologist, 51(2), 266–288. https://doi.org/10.1080/00461520.2016.1175306

Chen, B., Scardamalia, M., & Bereiter, C. (2015). Advancing knowledge building discourse through judgments of promising ideas. International Journal of Computer-Supported Collaborative Learning, 10(4), 345–366. https://doi.org/10.1007/s11412-015-9225-z

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