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Yoav Bergner

Associate Professor of Learning Sciences/Educational Technology

Administration, Leadership, and Technology

212-998-5453

Yoav Bergner is an Associate Professor of Learning Sciences and Educational Technology. He earned an A.B. in physics from Harvard University and a Ph.D. in theoretical physics from the Massachusetts Institute of Technology.

Yoav's research bridges psychometrics and learning sciences in pursuit of analysis methods that inform educational design. He is particularly interested in the assessment of learning in Makerspaces and Fablabs, in data literacy curriculum design (with dancers!), and in collaborative problem solving. He co-developed CPSX, an extension for real-time small-group discussion using the Open edX learning management system. His work has been presented at meetings of the International Educational Data Mining Society (EDM), Learning Analytics and Knowledge (LAK), and the International Meeting of the Psychometric Society (IMPS).

Prior to joining NYU, Yoav was a Research Scientist at Educational Testing Service. His post-doctoral path involved five years as a sculptor and a furniture-maker, followed by three years as a teacher in a public early college high school, where he taught science, mathematics, industrial arts, and philosophy of science. Interest in educational data and measurement brought him back to MIT as a research associate in 2011 to work on student modeling in online learning environments, including massive open online courses (MOOCs). From 2013-2016, Yoav was a MacArthur/ETS Edmund W. Gordon Fellow for 21st Century Learning and Assessment.

Courses

Databases and Data Science Practicum

This course provides a hands-on introduction to extracting, transforming, and visualizing data using real-world datasets. Students learn to query databases and join datasets using SQL, and learn to summarize, visualize, and map data in R using the tidyverse. Students also gain experience with git, enhancing their ability to work in modern collaborative environments. Alongside these modules, an ongoing emphasis of the course is to practice how to be a curious, skeptical, and articulate data scientist.
Course #
APSTA-GE 2017
Credits
2
Department
Applied Statistics, Social Science, and Humanities

How Humans Learn I

This course offers an in-depth journey into the mental processes that drive knowledge acquisition and understanding, examining how our minds encode, store, and retrieve information over time. Grounded firmly in cognitive science, the course emphasizes not only the theoretical underpinnings of human thought—such as memory structures, representation systems, and developmental trajectories—but also the practical ways in which these insights inform the creation of instructional media.
Course #
EDCT-GE 2174
Credits
3
Department
Administration, Leadership, and Technology

Statistical Analysis of Networks

This course is an introduction to the analysis and modeling of network data. Network analysis is a key tool in understanding relational data - data describing the relationships between pairs and groups of individuals, as well as the global structure of relationships. We will focus on applications to and building tools for research in the social sciences, but the methodology can be extended to other areas. By the end of the course, you should have a working knowledge of basic network analysis tools and be able to use them to analyze your own data.
Course #
APSTA-GE 2014
Credits
3
Department
Applied Statistics, Social Science, and Humanities