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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

Educational Data Science Practicum

This intensive laboratory course will focus on doing data analysis projects with real data selected by the students. The core skills are oriented around first framing good research questions, then having these guide interacting with data of all types and varying quality (e.g., web-scraped, or clickstream-based rather than large national surveys) via visualization, principled modeling and evaluation of models using statistical learning techniques such as regression, classification and clustering, and presentation of results, using "reproducible research" tools (e.g., knitr, sweave) in the R programming language.
Course #
APSTA-GE 2017
Credits
2
Department
Applied Statistics, Social Science, and Humanities

Foundations of Cognitive Science

Introduction to cognitive science applied to teaching, learning, and the design of instructional media. Readings include developments in cognitive science and analysis of instructional programs developed in a cognitive science framework. The design and implementation of cognitive learning and teaching strategies are examined through class demonstrations, discussions, online activities, readings and projects.
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