Research Bio

Dr. Zachary Pardos is an Associate Professor of Education at UC Berkeley studying adaptive learning and AI. His early scholarship focused on formative assessment using Knowledge Tracing, the predominant model used for estimating cognitive mastery in computer tutoring system contexts. His recent work designing Human-AI collaborations to pave pathways to and within higher education systems has been published in venues such as SIGCHI, NeurIPS, The Internet and Higher Education, and Science. This work has included developing high-quality tools used by tens of thousands of users, including course recommender systems (Equivalency Engine), a Python library for Knowledge Tracing (pyBKT), and an open-source adaptive tutoring system and creative commons content library (OATutor). At Cal, he directs the Computational Approaches to Human Learning (CAHL) research lab, teaches in the data science undergraduate program, and is a faculty affiliate in Cognitive Science. Before arriving at UC Berkeley, he studied as a postdoc at MIT and earned his Ph.D. in Computer Science from Worcester Polytechnic Institute.

Social media: 
https://twitter.com/zpardos
https://www.linkedin.com/in/zacharypardos/

Research Expertise and Interest

knowledge representation, adaptive learning, artificial intelligence, learning analytics, Recommender Systems, higher education, data science, Digital Learning Environments, cognitive modeling, Big Data in Education, Knowledge Tracing, Formative Assessment, intelligent tutoring systems, Online Learning, psychometrics, Educational Data Mining