Zachary Pardos

Assistant Professor (I School and Graduate School of Education)

Focus

Learning Analytics, Digital Learning Environments, Machine Learning

Research areas

Biography

Dr. Pardos is an Assistant Professor at UC Berkeley in a joint position between the Graduate School of Education and School of Information. His focal areas of study are educational data mining and learning analytics concentrating on measurement of learning phenomena in digital environments. He earned his PhD in Computer Science at Worcester Polytechnic Institute in the Tutor Research Group in 2012. Funded by a National Science Foundation Fellowship (GK-12) he spent extensive time on the front lines of K-12 education working with teachers and students to integrate educational technology into the curriculum as a formative assessment tool. He was program co-chair of the 2014 conference on Educational Data Mining, on the organizing committee for the 2014 Learning Analytics and Knowledge conference, and serves on the executive committee for the Artificial Intelligence in Education Society. He has received numerous academic awards and honors for work on predictive models of learning including a top prize applying his educational analytics in the 2010 KDD Cup, an international big data competition on predicting student performance within an intelligent tutoring system. Pardos comes to UC Berkeley after a post-doc at MIT studying Massive Open Online Courses. At UC Berkeley he directs the Computational Approaches to Human Learning (CAHL) research lab and teaches courses on Data Mining and Analytics, Digital Learning Environments, and Machine Learning in Education.

Current Research

(NSF IIS) Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge Profiles.

(NSF DRK-12) Personalizing Recommendations in a Large-Scale Education Analytics Pipeline.

(BMGF) Next Generation Courseware Challenge: Inspark Science Network for Postsecondary Success in Entry Level Science for Disadvantaged Students. 

(Google) Scaling Cognitive Modeling to Massive Open Environments. 

General areas:

- Digital Learning Environments (MOOCs and Intelligent Tutoring Systems)
- Predictive models of student learning (Computational cognitive modeling)
- Issues of ethics, privacy, and confidentiality in data sharing and research in education

Other Research

Select Service a Professional Activities:

  • Director of Computational Approaches to Human Learning (CAHL) Research Lab (https://github.com/CAHLR)
  • Artificial Intelligence in Education Executive committee
  • Editorial Board – Journal of Educational Data Mining & Int. Journal of AI in Education
  • Panelist - National Academy of Education: Big Data and Privacy (2016)
  • Program co-chair of the 2014 Educational Data Mining Conference
  • Program committee for the 2014,2016 Learning @ Scale Conference
  • Program committee EDM and LAK 2009-2016
  • Community Liaison for the International Educational Data Mining Society
  • Panelist - White House/OSTP: Big Data and Privacy Workshop, Berkeley (2014)
  • Joint Campus Committee on Information Technology (JCCIT)
  • Asiomar Highered Convention: http://asilomar-highered.info/

Teaching:

INFO 254: Data Mining and Analytics (every Spring)
INFO/EDU 290: Machine Learning in Education (every Fall)
WEDUC 161: Digital Learning Environments (every Fall)

Education

Postdoctoral Associate, Physics & CSAIL - Massachusetts Institute of Technology
Doctor of Philosophy, Computer Science - Worcester Polytechnic Institute
Bachelors of Science, Computer Science - Worcester Polytechnic Institute

Last updated:

March 23, 2017