Dr. Pardos is an Assistant Professor at UC Berkeley in a joint position between the School of Information and the Graduate School of Education. His focal areas of study are knowledge representation and personalized supports leveraging big data in education. He earned his PhD in Computer Science at WPI and comes to UC Berkeley after a post-doc at MIT Computer Science Artificial Intelligence Lab (CSAIL). 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.
Most recent work (2017):
Pardos, Z.A. (accepted) Big Data in Education and the Models that Love Them. Current Opinion in Behavioral Sciences.
Luo, Y., Pardos, Z.A. (accepted) Diagnosing University Student Subject Proficiency and Predicting Degree Completion in Vector Space. To appear in the Proceedings of the Eighth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18).
Pardos, Z.A., Horodyskyj, L. (2017) Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization. CoRR preprint, abs/1710.06654. [arXiv]
Pardos, Z.A., Dadu, A. (2017) Imputing KCs with Representations of Problem Content and Context. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP'17). Bratislava, Slovakia. ACM. Pages 148-155. [acm] [slides]
Pardos, Z.A., Tang, S., Davis, D., Le. C.V. (2017) Enabling Real-Time Adaptivity in MOOCs with a Personalized Next-Step Recommendation Framework. In Proceedings of the Fourth ACM Conference on Learning @ Scale (L@S '17). Cambridge, MA. ACM. Pages 23-32. [acm] [slides]
Tang, S., Peterson, J., and Pardos, Z. (2017). Predictive Modelling of Student Behaviour Using Granular Large-Scale Action Data. In Lang, C., Siemens, G., Wise, A. F., and Gaevic, D., editors, The Handbook of Learning Analytics, pages 223–233. Society for Learning Analytics Research (SoLAR), Alberta, Canada, 1st edition. [pdf]
Sharma, K., Jermann, P., Dillenbourg, P., Rau, M., Pardos, Z. A., Schneider, B., D’Angelo, S., Gergle, D., & Prieto, L. (2017). CSCL and Eye-tracking: Experiences, Opportunities and Challenges. In B. K. Smith, M. Borge, E. Mercier & K. Y. Lim (Eds.), Making a Difference: Prioritizing Equity and Access in CSCL, 12th International Conference on Computer Supported Collaborative Learning . Vol. 2, pp. 727-734. Philadelphia, PA: International Society of the Learning Sciences. [isls]
(NSF IIS) Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge Profiles [2015-present]
(NSF DRK-12) Personalizing Recommendations in a Large-Scale Education Analytics Pipeline [2015-present]
(BMGF) Next Generation Courseware Challenge: Inspark Science Network for Postsecondary Success in Entry Level Science for Disadvantaged Students [2016-2017]
(Google) Scaling Cognitive Modeling to Massive Open Environments 
- Representing knowledge as communicated by student behaviors
- Personalized educational supports leveraging learner process data
- Digital Learning Environments (online courses and Intelligent Tutoring Systems)
I am currently accepting PhDs (in Education and the iSchool). Consult tiny.cc/zpUCB to schedule a meeting.
Select Service / Professional Activities
- Director of Computational Approaches to Human Learning (CAHL) Research Lab: https://github.com/CAHLR
- Artificial Intelligence in Education Executive committee
- Program committee member (2018): ICLS, L@S, LAK, AIED, ITS
- Editorial Board – Journal of Educational Data Mining & Int. Journal of AI in Education
- Panelist/speaker - National Academy of Education: Big Data and Privacy (2016): http://naeducation.org/bigdata
- Program co-chair of the 2014 Educational Data Mining Conference
- Community Liaison for the International Educational Data Mining Society
- Panelist - White House/OSTP: Big Data and Privacy Workshop, Berkeley (2014)
- Co-keynote/seminar speaker: ACT, Educational Testing Service, Intel Adaptive Education
- Joint Campus Committee on Information Technology (JCCIT), 2016-2017
- Asiomar Highered Convention: http://asilomar-highered.info/
INFO 254: Data Mining and Analytics (every Spring) [syllabus]
INFO/EDU C290F: Machine Learning in Education (every Fall) [page]
WEDUC 161: Digital Learning Environments (every Fall - online, UC wide ) [website][syllabus]
EDUC 290A/003: Computational Approaches to Human Learning (CAHL) research group (every semester) [website]
Research group class info: This group will be run as a platform for discussions on topics ranging from analysis of equity, diversity, and inclusion on campus to the role of AI in K-16 education. Each session will involve a workshoping aspect, such as designing taxonomies or running analyses with new learning analytics tools shared by classmates and the facilitator (me). While this class can be a segue to research in my lab, it is primarily meant to be a thinktank, of sorts, of its own. The group will meet Wednesdays 11:30-1pm in Tolman Hall 4529 (CCN is 15555).
Postdoctoral Associate, Physics & CSAIL - Massachusetts Institute of Technology
Doctor of Philosophy, Computer Science - Worcester Polytechnic Institute
Bachelors of Science, Computer Science - Worcester Polytechnic Institute
How to reach me
Office: 211 South Hall and 4641 Tolman Hall
Office hours: (by appointment) - http://tiny.cc/zpUCB