Dr. Pardos is an Associate Professor at UC Berkeley studying adaptive learning and AI. His current research focuses on recommender system approaches to increasing upward mobility in postsecondary education and using behavioral and semantic data to map out paths to cognitive and career achievement in K-16.
He earned his PhD in Computer Science at Worcester Polytechnic Institute with a dissertation on computational models of cognitive mastery. Funded by a National Science Foundation Fellowship (GK-12), he spent extensive time with K-12 educators and students working to integrate educational technology into the curriculum as a formative assessment tool. After completing his PhD in 2012, he spent one year as a Postdoctoral Associate at the Massachusetts Institute of Technology applying adaptive learning paradigms to Massive Open Online Courses. At Cal, he directs the Computational Approaches to Human Learning research lab, teaches in the Graduate School of Education and the Division of Computing, Data Science, and Society, and is an affiliated faculty in Cognitive Science.
Please see my social media for news: https://twitter.com/zpardos
Pardos, Z.A., Chau, H., Zhao, H. (2019) Data-Assistive Course-to-Course Articulation Using Machine Translation. In J. C. Mitchell & K. Porayska-Pomsta (Eds.) Proceedings of the 6th ACM Conference on Learning @ Scale (L@S). Chicago, IL. ACM. *Best paper award* [paper] [slides]
Pardos, Z.A., Fan, Z., Jiang, W. (2019) Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-Adapted Interaction, 29(2), 487–525. [paper]
Most Recent (2019-2020):
Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., … Warschauer, M. (2020) Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130–160. *Open access*
Jiang, W., Pardos, Z. A. (2020) Evaluating sources of course information and models of representation on a variety of institutional prediction tasks. In A. Rafferty and J.R. Whitehill (Eds.) Proceedings of the 13th International Conference on Educational Data Mining (EDM). Pages 115-125.
Pardos, Z.A., Jiang, W. (2020) Designing for Serendipity in a University Course Recommendation System. In K. Verbert, M. Scheffel, N. Pinkwart, & V. Kovanovic (Eds.) Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK). ACM. Pages 350–359.
Jiang, W., Pardos, Z.A. (2019) Time Slice Imputation for Personalized Goal-based Recommendation in Higher Education. In D. Tikk & P. Brusilovsky (Eds.) Proceedings of the 13th ACM Conference on Recommender Systems (RecSys). Copenhagen, Denmark. ACM. Pages 506-510.
Dong, M., Yu, R., Pardos, Z.A. (2019) Design and Deployment of a Better Course Search Tool: Inferring latent keywords from enrollment networks. In M. Scheffel & J. Broisin (Eds.) Proceedings of the 14th European Conference on Technology Enhanced Learning (EC-TEL). Delft, The Netherlands. Springer. Pages 480-494.
Kolb, J., Farrar, S., Pardos, Z.A. (2019) Generalizing Expert Misconception Diagnoses Through Common Wrong Answer Embedding. In M. Desmarais, C.F. Lynch, A. Merceron, & R. Nkambou (Eds.) Proceedings of the 12th International Conference on Educational Data Mining (EDM). Montreal, Canada. pp. 342-347. [slides]
Pardos, Z.A., Chau, H., Zhao, H. (2019) Data-Assistive Course-to-Course Articulation Using Machine Translation. In J. C. Mitchell & K. Porayska-Pomsta (Eds.) Proceedings of the 6th ACM Conference on Learning @ Scale (L@S). Chicago, IL. ACM. *Best paper award* [slides]
Pardos, Z.A., Horodyskyj, L. (2019) Analysis of Student Behaviour in Habitable Worlds Using Continuous Representation Visualization. Journal of Learning Analytics, 6(1), 1-15.
Jiang, W., Pardos, Z.A., Wei, Q. (2019) Goal-based Course Recommendation. In C. Brooks, R. Ferguson & U. Hoppe (Eds.) Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK 2019). ACM. Tempe, Arizona. Pages 36-45. [slides]
Pardos, Z.A., Fan, Z., Jiang, W. (2019) Connectionist Recommendation in the Wild: On the utility and scrutability of neural networks for personalized course guidance. User Modeling and User-Adapted Interaction, 29(2), 487–525.
Select Prior Work:
Pardos, Z. A., & Heffernan, N. T. (2010) Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. In Proceedings of the International Conference on User Modeling, Adaptation, and Personalization (UMAP). Big Island, Hawaii. Springer, Berlin, Heidelberg. Pages 255-266.
Pardos, Z. A., Bergner, Y., Seaton, D., Pritchard, D.E. (2013) Adapting Bayesian Knowledge Tracing to a Massive Open Online College Course in edX. In S.K. D’Mello, R.A. Calvo, & A. Olney (Eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM). Memphis, TN. Pages 137-144.
Pardos, Z. A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2014) Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes. Journal of Learning Analytics, 1(1), 107–128. [conference version]
Pardos, Z.A. (2017) Big Data in Education and the Models that Love Them. Current Opinion in Behavioral Sciences. Vol 18, 107-113.
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 C. Thille & J. Reich (Eds.) Proceedings of the 4th Conference on Learning @ Scale (L@S). ACM. Pages 23-32. [slides]
Active Research Grants
(Ithika S+R) Improving Articulation of Credit and Transfer Student Support at CUNY [2019-2020]
(California OPR) Community Sourced, Data-Driven Improvements to Open, Adaptive Courseware [2019-2022]
(Peder Sather Center Research) Embracing Data in Educational Systems: The Use of Learning Analytics to Support Students at Risk [2019-2020]
(Google) Scaling Cognitive Modeling to Massive Open Environments [2014-2019]
(NSF IIS) Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge Profiles [2015-2019]
(NSF DRK-12) Personalizing Recommendations in a Large-Scale Education Analytics Pipeline [2015-2019]
General research areas:
- 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 grad students and undergraduate research assistants. Consult tiny.cc/zpUCB to schedule a meeting.
Select Service / Professional Activities
- Director of Computational Approaches to Human Learning (CAHL) Research Lab
- Director of the virtual advising project, AskOski
- Affiliate faculty in Cognitive Science
- Organizing committee for the IJCAI Workshop on Multimodal Analytics for Understanding Human Learning
- Artificial Intelligence in Education Executive committee member
- Program committee member (2020): ACM L@S, ACM LAK, ACM RecSys, ACM SIGCHI (AC), AIED, EDM
- AAAI - EAAI New and Future AI Educator (2018)
- Editorial Board – Journal of Educational Data Mining & Int. Journal of AI in Education
- Panelist/speaker - National Academy of Education: Big Data and Privacy (2016)
- 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)
INFO 254/DATA 144: Data Mining and Analytics (every Fall) [syllabus]
INFO/EDU C260F: Machine Learning in Education (every Spring) [syllabus][page]
WEDUC 161: Digital Learning Environments (every Spring - online, UC wide ) [syllabus][website]
EDUC 290A/003: Computational Approaches to Human Learning (CAHL) research group (every Fall) [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. After the first meeting brainstorming session (and food), a list of topics will be developed that students can choose from to discuss during one meeting of class. The second expected contribution is that each student use one meeting to present work of theirs, related directly or tangentially to the group's research area. Except for the first and last meeting, the class will meet ONLINE (on Zoom) Wednesdays 11:30-1pm (CCN is TBA).
Postdoctoral Associate, RLE & CSAIL - Massachusetts Institute of Technology
Doctor of Philosophy, Computer Science - Worcester Polytechnic Institute
Bachelors of Science, Computer Science - Worcester Polytechnic Institute