Dr. Pardos is an Associate Professor of Education at UC Berkeley studying adaptive learning and AI. His current research focuses on knowledge representation and recommender systems approaches to increasing upward mobility in postsecondary education using behavioral and semantic data.
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. At Cal, he directs the Computational Approaches to Human Learning research lab, teaches in the data science undergraduate program, and is an affiliated faculty in Cognitive Science.
Latest news releases:
- California 100 Grant to Evaluate Education for California’s Future [final reports]
- Undergraduate Council Report on Student Learning and Quality of Life in the COVID Era and Beyond
- The Resilience of Berkeley - teaching and learning under emergency remote instruction
- The pandemic could open a door to new technology — and dramatic innovation — in education
- What to Look for in Online Learning Apps for K-12
- This is Data Science: Using Machine Learning to Broaden Pathways from Community College
Please see my social media for other news: https://twitter.com/zpardos
For a list of publication relating to AI for articulation and wayfinding in higher education, please see: AskOSki Project
Zhuang, Y., Liu, Q., Zhao, G., Huang, Z., Huang, W., Pardos, Z.A., Chen, E., Wu, J., Li, X. (2023). A Bounded Ability Estimation for Computerized Adaptive Testing. In Proceedings of the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS). New Orleans, LA.
Pardos, Z.A., Tang, M., Anastasopoulos, I., Sheel, S.K., and Zhang, E. (2023). OATutor: An Open-source Adaptive Tutoring System and Curated Content Library for Learning Sciences Research. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 416, 1–17.
Kizilcec, R.F., Baker, R.B., Bruch, E., Cortes, K.E., Hamilton, L.T., Lang, D.N., Pardos, Z.A., Thompson, M.E., Stevens, M.L. (2023). From pipelines to pathways in the study of academic progress. Science, 380, 344-347.
Pardos, Z. A., Borchers, C., & Yu, R. (2023). Credit hours is not enough: Explaining undergraduate perceptions of course workload using LMS records. The Internet and Higher Education, 53, 100882.
Borchers, C., & Pardos, Z. A. (2023). Insights into undergraduate pathways using course load analytics. In Proceedings of the 13th International Learning Analytics and Knowledge Conference (LAK). Association for Computing Machinery, New York, NY, USA, 219–229. *best paper nominated*
Pardos, Z. A., & Bhandari, S. (2023). Learning gain differences between ChatGPT and human tutor generated algebra hints. arXiv preprint arXiv:2302.06871.
Xu, L., Pardos, Z. A., & Pai, A. (2023). Convincing the Expert: Reducing Algorithm Aversion in Administrative Higher Education Decision-making. In Proceedings of the Tenth ACM Conference on Learning@ Scale. Copenhagen, DK. ACM. Pages 215-225.
Xu, Y., Pardos, A.Z. (2023). Mining Detailed Course Transaction Records for Semantic Information. In Proceedings of the 16th International Conference on Educational Data Mining. Bengaluru, India. Pages 388-395.
Condor, A., Pardos, Z., Linn, M. (2022). Representing Scoring Rubrics as Graphs for Automatic Short Answer Grading. In Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (Eds.) Artificial Intelligence in Education. LNCS, vol 13355. Springer, Cham. *best paper nominated*
Condor, A. and Pardos, Z. A. (2022) A deep reinforcement learning approach to automatic formative feedback. In A. Mitrovic and N. Bosch (Eds.) Proceedings of the 15th International Conference on Educational Data Mining. Durham, UK.
McFarland, D. A., Khanna, S., Domingue, B. W., & Pardos, Z. A. (2021) Education Data Science: Past, Present, Future. AERA Open, 7.
Shao, E., Guo, S., & Pardos, Z. A. (2021). Degree Planning with PLAN-BERT: Multi-Semester Recommendation Using Future Courses of Interest. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 14920-14929.
Jiang, W., Pardos, Z.A. (2021) Towards Equity and Algorithmic Fairness in Student Grade Prediction. In B. Kuipers, S. Lazar, D. Mulligan, & M. Fourcade (Eds.) Proceedings of the Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES). ACM. Pages 608–617.
Pardos, Z. A., Rosenbaum, L. F., & Abrahamson, D. (2021). Characterizing learner behavior from touchscreen data. International Journal of Child-Computer Interaction, 100357.
Badrinath, A., Wang, F., Pardos, Z.A. (2021) pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models. In S. Hsiao, & S. Sahebi (Eds.) Proceedings of the 14th International Conference on Educational Data Mining (EDM). Pages 468-474.
Condor, A., Litster, M., Pardos, Z.A. (2021) Automatic short answer grading with SBERT on out-of-sample questions. In S. Hsiao and S. Sahebi (Eds.) Proceedings of the 14th International Conference on Educational Data Mining (EDM). Pages 345-352.
Yu, R., Pardos, Z.A., Chau, H., Brusilovsky, P. (2021) Orienting Students to Course Recommendations Using Three Types of Explanation. In Workshop on Explainable User Models and Personalized Systems (ExUM) in the Adjunct Proceedings of the 29th Conference on User Modeling, Adaptation and Personalization (UMAP). Pages 238–245.
Li, Z., Ren, C., Li, X., & Pardos, Z.A. (2021) Learning Skill Transfer Models Across Systems. In N. Dowell, S. Joksimovic, M. Scheffel, & G. Siemens (Eds.) Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK). ACM. Pages 354-363.
Cockkalingam, S., Yu, R., Pardos, Z.A. (2021) Which one's more work? Predicting effective credit hours between courses. In N. Dowell, S. Joksimovic, M. Scheffel, & G. Siemens (Eds.) Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK). ACM. Pages 599-605.
Jadhav, A., Amir, Y., Pardos, Z.A. (2020) Lexical Relation Mining in Neural Word Embeddings. In Proceedings of the 28th International Conference on Computational Linguistics (COLING). ACL. Pages 1299-1311.
Chiang, H.-Y., Camacho-Collados, J., Pardos, Z.A. (2020) Understanding the Source of Semantic Regularities in Word Embeddings. In Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL). ACL. Pages 119-131.
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.
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.
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*
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.
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.
Active Research Grants
Improving SUNY Transfer Policy Through a data-driven analysis comparing equivalent rigor of 2-year and 4-year courses
Data to Drive Smart Course Selection Research
(California Community Foundation) California 100 [2021-2022]
(Ithika S+R) Improving Articulation of Credit and Transfer Student Support at CUNY [2019-2020]
(Schmidt Futures) Seeding an Undergraduate Learning Engineering Fellowship [2019-2021]
(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
- Faculty affiliate and Head Graduate Advisor 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)
DATA 144/EDUC 244: Data Mining and Analytics (every Fall online) [syllabus]
EDU C260F: Machine Learning in Education (every Spring except '24/'26) [syllabus]
EDUC W161: Digital Learning Environments (every Spring online except '25) [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