Zachary A. Pardos

Dr. Pardos studies the representation of knowledge as communicated by student behavior and engineers personalized supports leveraging big data in education. His current projects focus on increasing upward mobility in the California postsecondary system 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. 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. He holds several academic leadership positions in the learning analytics community, including posts as an editorial board member for two if its journals (JEDM and IJAIED), executive committee member for the Artificial Intelligence in Education Society, and program committee member of the 2018 education conferences; ICLS, ITS, LAK, AIED and L@S. Dr. Pardos 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.

Teaching:

INFO 254: Data Mining and Analytics (every Spring) [syllabus]
INFO/EDU C290F: Machine Learning in Education (every Fall) [page]
EDUC W161: Digital Learning Environments (every Fall - online, UC wide - website) [syllabus]
EDUC 290A/003: Computational Approaches to Human Learning (CAHL) research group (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).

Please see the CV pdf for an updated list of my publications and my iSchool web page.

Publications

Most recent work (2017):

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 (2017) ACM Conference on Learning @ Scale (L@S '17). Cambridge, MA. ACM. Pages 23-32. [acm] [slides]

Pardos, Z.A. & Nam, A. (2017) The School of Information and its Relationship to Computer Science at UC Berkeley. In iConference 2017 Proceedings (pp.309-316). Wuhan, China. [pdf] [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]

Past 5 years:

Journal Articles

Pardos, Z. A., Whyte, A., & Kao, K. (2016) moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization. Technology, Knowledge and Learning, (Volume upcoming), p1-24.

Pardos, Z.A. (2015) Commentary On "Beyond Time-on-Task: The Relationship Between Spaced Study and Certification in MOOCs." Journal of Learning Analytics and Knowledge. Vol 2(2). Pp. 70-74 *Invited article commentary

Koedinger, K. R., D'Mello, S., McLaughlin, E. A., Pardos, Z. A., Rosé, C. P. (2015) Data mining and education. WIREs Cognitive Science. doi: 10.1002/wcs.1350

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

Rau, M.A., Aleven, V., Rummel, N., & Pardos, Z. (2014). How Should Intelligent Tutoring Systems Sequence Multiple Graphical Representations of Fractions? A Multi-Methods Study. International Journal of Artificial Intelligence in Education. 24(2), 125-161.

Pardos, Z.A., Gowda, S. M., Baker, R. S.J.D., Heffernan, N. T. (2012) The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software. In ACM's Knowledge Discovery and Datamining (KDD) Explorations, 13(2)

Pardos, Z.A., Dailey, M. & Heffernan, N. (2011) Learning what works in ITS from non-traditional randomized controlled trial data. The International Journal of Artificial Intelligence in Education, 21(1-2):45-63.

Conference Proceedings (stringent peer review)

Pardos, Z. A. & Kao, K. (2015). moocRP: An Open-source  Analytics Platform. In Woolf, B., Russell, D. and Kiczales, G. (eds.) Proceedings of the 2nd International  ACM Conference on Learning@ Scale (pp. 103-110).  Vancouver, CA. ACM.

MacHardy, Z., Pardos, Z.A. (2015) Evaluating The Relevance of Educational Videos using BKT and Big Data. In Romero, C. and Pechenizkiy, M. (eds.) Proceedings of the 8th International Conferenceon Educational Data Mining. Madrid, Spain. Pages 424-427.

Tang, S., Gogel, H., McBride, E., Pardos, Z.A. (2015) Desirable Difficulty and Other Predictors of Effective Item Orderings. In Romero, C. and Pechenizkiy, M. (eds.) Proceedings of the 8th International Conferenceon Educational Data Mining. Madrid, Spain. Pages 416-419.

Corrigan, S., Barkley, T., & Pardos, Z. (2015). Dynamic Approaches to Modeling Student Affect and its Changing Role in Learning and Performance. In Bontchieva, K. and Ricci, F. (eds.) Proceedings of the 23rd International Conference on User Modeling, Adaptation and Personalization (pp. 92-103). Springer.

Pardos, Z.A., Bergner, Y., Seaton, D., Pritchard, D.E. (2013) Adapting Bayesian Knowledge Tracing to a Massive Open Online College Course in edX. D’Mello, S. K., Calvo, R. A., and Olney, A. (eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM). Memphis, TN. Pages 137-144. *Best paper nominated

Falakmasir, M. H., Pardos, Z. A., Gordon, G. J., Brusilovsky, P. (2013). A Spectral Learning Approach to Knowledge Tracing. In D’Mello, S. K., Calvo, R. A., and Olney, A. (eds.) Proceedings of the 6th International Conference on Educational Data Mining (EDM). Memphis, TN, U.S.A. Pages 28-34 *Best student paper winner

Pardos, Z.A., Baker, R.S.J.d., San Pedro, M.O.C.Z., Gowda, S.M., Gowda, S.M. (2013) Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes. In Suthers, D.D., Verbert, K., Duval, E. & Ochoa, X. (eds.) Proceedings of the 3rd International Conference on Learning Analytics and Knowledge (LAK). Leuven, Belgium. ACM. Pages 117-124.

Pardos, Z.A., Trivedi, S., Heffernan, N. T., Sarkozy, G. (2012) Clustered Knowledge Tracing. In Cerri, S.A., Clancey, W.J. (eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (ITS). Crete, Greece. Pages 405-410.

Trivedi, S. Pardos, Z., Sarkozy, G. & Heffernan, N.T. (2012) Co-Clustering by Bipartite Spectral Graph Partitioning for Out-Of-Tutor Prediction. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM). Crete, Greece. Pages. 33-40.

Karlovcec, M., Cardova-Sanchez, M., Pardos, Z.A. (2012) Knowledge Component Suggestion for Untagged Content in an Intelligent Tutoring System. In Cerri, S.A., Clancey, W.J. (eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (ITS). Crete, Greece. Springer. Pages 195-200.

Pardos, Z.A., Wang, Q. Y., Trivedi, S. (2012) The real world significance of performance prediction. . In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM). Crete, Greece. Pages 192-195.

Yumeng, Q., Pardos, Z.A., Heffernan, N.T. (2012) Towards data driven user model improvement. In Youngblood, M.G., McCarthy, P. (eds.) Proceedings of the 25th annual Florida Artificial Intelligence Research Society Conference (FLAIRS). Marco Island, FL.

Pardos, Z.A., Heffernan, N.T. (2012) Tutor Modeling vs. Student Modeling. In Proceedings of the 25th annual Florida Artificial Intelligence Research Society Conference. Marco Island, FL. AAAI. Pages 420-425 *Invited article

Gowda, S., Pardos, Z.A., Baker, S.J.D.R. (2012) Content learning analysis using the moment-by-moment learning detector. In Cerri, S.A., Clancey, W.J. (eds.) Proceedings of the 11th International Conference on Intelligent Tutoring Systems (ITS). Crete, Greece. Springer. Pages 434-443.

Rau, M., Pardos, Z.A. (2012) Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., and Stamper, J. (eds.) Proceedings of the 5th International Conference on Educational Data Mining (EDM). Crete, Greece. Pages 168-171.

Pardos, Z. & Heffernan, N. (2011) KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model. In Konstant et al. (eds.) Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP). Girona, Spain. Springer. Pages 243-254.

Feng, M., Heffernan, N., Pardos, Z. & Heffernan, C. (2011) Establishing the value of dynamic assessment in an online tutoring system In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (eds.) Proceedings of the 4th International Conference on Educational Data Mining. Eindhoven, Netherlands. Pages 295-300.

Trivedi, S., Pardos, Z. & Heffernan, N. (2011) Clustering Students to Generate an Ensemble to Improve Standard Test Score Predictions In Biswas et al (eds.) Proceedings of the 15th bi-annual Artificial Intelligence in Education Conference (AIED). Springer. LNAI 6738. Pages 328–336.

Trivedi, S., Pardos, Z., Sarkozy, G. & Heffernan, N. (2011) Spectral Clustering in Educational Data Mining. In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM). Pages 129-138.

Qiu, Y., Qi, Y., Lu, H., Pardos, Z. & Heffernan, N.T. (2011) Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing In Pechenizkiy, M., Calders, T., Conati, C., Ventura, S., Romero , C., and Stamper, J. (Eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM). Pittsburgh, PA. Pages 139-148.

Baker, R., Pardos, Z., Gowda, S., Nooraei, B., & Heffernan, N. (2011) Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems. In Konstant et al (Eds.) Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP). Girona, Spain. Springer. Pages 13-24.

Nooraei, B., Pardos, Z.A., Heffernan, N.T., Baker, R.S.J.d (2011) Less Is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data. In Ventura, S., Romero , C., and Stamper, J. (eds.) Proceedings of the 4th International Conference on Educational Data Mining (EDM). Eindhoven, Netherlands. Pages 101-110.

Presentations/Professional Experience

1. White House / OSTP Big Data: Values and Governance, "Deep dive on new opportunities and challenges in health and education" [link] - April 1st, 2014.

2. Asilomar Highered Convetion, "Words of Wisdom" [link] - June 1-4, 2014

3. UC eNGAGE: Enhancing the student experience through technology, "UC Innovation: Data Analytics" [link] - October 24, 2014.

(historic presentations to be added)

Reviewing

Journal

Computers and Education

Int. Journal of Artificial Intelligence in Education

Journal of Educational Data Mining

Journal of Learning Analytics

PNAS, PLOS One

The Internet and Higher Education

Transactions on Interactive Intelligent Systems

    Transactions on Learning Technologies

Handbook of Educational Data Mining

Handbook of Learning Analytics and EDM


Conference

Educational Data Mining ‘09-‘16

Artificial Intelligence in Education ’13,‘15

Learning @ Scale ’14, ’16, ‘17

Learning Analytics and Knowledge ’14-‘17

Learning With MOOCs ‘14

Intelligent Tutoring Systems ‘16

Interests and Professional Affiliations

Qualitative research methods

Learning Analytics

Digital Learning Environments

Machine Learning

Big Data in Education

Educational Data Mining

Knowledge Representation

Degree(s)

Postdoctoral Associate, Physics & CSAIL - Massachusetts Institute of Technology

Doctor of Philosophy, Computer Science - Worcester Polytechnic Institute

Bachelors of Science, Computer Science - Worcester Polytechnic Institute

Curriculum Vitae

Contact Information

Office #4232

Graduate School of Education
Berkeley Way West Building (BWW)
UC Berkeley
2121 Berkeley Way
Berkeley, CA 94720-1670

Phone

(321) 219-9224

Staff Contact

Ann Foley