Dr. Zachary A. Pardos (zpardos@gmail)

[google scholar - ms academic]

Major: Computer Science
Degree status: Defended Ph.D. thesis on 4-26-2012

National Science Foundation GK-12 Fellow

Research: Learning Analytics, Educational Data Mining, Machine Learning, Bayesian Networks

Graduated: May, 2012
NSF GK12 Fellowship
Acknowledgement: This material is based in part upon work supported by the National Science Foundation under the GK-12 PIMPSE Grant. 
Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

*[06/21/2010: KDD Cup results: 2nd place student & 4th place overall]
- (WPI news release - CS department message about the research and placement - article on the solution)
[03/31/2010: GRAD Poster Competition - 2nd place Science award for work on improving model convergence with EM - 2nd place in Science division WPI - Poster (based on EDM2010 paper)]
[04/03/2011: GRAD Poster Competition - 1st place Science award for work on student assesment and prediction - 1st place in Science division WPI - Poster (based on JMLR W & CP article)]
[Graduating... please standby]
[02/27/2013: Enjoyed attending and presenting at the 2013 Strata conference in Santa Clara, CA]

Publications
(
a list of publications by topic category can be found here)

Book Chapter
2010:
Pardos, Z. A., Heffernan, N. T., Anderson, B., Heffernan, C. (2010) Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks. In C. Romero, S. Ventura, S. R. Viola, M. Pechenizkiy and R. S. J. Baker (Eds.) Handbook of Educational Data Mining. CRC Press, pp. 417-426.  - CRC

Journal & Special Issue Publications
2012:
Pardos, Z.A., Gowda, S. M., Baker, R. S.J.D., Heffernan, N. T., The Sum is Greater than the Parts: Ensembling Models of Student Knowledge in Educational Software. ACM SIGKDD Explorations, 13(2) - PDF
2011:

2011:
Pardos, Z.A., Heffernan, N. T.: Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset. To appear in the Journal of Machine Learning Research W & CP, In Press - PDF (pre-press)
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):47-63. - IJAIED
2007: Razzaq, L., Heffernan, N.T., Feng, M., Pardos, Z.A. (2007) Developing Fine-Grained Transfer Models in the ASSISTment System. Journal of Technology, Instruction, Cognition, and Learning, Vol. 5. Number 3. Old City Publishing, Philadelphia, PA. 2007. pp. 289-304.
TBD:

TBD:

TBD:
Trivedi S, Pardos Z. A., Heffernan N. T., "The Utility of Clustering in Prediction Tasks”, IEEE Transactions on Systems, Man and Cybernetics, Part B. (Under Review)
Trivedi S, Pardos Z. A., Sárközy G. N., Heffernan N. T., “Out of Sample Extensions to Spectral Clustering”, Statistics and Computing, Springer. (In Preparation)
Pardos, Z. A., Heffernan, N. T. Determining the Significance of Item Order . Journal of Educational Data Mining. (Under Review)

Conference Publications (full chapter proceedings with a 15-35% acceptance rate)
2013:
Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes - LAK
2012:
2012:
2012:
Tutor Modeling vs. Student Modeling - FLAIRS - PDF
Content learning analysis using the moment-by-moment learning detector - ITS - PDF
Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction - EDM - PDF
2011:
2011:
2011:
2011:
2011:
2011:
2011:
KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model - UMAP - PDF
Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems - UMAP - PDF
Clustering Students to Generate an Ensemble to Improve Standard Test Score Prediction - AIED - PDF
Less is More: Improving the Speed and Prediction Power of Knowledge Tracing by Using Less Data - EDM - PDF
Spectral Clustering in Educational Data Mining - EDM - PDF
Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing - EDM - PDF
Ensembling Predictions of Student Post-Test Scores for an Intelligent Tutoring System - EDM - PDF
2010:
2010:
2010:
Navigating the parameter space of Bayesian Knowledge Tracing models - EDM - PDF, Presentation - 2nd place in Science division WPI - Poster
Modeling individualization in a bayesian networks implementation of knowledge tracing - Best student paper nominee UMAP - PDF
Learning what works in ITS from non-traditional randomized controlled trial data - Best student paper nominee ITS - PDF
2009:
2009:
Detecting the learning value of items in a randomized problem set - AIED - PDF
Determining the significance of item order in randomized problem sets - Best student paper winner EDM - PDF
2008: The composition effect: conjunctive or compensatory? An analysis of multi-skill math questions in ITS - EDM - PDF

Short and Workshop Papers (short chapter and workshop proceedings)
2012:
2012:
2012:
2012:
2012:
Towards Data Driven Model Improvement - FLAIRS - PDF
Knowledge Component Suggestion for Untagged Content in an Intelligent Tutoring System - ITS - PDF
Clustered Knowledge Tracing - ITS - PDF
The real world significance of performance prediction - EDM - PDF
Investigating Practice Schedules of Multiple Fraction Representations Using Knowledge Tracing Based Learning Analysis Techniques - EDM - PDF
2011:
2011:
2011:
2011:
The Sum is Greater than the Parts: Ensembling Student Knowledge Models in ASSISTments - KDD - PDF
Response Tabling - A simple and practical complement to Knowledge Tracing - KDD - PDF
An Analysis of Response Time Data for Improving Student Performance Prediction - KDD - PDF
Establishing the value of dynamic assessment in an online tutoring system - EDM - PDF
2008: Effective skill assessment using expectation maximization in a multi network temporal Bayesian network - ITS - PDF
2007:
2007:
Analyzing fine-grained skill models using Bayesian and mixed effect methods - AIED - PDFPoster
The effect of model granularity on student performance prediction using bayesian networks - UM - PDF, 5-pagePoster
2006: Using fine-grained skill models to fit student performance with Bayesian networks - ITS - PDF

Tools (for CS525 class)
2010:
2010:
Dataset of student responses to 42 problem sets in The ASSISTment System - download - analyzed in this paper: PDF
Temporal Bayes Net Experimenter for MATLAB - download - requires: BNT

Zach A. Pardos | 100 Institute Road #3213, Worcester, MA 01609-2280 | +1-321-219-9224