Zachary A. Pardos, Ph.D.  |
I've started as faculty at UC-Berkeley (pr) Previously: Postdoctoral Associate ALFA Lab - CSAIL, MIT Email: pardos at mit dot edu [google scholar - ms academic - CV] Major: Computer Science Degree status: Defended Ph.D. thesis on 4-26-2012 Research: Learning Analytics, Educational Data Mining, Machine Learning, Bayesian Networks Graduated: May, 2012 |
 |
WPI - Poster (based
on EDM2010 paper)]
WPI - Poster (based
on JMLR W & CP article)]
| 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 |
| 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) |
| 2013: 2013: 2013: |
A Spectral Learning Approach to Knowledge Tracing - Best student
paper winner - EDM - PDF Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX - Best overall paper nominated - EDM - PDF Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes - LAK - PDF |
| 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
-
WPI - Poster
Modeling individualization in a bayesian networks implementation of knowledge tracing - UMAP - PDFLearning what works in ITS from non-traditional randomized controlled trial data - 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 - EDM - PDF |
| 2008: | The composition effect: conjunctive or compensatory? An analysis of multi-skill math questions in ITS - EDM - PDF |
| 2013: 2013: |
EDSciDB: Developing standards and backend support for MOOC Data Science
(Accepted) - moocshop@AIED Towards Moment of Learning Accuracy (Accepted) - Simulated Learners@AIED |
| 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 - PDF, Poster The effect of model granularity on student performance prediction using bayesian networks - UM - PDF, 5-page, Poster |
| 2006: | Using fine-grained skill models to fit student performance with Bayesian networks - ITS - PDF |
| 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 |
|
|