Author: Zachary A. Pardos

Ph.D Student Fellow

Zach Pardos works as an NSF funded fellow at Worcester Polytechnic Institute. He is pursuing a Ph.D. degree in Computer Science with advisor Neil Heffernan in the field of Intelligent Tutoring Systems. Zach\'s research focuses on using advanced machine learning techniques, such as Bayesian Network, to create accurate models of learning that can detect effective tutoring content and predict student achievement.

Publications

Publication period start: 2011
Number of co-authors: 6

Co-authors

Number of publications with favourite co-authors
Sujith M. Gowda
1
Bahador B. Nooraei
1
Neil T. Heffernan
4

Productive Colleagues

Most productive colleagues in number of publications
Sujith M. Gowda
2
Neil T. Heffernan
7
Ryan S. J. d. Baker
7

Publications

Pardos, Zachary A., Heffernan, Neil T., Anderson, Brigham, Linquist-Heffernan, Cristina (2007): The Effect of Model Granularity on Student Performance Prediction Using Bayesian Networks. In: Conati, Cristina, McCoy, Kathleen F., Paliouras, Georgios (eds.) User Modeling 2007 - 11th International Conference - UM 2007 June 25-29, 2007, Corfu, Greece. pp. 435-439. https://dx.doi.org/10.1007/978-3-540-73078-1_60

Pardos, Zachary A., Heffernan, Neil T. (2010): Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing. In: Proceedings of the 2010 Conference on User Modeling, Adaptation and Personalization , 2010, . pp. 255-266. https://www.springerlink.com/content/T53466280020K205

Baker, Ryan S. J. d., Pardos, Zachary A., Gowda, Sujith M., Nooraei, Bahador B., Heffernan, Neil T. (2011): Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems. In: Proceedings of the 2011 Conference on User Modeling, Adaptation and Personalization , 2011, . pp. 13-24. https://www.springerlink.com/content/2583971022V38370

Pardos, Zachary A., Heffernan, Neil T. (2011): KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model. In: Proceedings of the 2011 Conference on User Modeling, Adaptation and Personalization , 2011, . pp. 243-254. https://www.springerlink.com/content/700W38580UR60483

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