| Modeling individualization in a bayesian networks implementation of knowledge tracing ZA Pardos, NT Heffernan User Modeling, Adaptation, and Personalization: 18th International …, 2010 | 446 | 2010 |
| Mining big data in education: Affordances and challenges C Fischer, ZA Pardos, RS Baker, JJ Williams, P Smyth, R Yu, S Slater, ... Review of Research in Education 44 (1), 130-160, 2020 | 355 | 2020 |
| KT-IDEM: Introducing item difficulty to the knowledge tracing model ZA Pardos, NT Heffernan User Modeling, Adaption and Personalization: 19th International Conference …, 2011 | 333 | 2011 |
| Affective states and state tests: Investigating how affect and engagement during the school year predict end-of-year learning outcomes. ZA Pardos, RSJD Baker, MOCZ San Pedro, SM Gowda, SM Gowda Journal of Learning Analytics 1 (1), 107-128, 2014 | 211 | 2014 |
| Affective states and state tests: Investigating how affect throughout the school year predicts end of year learning outcomes ZA Pardos, RSJD Baker, MOCZ San Pedro, SM Gowda, SM Gowda Proceedings of the third international conference on learning analytics and …, 2013 | 201 | 2013 |
| Data mining and education KR Koedinger, S D'Mello, EA McLaughlin, ZA Pardos, CP Rosé Wiley Interdisciplinary Reviews: Cognitive Science 6 (4), 333-353, 2015 | 179 | 2015 |
| Goal-based course recommendation W Jiang, ZA Pardos, Q Wei Proceedings of the 9th international conference on learning analytics …, 2019 | 133 | 2019 |
| Adapting bayesian knowledge tracing to a massive open online course in edx Z Pardos, Y Bergner, D Seaton, D Pritchard Educational Data Mining 2013, 2013 | 125 | 2013 |
| Does Time Matter? Modeling the Effect of Time with Bayesian Knowledge Tracing. Y Qiu, Y Qi, H Lu, ZA Pardos, NT Heffernan EDM, 139-148, 2011 | 119 | 2011 |
| Using fine-grained skill models to fit student performance with Bayesian networks ZA Pardos, NT Heffernan, B Anderson, CL Heffernan, WP Schools Workshop in Educational Data Mining held at the 8th International Conference …, 2006 | 119 | 2006 |
| Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset ZA Pardos, NT Heffernan Journal of Machine Learning Research W & CP, 2001 | 112* | 2001 |
| The sum is greater than the parts: Ensembling models of student knowledge in educational software ZA Pardos, SM Gowda, RSJ Baker, NT Heffernan ACM SIGKDD explorations newsletter 13 (2), 37-44, 2012 | 111 | 2012 |
| Learning gain differences between ChatGPT and human tutor generated algebra hints ZA Pardos, S Bhandari arXiv preprint arXiv:2302.06871, 2023 | 98 | 2023 |
| Enabling real-time adaptivity in MOOCs with a personalized next-step recommendation framework ZA Pardos, S Tang, D Davis, CV Le Proceedings of the fourth (2017) ACM conference on learning@ scale, 23-32, 2017 | 93 | 2017 |
| Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm Z Pardos, N Heffernan Educational Data Mining 2010, 2010 | 92 | 2010 |
| Designing for serendipity in a university course recommendation system ZA Pardos, W Jiang Proceedings of the tenth international conference on learning analytics …, 2020 | 88 | 2020 |
| Connectionist recommendation in the wild: on the utility and scrutability of neural networks for personalized course guidance ZA Pardos, Z Fan, W Jiang User modeling and user-adapted interaction 29, 487-525, 2019 | 88 | 2019 |
| Clustering students to generate an ensemble to improve standard test score predictions S Trivedi, ZA Pardos, NT Heffernan Artificial Intelligence in Education: 15th International Conference, AIED …, 2011 | 77 | 2011 |
| Ensembling predictions of student knowledge within intelligent tutoring systems RSJ Baker, ZA Pardos, SM Gowda, BB Nooraei, NT Heffernan User Modeling, Adaption and Personalization: 19th International Conference …, 2011 | 69 | 2011 |
| The effect of model granularity on student performance prediction using Bayesian networks ZA Pardos, NT Heffernan, B Anderson, CL Heffernan International Conference on User Modeling, 435-439, 2007 | 68 | 2007 |
