Stephen E. Fancsali

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Susan R BermanUniversity of Pittsburgh, UPMC, Carnegie Learning, Inc.Verified email at carnegielearning.com
Michael (Mikhail) YudelsonPrincipal Machine Learning/ Artificial Intelligence Engineer, BrainPOPVerified email at brainpop.com
Ambarish JoshiCarnegie Learning, Research AnalystVerified email at carnegielearning.com
Vasile RusThe University of MemphisVerified email at memphis.edu
Robert GM HausmannCarnegie Learning, Inc.Verified email at carnegielearning.com
John StamperHuman-Computer Interaction Institute, Carnegie Mellon UniversityVerified email at stamper.org
Guoguo ZhengUniversity of GeorgiaVerified email at uga.edu
Ryan C SchwiebertCenter of Ring Theory and its ApplicationsVerified email at ohio.edu
R. Charles MurrayVerified email at chasmurray.com
Vincent AlevenProfessor of Human-Computer Interaction, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Kenneth HolsteinCarnegie Mellon UniversityVerified email at cs.cmu.edu
Bruce M. McLarenAssociate Research Professor, Carnegie Mellon University, U.S.Verified email at cs.cmu.edu
David DanksProfessor of Data Science & Philosophy; UC San DiegoVerified email at ucsd.edu
Richard ScheinesDean, Dietrich College of Humanities and Social Sciences, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Nabin MaharjanData Scientist, Audible Inc. (an Amazon Company)Verified email at audible.com
Lasang J.TamangPhD candidate, University of MemphisVerified email at memphis.edu
Charles Chuankai ZhangUniversity of MinnesotaVerified email at umn.edu
J Derek Lomas PhDIndustrial Design Engineering, Delft University of TechnologyVerified email at tudelft.nl
Nirmal PatelPlaypower LabsVerified email at playpowerlabs.com
Matthew L BernackiAssistant Professor, University of North Carolina, Chapel HillVerified email at unc.edu

Stephen E. Fancsali

Director of Advanced Analytics, Carnegie Learning, Inc.

Verified email at carnegielearning.com - Homepage

Educational Data Science Learning Analytics Causal Discovery Machine Learning


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How Mastery Learning Works at Scale S Ritter, M Yudelson, SE Fancsali, SR Berman Proceedings of the Third (2016) ACM Conference on Learning @ Scale, 71-79, 2016	75	2016
Predicting Standardized Test Scores from Cognitive Tutor Interactions S Ritter, A Joshi, SE Fancsali, T Nixon Sixth International Conference on Educational Data Mining, 2013	40	2013
Causal Discovery with Models: Behavior, Affect, and Learning in Cognitive Tutor Algebra SE Fancsali 7th International Conference on Educational Data Mining (EDM 2014), 28-35, 2014	34	2014
Revealing the learning in learning curves RC Murray, S Ritter, T Nixon, R Schwiebert, RGM Hausmann, B Towle, ... Artificial Intelligence in Education: 16th International Conference, AIED …, 2013	29	2013
Optimal and Worst-Case Performance of Mastery Learning Assessment with Bayesian Knowledge Tracing SE Fancsali, T Nixon, S Ritter Sixth International Conference on Educational Data Mining, 2013	27	2013
Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics SE Fancsali, S Ritter Fourth International Conference on Learning Analytics And Knowledge, 73-77, 2014	25	2014
Intelligent Instructional Hand Offs SE Fancsali, MV Yudelson, SR Berman, S Ritter Educational Data Mining 2018, 198-207, 2018	19	2018
Variable construction for predictive and causal modeling of online education data SE Fancsali Proceedings of the 1st International Conference on Learning Analytics and …, 2011	19	2011
Comorbid science? D Danks, S Fancsali, C Glymour, R Scheines Behavioral and Brain Sciences 33 (2-3), 153-155, 2010	19	2010
Using embedded formative assessment to predict state summative test scores SE Fancsali, G Zheng, Y Tan, S Ritter, SR Berman, A Galyardt Proceedings of the 8th International Conference on Learning Analytics and …, 2018	18	2018
Better Data Beat Big Data MV Yudelson, SE Fancsali, S Ritter, SR Berman, T Nixon, A Joshi 7th International Conference on Educational Data Mining (EDM 2014), 205-208, 2014	17	2014
Data-driven causal modeling of “gaming the system” and off-task behavior in Cognitive Tutor Algebra SE Fancsali NIPS 2013 Workshop on Data-Driven Education, 2013	17	2013
Variable Construction and Causal Discovery for Cognitive Tutor Log Data: Initial Results. S Fancsali EDM, 238-239, 2012	16	2012
Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics G Zheng, SE Fancsali, S Ritter, S Berman Journal of Learning Analytics 6 (2), 153–174, 2019	15	2019
Carnegie Learning’s Adaptive Learning Products S Ritter, R Carlson, M Sandbothe, SE Fancsali 8th International Conference on Educational Data Mining, 2015	13	2015
An Analysis of Human Tutors' Actions in Tutorial Dialogues V Rus, N Maharjan, LJ Tamang, M Yudelson, S Berman, SE Fancsali, ... Thirtieth International Florida Artificial Intelligence Research Society …, 2017	12	2017
Toward “Hyper-Personalized” Cognitive Tutors: Non-Cognitive Personalization in the Generalized Intelligent Framework for Tutoring SE Fancsali, S Ritter, J Stamper, T Nixon AIED 2013 Workshops Proceedings Volume 7, 71, 2013	12*	2013
The Complex Dynamics of Aggregate Learning Curves T Nixon, S Fancsali, S Ritter Sixth International Conference on Educational Data Mining, 2013	12	2013
Towards Integrating Human and Automated Tutoring Systems S Ritter, SE Fancsali, S Berman, M Yudelson 9th International Conference on Educational Data Mining, 626-627, 2016	11	2016
Simulated Students, Mastery Learning, and Improved Learning Curves for Real-World Cognitive Tutors SE Fancsali, T Nixon, A Vuong, S Ritter AIED 2013 Workshops Proceedings Volume 4, 2013	11	2013

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