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Optimal Ranking of Test Items using the Rasch Model
(2016)2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton)We study the problem of ranking test items, i.e., the ordering of items according to the amount of information they provide on the latent trait of the respondents. We focus on educational applications, where instructors are interested in ranking questions so as to select a small set of informative questions in order to efficiently assess the students' understanding on the course material. Using the Rasch model for modeling student responses, ...Conference Paper -
Matrix Recovery from Quantized and Corrupted Measurements
(2014)2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)This paper deals with the recovery of an unknown, low-rank matrix from quantized and (possibly) corrupted measurements of a subset of its entries. We develop statistical models and corresponding (multi-)convex optimization algorithms for quantized matrix completion (Q-MC) and quantized robust principal component analysis (Q-RPCA). In order to take into account the quantized nature of the available data, we jointly learn the underlying ...Conference Paper -
Time-varying Learning and Content Analytics via Sparse Factor Analysis
(2014)Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data miningWe propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for educational applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA) that jointly traces learner concept knowledge over time, analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, ...Conference Paper -
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data
(2016)Proceedings of Machine Learning Research ~ Proceedings of the International Conference on Machine Learning, 20-22 June 2016, New York, New York, USAStatistical models of student responses on assessment questions, such as those in homeworks and exams, enable educators and computer-based personalized learning systems to gain insights into students' knowledge using machine learning. Popular student-response models, including the Rasch model and item response theory models, represent the probability of a student answering a question correctly using an affine function of latent factors. ...Conference Paper -
Quantized matrix completion for personalized learning
(2014)Proceedings of the 7th International Conference on Educational Data MiningConference Paper -
Tag-aware ordinal sparse factor analysis for learning and content analytics
(2013)Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013)Conference Paper