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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: Sparse Factor Analysis for Learning and Content Analytics

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We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner’s knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question’s intrinsic difficulty. We estimate these factors given the graded responses to a collection of questions. The underlying estimation problem is ill-posed in general, especially when only a subset of the questions are answered.
The key observation that enables a well-posed solution is the fact that typical educational domains of interest involve only a small number of key concepts. Leveraging this observation, we develop a bi-convex maximumlikelihood solution to the resulting SPARse Factor Analysis (SPARFA) problem. We also propose SPARFA-Tag and SPARFA-Top, two extensions to SPARFA that incorporate instructor-defined tags on questions and question text to facilitate the interpretability of the estimated factors.
Additonally, we propose SPARFA-Trace, a new framework for timevarying learning and content analytics. We develop a novel message passing-based, blind, approximate Kalman filtering and smoothing algorithm for SPARFA that jointly traces student concept knowledge evolution over time, analyzes student concept knowledge state transitions (induced by studying learning resources, such as textbook sections, lecture videos, etc., or the forgetting effect), and estimates the content organization and difficulty of the questions in assessments. These quantities are estimated solely from binary-valued (correct/incorrect) graded student response data and the specific actions each student performs (e.g., answering a question or studying a learning resource) at each time instant.

Speakers
avatar for Richard Baraniuk

Richard Baraniuk

Victor E Cameron Professor of Electrical and Computer Engineering, Rice University
Richard G. Baraniuk is the Victor E. Cameron Professor of Electrical and Computer Engineering at Rice University and the founder and director of OpenStax. In 1999, Dr. Baraniuk launched Connexions (now OpenStax CNX), one of the world’s first and today one of the world’s largest “open education” platforms, providing free and remix able e-textbooks to millions of users from 200 countries. More recently, through his... Read More →


Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)