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Activity Number: 88
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #309156
Title: Bayesian Partially Ordered Multinomial Probit/Logit Model with an Application to Course Redesign
Author(s): Michael Sonksen and Kristin Umland and Xueqin Shelley Wang*+
Companies: The University of New Mexico and The Univeristy of New Mexico and The University of New Mexico
Keywords: Markov Chain Monte Carlo ; latent variable models ; multilevel logistic regression ; online learning ; model diagnostics

Large entry-level courses are commonplace at public 2- and 4-year institutions of higher education (IHEs) across the United States. Low pass rates in these entry-level courses, coupled with tight budgets, have put pressure on IHEs to look for ways to teach more students more effectively at a lower cost. Efforts to improve student outcomes in such courses are often called "course redesigns." The difficulty arises in trying to determine the impact of a particular course redesign; true random-controlled trials are expensive and time-consuming, and few IHEs have the resources or patience to implement them. As a result, almost all evaluations of efforts to improve student success at scale rely on observational studies. At the same time, standard multi-level models may be inadequate to extract meaningful information from the complex and messy sets of student data available to evaluators because they throw away information by treating all passing grades equally. We propose a new Bayesian approach that keeps all grading information: a Partially Ordered Multinomial Probit Model with random effects fit using a Markov Chain Monte Carlo algorithm, and a Logit model can be fit with importance sampling. Simulation studies show that the Bayesian Partially Ordered Probit/Logit Models work well, and the parameter estimation is precise in large samples. We also compared this model with standard models considering Mean Squared Error and the area under the Receiver Operating Characteristic (ROC) curve.

Authors who are presenting talks have a * after their name.

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