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Activity Number: 511 - High-Dimensional Data Analytics: Theory and Applications
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #324283 View Presentation
Title: Bayesian Estimation of the Q Matrix in Cognitive Diagnosis Models
Author(s): Yinghan Chen*
Keywords: cognitive diagnosis model ; DINA model ; rRUM ; Bayesian statistics ; Fraction-subtraction data

Cognitive diagnosis models(CDM) are partially ordered latent class models and are used to classify students into skill mastery profiles. Application of CDMs requires content expert knowledge of a Q matrix, which maps the test item to its corresponding required attributes or skills. Misspecification of the Q matrix has been shown to yield biased classification. We propose Bayesian frameworks for two popular CDMs (DINA model and rRUM) and estimate the corresponding Q matrix through Metropolis and Gibbs samplers. The proposed algorithms build upon prior research (Chen, Liu, Xu, & Ying, 2015) and ensure that the estimated Q matrices always satisfy the identifiability constraints. Monte Carlo simulation evidence is presented to support the accuracy of parameter recovery. We apply our algorithms to Tatsuoka's fraction-subtraction dataset.

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

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