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Activity Number: 609 - New Advances in Analysis of Complex Cohort Studies
Type: Topic Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Korean International Statistical Society
Abstract #323217 View Presentation
Title: Latent Class Analysis for Modeling and Promoting Online Learning
Author(s): Jeff Douglas* and Shiyu Wang and Steven Culpepper
Companies: and University of Georgia and University of Illinois
Keywords: latent class ; cognitive diagnosis ; hidden Markov model
Abstract:

Latent class models for learning in online and e-learning settings are introduced. A real data example of an intervention for learning rotational skills in spatial reasoning is used to illustrate restricted latent class models for item responses, known as cognitive diagnosis models, that are coupled with transition models for learning. An array of possibilities are considered that include models with explanatory variables, such as intervention and practice effects, as well as more parsimonious first-order Markov models. The value of response times in assessing learning is considered, and the concept of fluency in which learned attributes are applied more and more easily is introduced. Extensions of the models that include parameters for the instructional value of individual items are given, and MCMC methods for fitting the models are discussed along with results from numerical studies. Future directions and new possibilities for applying learning models in e-learning environments are discussed.


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