Detecting intervention effects with a cognitive diagnostic model for learning trajectories
Li, Anqi
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https://hdl.handle.net/2142/106500
Description
Title
Detecting intervention effects with a cognitive diagnostic model for learning trajectories
Author(s)
Li, Anqi
Issue Date
2019-12-12
Director of Research (if dissertation) or Advisor (if thesis)
Culpepper, Steven A.
Chang, Hua-Hua
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
NA
Abstract
As students are exposed to more learning materials or resources, educators and researchers become more interested in selecting effective tools in assisting students' learning process. Specifically, students' mastery and growth of skills in a longitudinal manner depend on the various instructional impacts. However, most current studies tracking transitions of students' mastery of skills rarely addressed instructional effects. For those studies involving intervention covariates, instructional effects are not differentiated considering their categories, the time points they are assigned, and their interactions. This study focuses on the common educational setting when students are assessed after certain instructions. We proposed a cognitive diagnostic model framework in detecting instructional intervention effects while assessing students' learning trajectories. Two specific probit regression models are introduced under the framework. A Bayesian modeling formulation is presented, and Gibbs sampling algorithm is proposed for parameter estimation. Simulation study results show that the proposed model provides accurate estimation of intervention effects and reliable recovery of students' latent attributes.
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