Reduced Reparameterized Unified Model Applied to Learning Spatial Rotation Skills

Research output: Chapter in Book/Report/Conference proceedingChapter


There has been a growing interest in measuring students in a learning context. Cognitive diagnosis models (CDMs) are traditionally used to measure students’ skill mastery at a static time point, but recently, they have been combined with longitudinal models to track students’ changes in skill acquisition over time. In this chapter, we propose a longitudinal learning model with CDMs. We consider different kinds of measurement models, including the reduced-reparameterized unified model (r-RUM) and the noisy input, deterministic-“and”-gate (NIDA) model. We also consider the incorporation of theories on skill hierarchies. Different models are fitted to a data set collected from a computer-based spatial rotation learning program (Wang S, Yang Y, Culpepper SA, Douglas JA, J Educ Behav Stat, 2016. and we evaluate and compare these models using several goodness-of-fit indices.
Original languageEnglish (US)
Title of host publicationHandbook of Diagnostic Classification Models
Subtitle of host publicationModels and Model Extensions, Applications, Software Packages
EditorsMatthias von Davier, Young-Sun Lee
Number of pages22
ISBN (Electronic)9783030055844
ISBN (Print)9783030055837
StatePublished - 2019

Publication series

NameMethodology of Educational Measurement and Assessment
ISSN (Print)2367-170X
ISSN (Electronic)2367-1718


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