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. https://doi.org/10.3102.1076998617719727) and we evaluate and compare these models using several goodness-of-fit indices.
|Original language||English (US)|
|Title of host publication||Handbook of Diagnostic Classification Models|
|Subtitle of host publication||Models and Model Extensions, Applications, Software Packages|
|Editors||Matthias von Davier, Young-Sun Lee|
|Publisher||Springer Nature Switzerland AG|
|State||Published - Oct 12 2019|
|Name||Methodology of Educational Measurement and Assessment|