Jianyong Wang, Gui-Shuang Ying, Xiaojin Fu, Ronghua Zhang, Jia Meng, Fang Gu, Juanjuan Li
Prevalence of myopia and vision impairment in school students in Eastern China
BMC ophthalmology
response process data computerbased problemsolving items describe respondents problemsolving processes sequences actions data provide valuable source understanding respondents problemsolving behaviors recently datadriven feature extraction methods developed compress information unstructured process data relatively lowdimensional features although extracted features used covariates regression models understand respondents response behaviors results often easy interpret since relationship extracted features original response process often explicitly defined paper propose statistical model describing response processes vary across respondents proposed model assumes response process follows hidden markov model given respondents latent traits structure hidden markov models resembles problemsolving processes hidden states interpreted problemsolving subtasks stages incorporating latent traits hidden markov models enables us characterize heterogeneity response processes across respondents parsimonious interpretable way demonstrate performance proposed model simulation experiments case studies pisa process data