Data are an essential factor in the fourth industrial revolution, demanding engineers and scientists to leverage and analyze their potential for significantly improving the efficiency of industrial processes and their control systems. In classical industrial process control systems, the models are constructed using linear data-driven approaches,…

The recently proposed "continuous-time latent curve model with structured residuals" (CT-LCM-SR) addresses several challenges associated with longitudinal data analysis in the behavioral sciences. First, it provides information about process trends and dynamics. Second, using the continuous-time framework, the CT-LCM-SR can handle…

The issue of model uncertainty has been gaining interest in education and the social sciences community over the years, and the dominant methods for handling model uncertainty are based on Bayesian inference, particularly, Bayesian model averaging. However, Bayesian model averaging assumes that the true data-generating model is within the…

The present study investigated multilevel models posited to predict student approaches to homework. Participants were 1,072 middle school students in China. Results revealed that deep and surface approaches were positively associated with performance-approach. Furthermore, deep approach to homework was associated negatively with homework cost, yet…

The present study aimed to examine how to improve the effectiveness of moral exemplar-applied interventions based on the pillars of the self-determination theory framework, autonomy, competence, and relatedness. Past research has mainly focused on the relatedness and attainability of moral exemplars for predicting motivation outcomes. The data for…