Abstract
Technological advancement provides an unprecedented amount of high-frequency data of human dynamic processes. In this paper, we introduce an approach for characterizing qualitative between and within-subject variability from quantitative changes in the multi-subject time-series data. We present the statistical model and examine the strengths and limitations of the approach in potential applications using Monte Carlo simulations. We illustrate its usage in characterizing clusters of dynamics with phase transitions with real-time hand movement data collected on an embodied learning platform designed to foster mathematical learning.
Highlights
Human dynamic processes vary within a subject over time and differ between subjects at all behavioral, physiological, emotional, attentional, and cognitive levels (Molenaar et al, 2003)
Bayesian Information Criterion (BIC) could be useful for model selection under certain conditions, the smallest BIC did not always indicate the true model in simulations
Advancements in real-time data capture technology revolutionized the type and amount of data we collect about human dynamic processes
Summary
Human dynamic processes vary within a subject over time and differ between subjects at all behavioral, physiological, emotional, attentional, and cognitive levels (Molenaar et al, 2003). Widespread examples include but not limited to change processes in belief and attitudes (van der Maas et al, 2003; Jansen et al, 2007), affective experiences (Cole et al, 2004; Kuppens et al, 2010; Hamaker et al, 2015), and executive functions (Zelazo, 2016). The within- and betweensubject variabilities can be quantitative as well as qualitative in nature (Pintrich, 1988; Van Geert, 1991; van der Maas and Molenaar, 1992; van Dijk and van Geert, 2007; Stephen et al, 2009). In order to understand the essence and drivers of human processes, researchers argue for a need to focus on studying and interpreting qualitative variability (Kelso, 2000). We infer qualitative changes and differences from data using quantitative methods that bring objectivity and computational accuracy and efficiency
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