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With respect to the operation of mechanical systems, small components with notable ranges of movement might easily capture the learners’ attention, while large but motionless components might be missed. There is misalignment between visual conspicuity, moving range and physical size of components with respect to the operation of a complex mechanical system (Boucheix & Lowe, 2010; Boucheix et al., 2013; Lowe & Boucheix, 2011). The addition of visuospatial cues on a dynamic mechanical system is intended to improve the misalignment between perceptibility and thematic essence by directing learners to perceive thematically relevant components (Boucheix et al., 2013; Boucheix & Lowe, 2010; Lowe & Boucheix, 2011). However, if the dynamics of a component are too distracting, it will suppress the visuospatial cues for learners’ attention. To make visuospatial cues strong enough to counteract the perceptual contrast posed by dynamic components, the perceptual contrast of visuospatial cues has to be reinforced by dynamic contrast (Boucheix et al., 2013). Visuospatial cues presented in a snowballing manner (e.g., A, A + B, A + B + C, etc.) by repeatedly and incrementally connecting each discrete component into a relation set might correspond with learners’ repetitive and cumulative internal processing proposed by the animation processing model (Lowe & Boucheix, 2008) wherein learners deconstruct dynamic visualizations into segments, then associate segments into micro chunks, and eventually organize micro chunks into a coherent configuration which entails causal chains in mechanical operation (Boucheix et al., 2013). Snowballing cues, presented in synchrony with the progress of dynamic visualizations by incrementally and iteratively associating each individual component into a relation set, are expected to influence learners’ attention to be uniformly distributed to each component, solve the incongruence between perceptibility and thematic relevance, and reduce the possibility of visuospatial cues being visually suppressed by dynamic components (e.g., Yang, 2020, 2021).
Dynamic visualizations composed of a series of frames allow learners to process transient information within a limited time. The information stream of dynamic visualizations presented quickly might vanish before it is completely processed. While learners process current information, upcoming information appears, and learners must keep current information active in their working memory while simultaneously integrating upcoming information with current information, which might impose a cognitive load on learners (de Koning, Marcus, Brucker & Ayres, 2019; Meyer, Rasch & Schnotz, 2010; Spanjers, van Gog & van Merriënboer, 2010). It is assumed that visuospatial cues in static visualizations might yield stronger effects than those in dynamic visualizations (Boucheix et al., 2013). However, snowballing cues presented in synchrony with the progress of dynamic visualizations might counteract the perceptual contrast imposed by dynamic components and yield a more favorable mental model (e.g., Yang, 2020, 2021). The present study was aimed at administering snowballing cues in dynamic visualizations and exploring whether it might yield stronger effects than their counterparts in static visualizations.