Attention cueing in an instructional animation: The role of presentation speed

Abstract

Research has shown that guiding learners’ attention in animations by cueing does not necessarily improve conceptual understanding. This study investigated whether the number of elements that are presented per unit of time influences the effectiveness of cueing by showing a cued or an uncued animation about the cardiovascular system at a high or at a low speed. It was hypothesized that cueing would be most helpful for learning when the animation was shown at a high rather than at a low speed. Unexpectedly, students showed equal performances on comprehension and transfer tests irrespective of cueing and the animation’s speed. However, the low speed groups invested more mental effort to obtain this performance than the high speed groups. The findings and their implications for the design of animations are discussed in terms of cognitive load theory.

Introduction

Dynamic visualizations such as animations have become popular means for providing instruction (Ayres & Paas, 2007). Animations can explicitly show object movements and therefore seem best suited to convey dynamic events (Tversky, Morrison, & Bétrancourt, 2002). However, research on the effectiveness of dynamic and static visualizations for teaching change-related information is somewhat unclear. Whereas Höffler and Leutner (2007) showed that animations can be superior to statics; Tversky et al., (2002) have shown that learners do not construct more accurate mental representations of dynamic systems from animations than from statics.

According to Tversky et al., (2002), animations may impede understanding, because the presented information cannot be accurately perceived and understood (cf. the apprehension principle). Cognitive Load Theory (CLT; Paas, Renkl, & Sweller, 2003) argues that due to working memory (WM) limitations learners may be unable to deal with the high information load of animations, which might hamper understanding. The simultaneous presentation of multiple changes requires inexperienced learners to spatially and temporally split their attention over the animation to search for its crucial parts and relations. Deciding which parts contain relevant information and thus deserve attention may keep learners from engaging in learning-related activities thereby causing high extraneous load (Paas et al., 2003). This may especially occur if an animation’s most conspicuous aspects do not necessarily represent the most relevant information (Lowe, 1999). Therefore, understanding animations with many (irrelevant) salient details might require so much cognitive resources that little remains for processing the actual subject-matter (Ayres & Paas, 2007). Consequently, extracting the information required for building a satisfactory dynamic mental representation may fail.

Recent suggestions for improving learning from animations focus on using cues (e.g., arrows) that explicitly direct learners’ attention to relevant aspects of animations without adding extra information (Mayer & Moreno, 2003). Emphasizing an animation’s crucial parts minimizes learners’ engagement with demanding visual search processes (i.e., extraneous load) to locate task-relevant information. Thereby, WM resources that can be used for activities that help learners understand the system’s underlying relations (i.e., germane load) are supposed to be increased.

Recent studies investigating the effectiveness of cueing in animations have shown that attention-directing cues effectively (re)direct learners’ attention towards specific elements (De Koning et al., 2010, Kriz and Hegarty, 2007). However, increased attention for cued locations does not necessarily coincide with better conceptual understanding of the animation (for an overview, see De Koning, Tabbers, Rikers, & Paas, 2009). Whereas some studies have shown improved retention and transfer performance with cued animations (De Koning, Tabbers, Rikers, & Paas, 2007), other studies have failed to find better learning outcomes for cued compared to uncued animations (Kriz & Hegarty, 2007).

One possible explanation for not finding animation cueing-effects on learning is that previously used animations represented no or few simultaneous changes and did not impose considerable attentional processing demands to find relevant information and/or to understand the animation. So, to cope with the animations’ complexity not much mental effort was required and therefore animations did not need extra guidance of cues to direct attention and reduce extraneous load (cf. Mautone & Mayer, 2001). This is consistent with studies on cueing in static representations, showing that cues are most beneficial for learning when texts and/or visualizations have high complexity (Jeung et al., 1997, Lorch and Lorch, 1996). This suggests that cueing may be especially effective for improving animation-based learning if animations have high complexity. One factor that might influence an animation’s’ complexity may lie in the animations’ presentation speed.

Recent studies investigating the effects of an animation’s presentation speed on learning have indicated that altered speeds may improve understanding (Fischer et al., 2008, Meyer et al., 2010). These studies manipulated speed to make micro and macro information in animations available that otherwise could not be perceived. In the present study, however, the animation did not have such micro–macro structures. Rather, presentation speed was varied by manipulating the number of already perceivable elements that should be processed per unit of time and we investigated whether this influences the effectiveness of cueing.

Animations can show change over time at different speed levels, which enables researchers to manipulate the amount of information that should be processed per unit of time and thereby the load that is imposed on learners’ perceptual and cognitive resources, without removing or adding elements from the content, by increasing or decreasing an animation’s speed. Decreasing presentation speed might make it easier for learners to extract relevant parts and reduces the possibility of only partially processing or missing relevant parts because learners have more WM resources available for exploring the animation due to more time and less visual search requirements. Then, a low speed may allow learners to construct a mental representation of local parts, which then can be integrated into a coherent mental model (Meyer et al., 2010). In contrast, increasing presentation speed may force learners to quickly and repeatedly decide which information requires intentional processing as the same amount of information is presented in less time. Consequently, learners may miss or partially process information due to high visual search and limited time to relate and integrate current with previous information in order to comprehend the animation, thereby increasing WM demands (Ayres & Paas, 2007).

In this study, participants studied a cued or an uncued animation about the cardiovascular system at a high or a low speed. In the cueing-conditions, one subsystem of the cardiovascular system was cued. It was argued that the high speed animation (i.e., many elements per unit of time), would require cueing to facilitate the identification of task-relevant elements (Kriz & Hegarty, 2007). Minimizing the necessity to quickly search for relevant information increases the possibility that learners use their WM resources for trying to understand the elements and their relations. However, in the low speed animation (i.e., few elements per unit of time), the speed is better aligned with learners’ limited processing capacities and they may thus have more time and hence WM resources for trying to understand the content and cues may therefore not be needed. Consequently, we predicted a significant interaction between the animation’s speed and cueing, indicating that learners studying the high speed animation would benefit from cueing as indicated by higher performance on comprehension and transfer tests, whereas learners studying the low speed animation would not profit from cues and would not enhance their performance on these learning tests. Involvement of cognitive load was investigated by looking at invested mental effort.