The attention-guiding effect and cognitive load in the comprehension of animations

Abstract

To be effective, instructional animations should avoid causing high extraneous cognitive load imposed by the high attentional requirements of selecting and processing relevant elements. In accordance with the attention-guiding principle (Bétrancourt, 2005), a study was carried out concerning the impact of cueing on cognitive load and comprehension of animations which depicted a dynamic process in a neurobiology domain. Cueing consisted of zooming in important information at each step of the process. Thirty-six undergraduate psychology students were exposed to an animation three times. Half of the participants received an animation without cueing while the other half received the same animation with cueing. Measures of cognitive load and comprehension performance (questions on isolated elements and on high-element interactivity material) were administered twice, after one and three exposures to the animation. The analyses revealed two main results. First, extraneous cognitive load was reduced by cueing after three exposures. Second, retention of the isolated elements was improved in both animation groups, whereas comprehension of high-element interactive material (i.e., the causal relations between elements) increased only in the cueing condition. Furthermore, a problem solving task showed that cueing supported the development of a more elaborate mental model.

Introduction

Animations take part in multimedia instructions to teach dynamic systems. In spite of the dynamic information conveyed by animations, these instructional devices still fail to be systematically efficient for learning as compared to static graphics (Bétrancourt, 2005, Höffler and Leutner, 2007, Tversky et al., 2002). Designing effective animations for learning requires more investigations on the cognitive processing of animations and on the difficulties experienced by learners. Although animations may reduce the cognitive cost of mental simulation of a dynamic system (Kühl, Scheiter, Gerjets, & Edelmann, 2011), they also require perceptual and cognitive resources to process their spatial and temporal aspects (Bétrancourt, 2005), and thereby, might hamper comprehension and learning processes such as selecting, organizing, and integrating relevant information into existing knowledge (De Koning, Tabbers, Rikers, & Paas, 2009).

The attention-guiding principle (Ayres and Paas, 2007, Bétrancourt, 2005), like signalling key information consists in directing learners’ attention to specific parts of the learning material in order to support learning. Using Cognitive Load Theory (CLT, Sweller, 2003) as theoretical background, the present study investigates how attentional guidance (i.e., cueing) may limit the attentional requirements for processing of animations and supports higher comprehension.

To reach an effective comprehension of animations, perceptual and cognitive processing may be highly demanding for learners. Processing dynamic information of animations implies remembering the various steps and their relations (Bétrancourt, Dillenbourg, & Clavien, 2008) and the transient nature of information may cause difficulties involving split attention over different elements (De Koning et al., 2009). Therefore, during an animated presentation, learners need to coordinate spatial and temporal aspects of their visual exploration of the relevant contents (Boucheix & Lowe, 2010). To do so, attentional processes are crucial. The problem is that learners’ attention may be distracted from the relevant information (Hillstrom & Chai, 2006) by non-topic elements of animation like seductive details or salient elements (Höffler and Leutner, 2007, Lowe, 2003) or by irrelevant movements in the animation (Lowe, 1999).

In order to avoid burdening the capacity of working memory unnecessarily, the animation features have to decrease the attentional requirements. CLT offers a relevant conceptual framework to describe and study the processing costs imposed by animations. Searching and extracting relevant elements may be viewed as an additional task (De Koning, Tabbers, Rikers, & Paas, 2007). Deep learning occurs only if sufficient cognitive resources are allocated to germane cognitive load (Sweller, Van Merriënboer, & Paas, 1998). Therefore, to be effective, animations for learning should avoid a high extraneous cognitive load imposed by high attentional requirements due to selecting relevant elements and processing their relations.

“When applied to animations, cueing can be defined as the addition of non-content information that captures attention to those aspects that are important in an animation...” (De Koning et al., 2007, p. 733). As well as for static illustrations with (spoken) narrations (e.g., Craig et al., 2002, Jamet et al., 2008, Tabbers et al., 2004), evidence of positive effects of cueing was obtained recently for animations. De Koning et al. (2007) confirmed that attention cueing for animations supports comprehension and transfer performance. Boucheix and Lowe (2010) also emphasized that cueing supports the construction of a mental model of causal chains. Examination of eye movements during learning confirmed that cueing (vs. no cueing) directed learners’ attention within animations (Boucheix & Lowe, 2010). De Koning, Tabbers, Rikers, and Paas (2010) also observed that cueing facilitates learners to look more often and for longer periods of time at cued rather than at non-cued contents. However, cueing does not improve learning performance in a systematic way (e.g., De Koning, Tabbers, Rikers & Paas, 2011; Moreno, 2007). Kriz and Hegarty (2007) as well as De Koning et al. (2010) failed to prove an effect of cueing on learning performance, yet eye movement recordings indicated that cueing effectively guided attention to the signalled region.

Furthermore, cueing is expected to reduce extraneous cognitive load associated with locating relevant information (De Koning et al., 2009). Unfortunately, only a few studies included cognitive load measures in their experimental apparatus and they did not provide clear evidence of a reduction of cognitive load due to cueing methods (De Koning et al., 2007, Moreno, 2007). Hence, more investigations of the cognitive load experienced by learners processing animations should be conducted using measurements of cognitive load throughout learning.

The aim of the present study was to determine whether cueing leads to both better comprehension performance and lower cognitive load. According to CLT, it was hypothesized that helping learners focus attention to relevant information at the right time would reduce extraneous cognitive load caused by attentional requirements, and thereby, enhance their comprehension of a causal dynamic system (elaboration of a functional mental model). Such an effect was expected to occur only for complex information, i.e., information with the high-element interactivity because its processing is assumed to require more working memory resources than the processing of simple information that contains isolated elements. So, extraneous cognitive load imposed by an animation without cueing should only interfere with comprehension processes for complex information. In other words, an element interactivity effect was expected (Sweller et al., 1998, Hasler et al., 2007).