Factors that Impact on the Effectiveness of Instructional Animations

Abstract

This chapter outlines a number of factors that have impacted the research into instructional animations. It describes how the findings from comparisons between animations and statics, the major research paradigm, has found mixed results showing that animations are not always more effective than equivalent static pictures. We also describe some mounting evidence that animations seem to be particularly more suited to learning human motor skills rather than other types of knowledge and skills. However, we conclude that it is difficult to have total confidence in the research because many studies have inbuilt design biases that have not been controlled for. In addition, three learner characteristics (spatial ability, gender, and prior knowledge), which have been shown to influence the effectiveness animations, are also often ignored in the research. We discuss the transient nature of information present in animations that increases cognitive load and is a major impediment to learning. We also outline a number of compensatory strategies, such as learner interactivity and segmentation, that can support learning from animations, and describe how more general learning strategies, such as gesturing and collaboration, can be used in tandem with animations to facilitate greater learning.

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15
FACTORS THAT IMPACT ON THE
EFFECTIVENESS OF INSTRUCTIONAL
ANIMATIONS
Paul Ayres
SCHOOL OF EDUCATION, UNIVERSITY OF NEW SOUTH WALES, SYDNEY, AUSTRALIA
Juan C. Castro-Alonso
CENTER FOR ADVANCED RESEARCH IN EDUCATION (CIAE), UNIVERSIDAD DE CHILE, SANTIAGO, CHILE
Mona Wong
FACULTY OF EDUCATION, THE UNIVERSITY OF HONG KONG, HONG KONG, HONG KONG
Nadine Marcus
SCHOOL OF COMPUTER SCIENCE & ENGINEERING, UNIVERSITY OF NEW SOUTH WALES, SYDNEY, AUSTRALIA
Fred Paas
DEPARTMENT OF PSYCHOLOGY, EDUCATION, AND CHILD STUDIES, ERASMUS UNIVERSITY ROTTERDAM, ROTTERDAM,
THE NETHERLANDS; SCHOOL OF EDUCATION/EARLY START, UNIVERSITY OF WOLLONGONG, WOLLONGONG, AUSTRALIA
This chapter examines a number of significant issues associated with the design of
instructional animations. The term animation used here refers to visualisations
composed of a number of static pictures that are shown in rapid sequence. This
broad definition includes many different types of instructional materials, such as
sequential presentations, simulations, videos, and other types of dynamic
visualisations.
Despite the immense promise of animations to enhance learning, proof of their
effectiveness has lagged behind many educators’enthusiasm for using them. We
argue that the research has not only failed to find convincing evidence in support
of the wide-scale use of instructional animations, but also the research itself has in
many instances, failed to consider or control for significant moderating factors. This
chapter discusses these factors and their implications for designing effective
instructional animations. We begin our discussion by examining the research into
studies that have compared animations with static presentations. This comparison

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has been an important methodology in deciding whether moving pictures provide
any learning advantage compared to static pictures.
Animations versus static pictures research
Although many studies have shown that dynamic visualisations are an advantage
over statics, the overall evidence is far from conclusive (see Tversky, Morrison, &
Betrancourt, 2002). There are studies that show: (a) animation superior to statics
(e.g., Stebner, Kühl, Höffler, Wirth, & Ayres, 2017; Yarden & Yarden, 2010), (b)
static pictures superior to animations (e.g., Koroghlanian & Klein, 2004), and (c)
neither format superior to the other (e.g., Kühl, Scheiter, Gerjets, & Gemballa,
2011).
One example that demonstrates the mixed outcomes of this type of research can
be found in the meta-analysis of Berney and Bétrancourt (2016). Overall, the meta-
analysis showed an overall advantage of animation over statics. However, closer
examination of the data reveals that in their 140 pair-wise comparisons, 59% of the
studies failed to show significant differences between either type of visualisations,
10% showed static dominance, and only 31% favoured animations.
These research findings suggest somewhat mixed results making it difficult to
conclude that animations are the best form of presentation format for all condi-
tions. Complicating the issue even further is that the research base has been often
tainted by the inclusion of design biases, which are discussed next.
Design biases in animation research
Examinations of the design details used in studies comparing statics to animations
have shown that they have often failed to consider a number of moderating vari-
ables (Tversky et al., 2002). For example, there are biased comparisons that have
not controlled for variables such as appeal, variety, media, size, and interaction (see
Castro-Alonso, Ayres, & Paas, 2016). A typical example of appeal bias is observed
in comparisons that do not match the degree of colour included in both visualisa-
tions, and, for example, compare coloured animation to black and white statics
(e.g., Yang, Andre, Greenbowe, & Tibell, 2003). As colour influences memorisa-
tion and multimedia learning (e.g., Matthews, Benjamin, & Osborne, 2007), a
failure to control for this variable will influence learning outcomes. The variety bias
is observed when one of the compared formats presents more visual elements than
the other, for example, the static pictures include signalling arrows, which are
lacking in the animated design (e.g., Lewalter, 2003). Any extra quantity of visual
elements can generate advantageous cueing or signalling effects (e.g., Xie et al., 2017)
or unfavourable redundancy effects (see Kalyuga & Sweller, 2014). The media bias is
observed when the comparison is not made in the same medium, such as paper
static images compared to computer animations (e.g., Marbach-Ad, Rotbain, &
Stavy, 2008). Variations in media can lead to different learning effects (e.g., Salo-
mon, 1984). An example of size bias is produced when a larger animation is
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compared to the smaller images of statics (e.g., Ng, Kalyuga, & Sweller, 2013),
most likely hindering the effectiveness of the static pictures. Last, the interaction
bias includes comparisons where the animated visualisation includes interactive
buttons, for example to pause or fast forward the content, whereas these features
are not included in the static format (e.g., Watson, Butterfield, Curran, & Craig,
2010). As the inclusion of interaction can help multimedia learning (e.g., Evans &
Gibbons, 2007), such comparisons are biased in favour of the animated format.
Hence, studies that investigate animation-static comparisons should ensure that
such moderating variables are controlled for. Furthermore, there are a number of
learners’individual characteristics that may also influence these studies, such as
spatial ability, gender, and prior knowledge.
Impact of individual characteristics
Spatial ability
Spatial ability is considered an important skill in extracting and understanding
visual information when learning from animations and static pictures (see,
Hegarty, Kriz, & Cate, 2003; Hegarty, Montello, Richardson, Ishikawa, &
Lovelace, 2006; Hegarty & Sims, 1994; Hegarty & Waller, 2005; Narayanan &
Hegarty, 2002). The meta-analysis of Höffler (2010) found a correlation
between spatial ability and learning from instructional animations, showing that
the effectiveness of animations can be influenced by the spatial ability of lear-
ners. Similarly, research has also found that spatial ability is highly correlated
with mental animation (see Hegarty et al., 2003; Hegarty et al., 2006). When
static pictures are used to display dynamic processes, the learner must mentally
animate the processes in order to understand the motion depicted (Hegarty et
al., 2003). In other words, motion must be inferred, and therefore learners with
low spatial ability may find it difficult to make these inferences. On the con-
trary, there is evidence showing that learners with low spatial ability may be
advantaged by learning from animations rather than statics in comparison to
learners with high spatial ability (Höffler, 2010). This finding suggests that
because learners with low spatial ability find it difficult to mentally animate
static pictures, animations reduce the amount of mental animation that needs to
be made and therefore provide an advantage. In contrast, learners with high
spatial ability have fewer problems with mentally animating statics, and therefore
show fewer pronounced differences in learning from statics and animation.
Another issue associated with spatial ability is that there have been a number of
ambiguous definitions and different psychometric tests used to obtain a general
measure of spatial ability (for details, see Wong, Castro-Alonso, Ayres, & Paas,
2018). As also outlined by Castro-Alonso, Ayres, Wong, and Paas (Chapter 8, this
book), if spatial ability is measured, often there is a disparity between the actual test
used and the content to-be-learned, for example, a paper folding task may have
little in common with learning science concepts.
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It can be concluded that if spatial ability is an important skill in learning from
animations (and statics), then it is important to measure it using the appropriate
tests.
Gender effects
Spatial ability research generally suggests that females have a lower spatial ability (in
particular, mental rotation ability) than males (e.g. Halpern, 2011; Maeda & Yoon,
2013).There is also evidence that instructional animations can support females more
than males (e.g., Falvo & Suits, 2009; Sánchez & Wiley, 2010; Wong, Castro-
Alonso, Ayres, & Paas, 2015). Consequently, it has been argued that any advantage
for females from instructional animations is due to their lower spatial ability (e.g.
Sánchez & Wiley, 2010; Yezierski & Birk, 2006), as animations generally benefit
learners with low spatial ability (Höffler, 2010) rather than static pictures.
Similar to the treatment of spatial ability, gender is often not reported leading to
potential biases with treatment sampling. More females in one group may produce
adifferent result to more males. There is a lack of rigorous investigation linking the
effectiveness of animations with gender and spatial ability measures, despite research
showing that gender may have a significant impact on the effectiveness of
instructional animations (see Wong et al., 2018).
Prior knowledge
A major finding of cognitive load theory is that expertise can influence the effec-
tiveness of learning strategies (Kalyuga, Ayres, Chandler, & Sweller, 2003). Strate-
gies that are helpful for novices may be detrimental to those with greater
knowledge. Animated environments are no exception in demonstrating this
expertise reversal effect. A study by Spanjers, Wouters, van Gog, and van Mer-
riënboer (2011), demonstrated an effect using a segmentation strategy. One
method of improving the effectiveness of animations is to segment the presentation
into smaller parcels of information. However, Spanjers et al. (2011) found that the
segmentation strategy was only effective for learners with low domain-specific
knowledge compared to learners with greater knowledge. Arguably, learners with
more prior-knowledge are able to deal with more information at a time, due to
expertise information chunking advantages (see Sweller, Ayres & Kalyuga, 2011).
Furthermore, Kalyuga (2008) also showed that greater domain-specific knowledge
could reduce the negative effects of transient information, which is discussed next.
The transient information effect
As described above there are number of factors that have influenced the research
into animation-static studies. By not controlling for these factors the research base
has been tainted to some degree, and it is difficult to form a conclusion that ani-
mations are superior. In addition, there are theoretical grounds suggesting that
Factors impacting instructional animations 183

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statics may be superior to animations. Animations, when used as instructional
resources share a common feature: they convey transient visual information. This
transiency feature is present in animations but not in their constituent static pic-
tures. Ayres and Paas (2007) observed that because animations are dynamic con-
sisting of a series of frames, which roll from one to another, visual information
often disappears from sight. If information from previous frames is needed to
understand later frames, the learner has to remember previous information and
mentally integrate it with newly presented information. This processing requires
additional working memory resources and from a cognitive load theory perspective
has negative effects on learning. In contrast, static presentations are more perma-
nent, generating less transient information, which allows more working memory
processes to be allotted to learning.
The transient information effect occurs when non-transient information leads to
higher learning than the same information presented in a transient form (Castro-
Alonso, Ayres, Wong, & Paas, 2018). This effect has been demonstrated in ani-
mation studies involving mechanical systems (Mayer, Hegarty, Mayer, & Campbell,
2005) and symbol memorisation tasks (Castro-Alonso, Ayres, & Paas, 2014) where
statics have been found to be superior to animations. The effect has also been
found with spoken narration, which is a more fundamental form of transient
information (see Singh, Marcus, & Ayres, 2017; Wong, Leahy, Marcus & Sweller,
2012).
Animations and learning human movement skills
Considerations of transient information suggest that animations may not create the
optimum learning environment, although levels of transiency are an important
factor in deciding the extent to which they inhibit learning (Leahy & Sweller,
2011). Nevertheless, there is some clear evidence that animations are conducive for
promoting learning for a particular class of tasks. The meta-analysis of Höffler and
Leutner (2007) identified a number of conditions under which animations were
more effective than equivalent statics. In particular, the largest effect size was found
when the animations featured procedural-motor knowledge. Our own research has
since supported this conclusion, and has shown that when learning cognitive tasks
involving human motor skills, animations are an advantage. For example, we have
found that animations are superior to statics in learning to tie knots (Marcus,
Cleary, Wong, & Ayres, 2013), build Lego shapes (Castro-Alonso, Ayres, & Paas,
2015a), and make origami shapes (Ayres, Marcus, Chan, & Qian, 2009; Wong et
al., 2009). Other researchers have also found similar advantages, for example when
learning surgical skills (Masters, Lo, Maxwell, & Patil, 2008).
The prediction that transient information reduces the impact of animations
and the findings that animations are very helpful for learning human movement
tasks creates an obvious dichotomy: How can both be true? Paas and Sweller
(2012) suggest that humans have evolved to learn human movement skills more
easily than other types of skills (see also van Gog, Paas, Marcus, Ayres, &
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Sweller, 2009). Based on the work of Geary (2008) it can be argued that
human movement is a form of biologically primary knowledge that requires
little conscious processing of information. As a result, when learning about
human movement from an animation, working memory may be less affected by
transient information. In other words, learning about human movement might
be a special case, where humans have evolutionary advantages that are not
afforded to other types of learning topics.
Methods to improve the effectiveness of animations
Multimedia principles
Badly designed animations reduce their potential significantly, regardless of any
inherent difficulties associated with them. Features like how and when text is
applied is critical. The meta-analysis of Berney and Bétrancourt (2016) found sig-
nificant advantages when spoken explanatory text was added to animations. This
finding is consistent with the modality effect where spoken text and pictures gen-
erate higher learning outcomes than written text and pictures (Low & Sweller,
2014). Adding text to pictorial information creates a multimedia learning environ-
ment (see Mayer, 2014), where there are a number of guiding principles that
should be followed based on cognitive load theory (Ayres, 2015). For example,
these principles include: a) synchronising the spoken text with the relevant pictures
to avoid split-attention effects (Ayres & Sweller, 2014); b) ensuring that the spoken
text and pictures do not convey the same information leading to redundancy
(Kalyuga & Sweller, 2014); and c) avoiding lengthy spoken text that can cause the
transient information effect (Leahy & Sweller, 2011).
Compensatory strategies
Using best-practice multimedia principles in the design of instructional animations
ensures that many potential negative effects can be avoided. However, they do not
guarantee that animated transitory effects will be reduced. To deal with transitory
information a number of compensatory strategies have been employed. Animations
can be paused either through learner control or system control (Mayer & Chandler,
2001), or segmented into smaller sections (Spanjers et al., 2011; Wong et al., 2012).
Both interventions (pausing or segmenting) deal with the transient information by
reducing the amount of information that the learner must cope with at a given
time. That reduction can ameliorate the negative effects of transience when using
animated instructional presentations. Whereas these compensatory strategies are
helpful, they also have some disadvantages. Segmentation can make it more diffi-
cult to integrate knowledge across segments (see Singh et al., 2017) and learner
interactivity can be a burden for novice learners who do not have the expertise to
know when to stop and start animations at key points in the presentations (Hasler,
Kersten, & Sweller, 2007).
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Using general learning strategies
Much of the research into instructional visual representations has tended to
focus on ways to improve the presentation formats, by for example, adding
text. There has been less emphasis on combining multimedia formats with other
learning strategies. Two notable exceptions have been the use of worked
examples (see Renkl, 2014) and self-explanations (see Wylie & Chi, 2014),
where favourable results have been found when these strategies have been used
in multimedia settings. In contrast, little research of this type has been con-
ducted with animations, especially in regard to the transitory information issue.
However, one promising new research direction has been to examine the
impact of gesturing used in conjunction with animations. Gesturing, which is
described next, can be considered a general learning strategy as it can be
applied in many learning environments. We finish this section by suggesting
how a second general learning strategy, collaboration, can also be used in
tandem with instructional animations.
Gesturing
Gesturing has been shown to enhance learning, either in the case of learners
who express information in gesture (Cook, Mitchell, & Goldin-Meadow, 2008),
or learners who observe instructors expressing information by gesture (Cook &
Tannenhaus, 2009). Studies have found gesturing advantages across a number of
learning disciplines such as science (Agostinho et al., 2015; Castro-Alonso,
Ayres, & Paas, 2015b), and second language acquisition (Lajevardi, Narang,
Marcus, & Ayres, 2017; Mavilidi, Okely, Chandler, Cliff, & Paas, 2015). These
findings support the embodied cognition view (see Barsalou, 2008; Glenberg,
1997) that observing making movements (i.e. gestures) leads to richer encoding
and therefore richer cognitive representations, that allow students to perform
faster and more accurately on tasks. Direct evidence that gesturing can lower
cognitive load, was found by Ping and Goldin-Meadow (2010) with mathematics
tasks.
The evidence suggests that gesturing can improve learning and reduce cog-
nitive load, which is often referred to as cognitive offloading, where the use of
physical actions generate cognitive savings (Risko & Gilbert, 2016). From the
perspective of learning from animations, there are potential benefits for includ-
ing gesturing in animation environments where cognitive load may be high due
to transient effects. By reducing cognitive load gesturing is automatically/
effortlessly integrated into cognitive schemata, thereby enriching the schemata
(embodied cognition), and at the same time helping to reduce cognitive load
generated by the instructional format. As some evidence exists that gesturing
can be combined effectively with viewing videos as an example of animations
(e.g. Lajevardi et al., 2017), gesturing may alleviate the difficulties posed by the
transient information of animations.
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Collaboration
Collaborative learning is widely used and has significant academic, social and psy-
chological benefits (see Johnson, Johnson, & Smith, 1998). Explanations for this
advantage are usually grounded in social constructivist theory or social indepen-
dence theory (Johnson & Johnson, 1994; Schreiber & Valle, 2013); however, some
fresh insights can be gained by considerations of cognitive load theory. From this
theoretical perspective collaborative learning uses the borrowing and re-organising
principle (see Paas and Sweller, 2012). Learners, with a gap in their knowledge can
fill that gap from knowledge provided by other members of the group (borrowed)
if such group members have that knowledge (Khawaja, Chen, & Marcus, 2012). A
second advantage of collaboration is that it can help share working memory load
by having different members of a group contribute knowledge particular to them
but not otherwise available to other members of the group. F. Kirschner, Paas, &
P. A. Kirschner (2009) have suggested that collaboration generates an expanded
processing capacity with reduced collective cognitive load compared to individuals.
Instead of one working memory dealing with the load, several working memories
work together and share the load (i.e., collective working memory effect, F.
Kirschner, Paas, & P. A. Kirschner & Janssen, 2011). Further findings from colla-
borative memory research suggest that individuals learn from listening to others
recall information (Blumen & Rajaram, 2008) and are able to rehearse known
information recalled by others (Rajaram & Periera-Pasarin, 2010).
As far as we know little research has been conducted into using collaboration to
learn more effectively from animations, as most research focuses on using anima-
tion to support collaboration. Computer-based facilities are used to enhance colla-
boration in what is often referred to as computer-supported collaborative learning
(Zhang, Ayres, & Chan, 2011). Significantly, the advantages detailed above, where
individuals can gain and rehearse information from others, especially key informa-
tion that was missed, can alleviate the difficulties associated with transient infor-
mation. Further being part of a group with an enhanced collective working
memory can be expected to enhance the capabilities of learning from animations.
In summary, adding gesturing and collaboration to animations provide the
capacity to not only deal with transient information, but they are also powerful
learning strategies themselves. These two strategies are expected to enhance ani-
mations. Future research could also identify other general strategies that have
similar positive effects, such as imagination or visualisation techniques (see Cooper,
Tindall-Ford, Chandler, & Sweller, 2001).
Conclusions
This chapter has outlined a number of factors that have impacted on the research
into instructional animations. Whereas many research findings indicate that ani-
mations can be more effective than equivalent static pictures, there are examples
where there is no difference, or in some cases static pictures generate higher
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learning outcomes. As some of the studies have included design biases, we believe
that the findings of the literature base must be treated with some caution.
Variables that have an impact on instructional presentations such as appeal,
variety, media, size, and interaction, have not been consistently controlled for.
Furthermore, three individual characteristics of learners (spatial ability, gender,
and prior knowledge) have been shown to influence the effectiveness of
dynamic representations, yet many studies have not considered these factors.
Therefore, important interactions between them, as well as with the learning
materials, have been missed. Learning topics have also been found to be an
important factor. Mounting evidence suggests that animations seem to be par-
ticularly more suited to learning cognitive tasks containing human motor skills
rather than other types of knowledge and skills. However, because of the noted
issues associated with the research base, this conclusion is far from being established as
a fact.
A major impediment to learning from animations is the transient nature of the
information presented. Such information can tax working memory resources and
reduce learning. To prevent this situation a number of compensatory strategies are
available such as learner interactivity and segmentation. In addition, because of the
multimedia nature of animations, there are a number of multimedia principles that
can be followed to ensure best-practice animations. There is also the potential to
use more general learning strategies such as gesturing and collaboration, to ease the
cognitive load and facilitate learning further.
Implications for education
A number of implications can be identified from the research outlined in this
chapter, relevant to teachers, instructional designers, and researchers. Teachers have
to be careful in choosing their animations. Consistent with most teaching and
learning paradigms an appropriate match has to be found between the learner and
the learning content. Animations, as previously noted, generate some unique con-
ditions. In some cases, an animation may not be the best choice, and static pictures
should be chosen. Regardless, animations (and static pictures), should be chosen
that have been developed with sound multimedia principles in place. In cases
where transient animations must be used because of Government and School
policies or other external factors, gesturing and collaboration strategies could be
considered. Failure to adapt to such animations could decrease the chances of
learning.
An important issue for instructional designers is to understand that animations that
contain highly transitory information can reduce their effectiveness. Hence, where
possible, animations should be constructed that prevent this type of extraneous
cognitive load. If unavoidable, options must be available to provide learner-inter-
activity and/or segmentation facilities. Other intrinsic methods to reduce transitory
effects should be considered, as well as following multimedia principles in order to
construct high quality animations.
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Regarding researchers, considerable research is required in future to fully under-
stand the conditions under which animations are most helpful. There is also the
scope to investigate the combination of animations with other general learning
strategies such as gesturing and general collaboration. It is essential that such
research is free of design biases and includes the many factors that can influence the
effectiveness of learning from animations.
Of interest to all stakeholders is that it may be important to determine students’
spatial ability and prior knowledge and adapt the animations to this. It is important
for all to realise that animations are just a sequences of statics. The speed at which
the sequence is shown could be slowed down to make the information less tran-
sient for low spatial ability and/or low prior knowledge students, and speed up for
high spatial ability and/or high prior knowledge students. In addition, students
could be given control of the pacing to self-manage their cognitive load. Further
research is required to provide specific guidelines between the relationship between
spatial ability, prior knowledge and learning from animations.
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