annotated bibliography

RESEARCH ARTICLE

Designing computer-based learning contents: influence of digital zoom on attention

Manuela Glaser1 • Dominik Lengyel2,3 • Catherine Toulouse2,3 •

Stephan Schwan1

Published online: 27 October 2016 � Association for Educational Communications and Technology 2016

Abstract In the present study, we investigated the role of digital zoom as a tool for directing attention while looking at visual learning material. In particular, we analyzed

whether minimal digital zoom functions similarly to a rhetorical device by cueing mental

zooming of attention accordingly. Participants were presented either static film clips, film

clips with minimal zoom-ins, or film clips with minimal zoom-outs while eye movements

were recorded. We hypothesized that minimal zoom-ins should lead to more gaze

coherence, to longer dwell times as an indicator of more elaborative processing, and to

fewer transitions as an indicator of less mental integration. Zoom-outs, on the other hand,

were expected to have opposite effects. Results showed that zoom-ins increase gaze

coherence and dwell times on the center parts of the depictions while decreasing transitions

of pictorial elements from the center and the context areas. In contrast, patterns of results

from zoom-outs and static presentations were similar to a large degree, indicating that

zoom-ins and zoom-outs do not operate in a complementary fashion. Theoretical and

practical implications of the present results are discussed.

Keywords Zoom � Camera � Attention � Eye-tracking � Cueing � Gaze coherence

& Manuela Glaser [email protected]

Dominik Lengyel [email protected]

Catherine Toulouse [email protected]

Stephan Schwan [email protected]

1 Leibniz-Institut für Wissensmedien, Schleichstr. 6, 72076 Tuebingen, Germany

2 Brandenburgische Technische Universität Cottbus-Senftenberg, Postfach 101344, 03013 Cottbus, Germany

3 Brandenburgische Technische Universität Cottbus-Senftenberg, Konrad-Wachsmann-Allee 8, 03046 Cottbus, Germany

123

Education Tech Research Dev (2017) 65:1135–1151 DOI 10.1007/s11423-016-9495-9

Introduction

How can cinematographic techniques be used for making educational videos and films

more intelligible and their content easier to comprehend and to learn? While professional

cameramen and film directors routinely use a broad range of cinematographic rules and

techniques such as staging and lighting, camera movements and framing, as well as

principles of film montage, surprisingly little empirical research has been conducted on the

effects of these devices on various processing stages during learning and comprehension.

In the present paper, we focus on one such widely used film technique—digital zoom—

reporting the findings of an empirical study that investigated the effects of zoom-ins and

zoom-outs on viewers’ processes of attention while looking at depictions of archaeological

reconstructions.

In general, there is growing empirical evidence that cinematographic techniques can be

used for shaping viewers’ processing of moving pictures (Bordwell 1985). Cutting et al.

(2011a, 2011b) examined Hollywood movies from 1935 to today, showing that over the

years, shots have become shorter, motion and movement in the films have increased

linearly, and films have become darker. As a single determinant of all these different

changes, Cutting et al. (2011a, 2011b) suggest that filmmakers have tried more and more to

direct the viewers’ attention to specific details, thereby influencing subsequent steps of

cognitive processing as well. Taking up these findings, Smith (2012) postulates in his

attentional theory of cinematic continuity that continuity editing rules provide advice on

how the attention of the viewers can be directed to audiovisual details and across otherwise

discontinuous and distracting cuts. Different types of attentional cues thereby establish

different kinds of expectations. If these expectations are met within a few frames after a

cut, viewers’ attention shifts to the target object. Hasson et al. (2008) provide evidence for

this shaping of cognitive processes. They compared inter-subject-correlations of functional

magnetic resonance imaging (fMRI) data of adult participants watching a Hollywood

movie with those of adult participants watching an unedited one-shot real life film. The

results showed that neural activity correlated highly for Hollywood movies but less for the

unedited film. Hence, film editing in Hollywood movies leads to more synchronization of

gaze behavior and brain activity than unedited one-shot films of street scenes. Dorr et al.

(2010) could show similar effects. They compared trailers of Hollywood movies with

natural movies (mostly without camera movement), stop motion movies (generated from

the natural movies), and static images (from natural movies not used in the stop motion

condition). Again, results show that eye movements on professionally produced Holly-

wood movies are significantly more coherent than those on unedited recordings of natural

scenes.

Camera techniques as cognitive tools for comprehension and learning

Professional cameramen and film directors use various camera techniques in order to

achieve a broad range of effects on viewers, such as heightening recipients’ involvement

with the characters and their actions, providing different perspectives on a scene, or

establishing spatial relations between different places (Bordwell and Thompson 2012;

Smith 2012). Also, in the field of documentaries and educational films, principles of

camera movement have been applied to static pictorial material, giving it a more ‘‘film-

like’’ appeal. In particular, the documentary filmmaker Ken Burns faced the challenge of

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making aesthetically pleasing, interesting, and entertaining historical documentary films by

use of predominantly static pictorial material. He used digital zooming (or scaling) and

panning of the movie frame across the static images in order to make these contents appear

more diversified, to elicit a feeling of motion, to keep the viewer visually engaged, to guide

recipients’ attention to particular parts of the pictures, to make connections between these

parts, to bring contents closer to the viewers, or to create a more distant feeling in the

recipients. Accordingly, this cinematographic technique has been termed the Ken Burns

Effect and has become a standard functionality in current film editing software often used

for the production of documentaries and educational videos on the basis of static visual

learning material (such as photographs, illustrations, or maps).

3D-designers of static archaeological reconstructions are also faced with similar

problems of appropriate presentation. Today, computer-based reconstructions of historical

buildings are frequently used for communicating scientific knowledge in informal learning

contexts such as history magazines (Glaser 2015), archaeological television documentaries,

and historical museums. Such reconstructions are furthermore not exclusively used in the

field of public understanding of science and humanities. Instead, in archaeological and

historical science, they are an essential part of the scientific process (Samida 2009). In both

fields, historical reconstructions are intended to show a detailed and vivid picture of the

past that corresponds to the current state of research and, at the same time, are something

into which one can become immersed (Glaser et al. 2012). Thus, they serve as easy-to-

grasp visualizations of notions and hypotheses regarding certain historical sites, providing

a basis for discussions, which is in the end necessary for comprehension and communi-

cation of the subject. Further, they actually often bring new issues to light. In order to make

the inherently static content of a reconstructed site appear more dynamic, the 3D-designers

use two different methods: (1) They either animate the contents (e.g. by showing the

construction of a building in a stepwise manner or by populating the site with virtual

characters or animals) or (2) they use a moving virtual camera, not only following the Ken

Burns technique, but also moving around (camera flights) and through (walk-throughs) the

scene. Both types of dynamic presentations serve different functions. With animated

content, dynamics represent the progression of a process in time. With an animated

camera, on the other hand, dynamics are used to progress through space, thereby depicting

an inherently static content (e.g. a building) from different viewpoints and perspectives.

Issues of animated content have been investigated intensively with regard to knowledge

acquisition and learning (Höffler and Leutner 2007; Mayer 2014; Tversky et al. 2002). In

contrast, with few exceptions (Amadieu et al. 2011; Munger et al. 2006), the influence of

cinematographic techniques on knowledge acquisition, and particularly of camera usage on

attention, has attracted far less empirical research (Schwan and Papenmeier in press).

Cueing attention by camera movements and zooms

At a closer look, cognitive shaping effects of cinematographic techniques have been

demonstrated not only for film cuts but also for camera movements and zooms. More

specifically, camera movements and zooms have been shown to (1) guide attention within a

scene, (2) modulate the amount of elaborative processes regarding individual elements of a

scene, and (3) foster mental integration of different elements of a given scene.

Regarding attentional guidance, we know that camera movement as a mechanism of

continuity editing is able to guide viewers’ attention continuously to the following shot

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(Smith 2012) and to make viewers’ gaze behavior more coherent (Dorr et al. 2010; Hasson

et al. 2008). Zoom-ins and zoom-outs in particular are often used to approach a scene

visually or retract from it. Zoom-ins and zooms-outs regulate framing, that is, the scope of

the visual field within a given scene. By taking up the notion of the film camera as a

substitute for an observer, a zoom-in can be interpreted as bringing the recipients closer to

particular contents, making it possible to see these contents in more detail and with less

distracting context. Thus, besides inducing a feeling of being more involved (Heimann

et al. 2014) and being more present (Nakanishi et al. 2011), a zoom-in may also set

attentional foci by narrowing the field of view and by excluding other potentially inter-

esting elements that might draw the viewers’ attention. Accordingly, a zoom-out may

provide a better overview of the scene with additional context. It may further induce a

more distant feeling, a more analytic perspective, and a critical attitude toward the con-

tents. Finally, it may broaden visual attention.

Regarding modulation of elaborative processes, camera zooming has some parallels

with the operational characteristics of human visual attention, which also show fluctuations

both in spatial extension and processing efficiency. Accordingly, researchers have even

named a model for attentional processes after the functioning of the camera lens: the zoom

lens model (Eriksen and James 1986). According to this model, the location and spatial

extent of a person’s attentional focus is task dependent; the processing efficiency for

stimuli within the focus decreases with the enlarged focus, and the boundary of the focus

shows a processing gradient. The zoom lens model also has physiological correlates.

Müller et al. (2003) used event-related functional magnetic resonance imagery and showed

that neural activity preceding target objects correlated with the size of the attended region,

as did subjects’ task performance. While the extent of the activated retinotopic visual

cortex increased with the size of the attended region (defined by spatial cue stimulus), the

level of neural activity in a given subregion decreased. Comparable processing fluctuations

have also been demonstrated for camera zooms. For example, Poltoratski and Tong (2014)

showed that viewers classified a zoom-out sequence (from an object view to a scene view)

as depicting an object for several seconds longer than a comparable zoom-in sequence. On

the other hand, scenes were perceived longer with zoom-ins than with zoom-outs. Amadieu

et al. (2011) examined the learning of animations of a neurobiological topic with zooming

as the cuing method compared to the learning of animations without zooming. The results

showed that extraneous load was reduced by cueing after three exposures. Further,

retention of single elements of the animation was improved in both conditions, whereas

comprehension of the relations between the elements increased only in the zooming

condition. Finally, with zooming, learners showed better outcomes in a problem solving

task than without zoom, indicating the development of a more elaborated mental model.

Regarding mental integration, Kipper (1986) compared film clips that included camera

movement with film clips using a long and close shot and found that with camera

movement, viewers scored significantly higher in the recall tests, the object placement

tests, and the recognition tests. With a moving camera, viewers understood and remem-

bered the physical properties of a scene better. Kipper (1986) concluded that filmic

depictions benefit from a moving camera when knowledge of the spatial layout of a

situation is important. Similarly, spatial relationships between the constellations of moving

objects or persons (e.g. players on a basketball field) are understood better under conditions

of camera movement than of abrupt film cuts (Garsoffky et al. 2007; Meyerhoff et al.

2013). Taken together, these studies demonstrate that camera movements may facilitate

viewers’ cognitive integration of different elements of a given scene.

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Based on the parallels between camera zoom as an external mechanism and attention as

its internal counterpart, the question about the relationship between digital and mental

zooming arises. On the one hand, a digital zoom may substitute attention processes in the

sense that the extension of the field of attention is now controlled externally instead of

mentally. This should be particularly the case for ‘‘bold’’ zooms that induce a substantial

enlargement or narrowing of what is visible from a scene. Hence, a bold zoom-in selects a

subset of elements from a given scene while excluding a substantial portion of scenic

elements which move out of the frame (and therefore out of sight). Hence, the process of

adapting the field of attention to the processing requirements is at least partly offloaded

from the mental apparatus to the film medium.

In contrast, a zoom may also function similarly to a rhetorical device that cues the

viewers on how to adapt their attention in order to appropriately process a given scene.

Thus, even a minimal, barely noticeable zoom-in that does not narrow visibility to a large

degree may nevertheless cue viewers to narrow their attention accordingly to some details

of the scene. Also, a minimal, barely noticeable zoom-out may cue viewers to enlarge their

field of attention to the overall visual characteristics of a scene. In other words, for zooms

functioning similarly to rhetorical devices, it may suffice that viewers notice that a zoom is

occurring in order to take it as a signal for adapting their attention accordingly. Evidence

for zooms as procedural primes comes from developmental studies with young children.

Salomon (1994) examined second-graders and compared a modeling condition in which

the camera moved around a scene, changing viewpoints with static images of the initial and

final camera position, with a static image of only the initial camera position, or with a

control condition without treatment. After the presentation, the children had to select

pictures that depicted somebody else’s way of seeing the scenes that they had previously

watched. The results showed that especially the children who scored low in a pretest on the

ability of changing viewpoints benefited from camera modeling. Salomon (1994) also

examined eighth-graders and compared zooming in and out on a painting with altering

static images of total views and details of paintings, with static images of only total views,

or with a control condition without treatment. After watching the presentations, the chil-

dren were asked to report the maximum number of items that they had noticed in the

pictures. The results showed that especially low scorers in a pretest benefited from

zooming compared to watching only total views of the pictures. The author concludes that

zooming may have provided a highly explicit symbolic code that could be imitated and

internalized. Salomon and Cohen (1977) compared different formal styles of film

(zooming, close-ups interchanged with long shots, logical gaps, fragmentation) with regard

to fifth graders’ knowledge acquisition of the films’ contents. They could show that, while

close-ups interchanged with long shots called for specific skills, zooming supplanted these

skills, rendering them unnecessary for the acquisition of the knowledge. Children in the

zooming condition had significantly better scores in the knowledge tests than children in

the group with close-ups interchanging with long-shots.

Guided attention and learning theories

The theory of multimedia learning (Mayer 2009, 2014) postulates different principles for

designing learning material. One basic principle is the signaling or cueing principle, which

refers to the finding that multimedia learning material becomes more effective when cues

are added that guide learner’s attention to the relevant elements of the presentation (van

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Gog 2014). Such cues may be text-based cues, picture-based cues, or cues based on the eye

movement of others. Signaling is a powerful tool for instructional designers, helping

learners select, organize, and integrate information. Hence, zooms as a particular instance

of picture-based cues are not only found in documentaries and educational films, but are

also being increasingly used in simulations and virtual reality applications (Hirumi et al.

2016; Lan et al. 2015; Pasfield-Neofitou et al. 2015; Zacharia et al. 2015). According to the

cognitive load theory, by guiding learners’ attention, the amount of visual search and

therefore extraneous load should be reduced and germane load increased if the learners are

motivated (Chandler and Sweller 1991; van Merriënboer and Ayres 2005). A meta-analysis

by Richter et al. (2016) confirmed the effectiveness of the signaling principle by showing

small to medium effect sizes for signaled learning material, especially for learners with low

prior knowledge.

According to the animation processing model, perceptual aspects of processing play a

key role in learning from animations (Lowe and Boucheix 2011). It postulates different

phases of processing: segmentation of the learning object (Phase 1), detailed processing of

single parts of the learning content (Phase 2), integration of these parts into a coherent

mental model (Phase 3), learning the functionalities of the depicted system and its com-

ponents (Phase 4), and learning the functionalities of the system within different contexts

(Phase 5). Although this model refers mainly to learning animated content, Phase 1, Phase

2, and Phase 3 may also be valid for a complex static content with an animated camera.

Further, the attention guiding function of the camera (framing, zooming, panning) may

foster these different processing steps. Zoom-in may thus be able to frame the different

segments of a complex learning object (Phase 1), and at the same time focus on these

segments for a more detailed processing (Phase 2), and zoom-out may be able to give an

overview of all the segments, which is necessary for integrating the different learning

segments into a coherent mental model (Phase 3).

Based on these theories, attentional guidance via visual zooming can be regarded as an

effective instructional feature to foster learning. Hence, in the present study, we focused on

the attentional processes of learning, thereby using eye-tracking measures which are widely

accepted operationalizations for providing indications of particular cognitive processes.

Overview of the experiment

To sum up, prior research indicates that camera movements and, more specifically, zooms

may guide attention, modulate the amount of elaborative processes, and foster mental

integration. Based on the studies by Salomon (1994; Salomon and Cohen 1977), we further

assume that these effects should not only hold for bold zooms, but also for minimal zooms.

Although minimal zoom-in does not substantially reduce the visible portion of a scene, it

may nevertheless elicit mental zooming in terms of a narrower focus and a deeper scru-

tinizing of elements in the center of the zoom. In turn, digital zoom-outs can be interpreted

as moving backwards in the scene, thus enlarging the field of view and providing an

overview of the depicted scene. Again, although minimal zoom-outs do not substantially

enlarge the visible portion of a scene, they may nevertheless cue mental zoom-outs, which

in turn may lead to a more dispersed pattern of attention across the scene, along with a

shallower processing of its individual elements.

In the past years, eye-tracking has become a well-accepted objective instrument to

measure visual processing. In particular, the total Euclidean distance between gaze points

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is assumed to represent the similarity of these gaze positions; dwell times (sum of the

duration of all fixations) are assumed to represent elaborative processing (either deeper or

shallower), and the number of transitions between two areas of interest per time unit is

assumed to represent integration processes (Holmqvist et al. 2011). Most of the reported

studies on zoom were conducted before 1995. At this time, eye-tracking was not a method

available to the researchers, and we are not aware of any further following studies that

examine the influence of zoom on visual attention. Hence, we chose to close this research

gap by conducting a study in which we measured the eye movements of the recipients

while they watched historical reconstructions either static, with zoom-in, or with zoom-out.

Assuming that zoom, similarly to a rhetorical device, directs viewers’ attention, we pos-

tulate the following hypotheses:

H1 Attentional guidance We assume that with zoom-ins there should be more gaze coherence (indicated by a lower total Euclidean distance) than with static film clips (H1a).

With zoom-outs, on the other hand, there should be lower gaze coherence compared to

static film clips (H1b).

H2 Elaborative processes We assume that with zoom-ins, dwell times on the center of a scene should be longer than with static film clips (H2a). Dwell times on the context should

be shorter with zoom-ins than with static film clips (H2b). With zoom-outs, there should be

shorter dwell times on the center (H2c) and longer dwell times on the context (H2d) than

with static film clips.

H3 Mental integration We assume that with zoom-ins fewer gaze transitions should occur between the center and the context than with static film clips (H3a). With zoom-outs,

more gaze transitions should occur between the center and the context than with static film

clips (H3b).

Method

Participants

For the experiment, 64 participants were recruited. Twenty-five had to be excluded from

analyses because of wearing contact lenses or the malfunctioning of technical equipment.

The remaining 39 participants were between 20 and 44 years old (M = 24.44, SD = 1.77),

nine (23.1 %) were male and 30 (76.9 %) were female.

Design

The study had a two factorial mixed 3 9 2 design with zoom (static vs. zoom-in vs. zoom-

out) as the between subjects factor and AOI (area of interest; center vs. context) as the

within subjects factor. Participants were randomly assigned to the three conditions. In each

condition there were n = 13 participants.

Material

As research material, we used images of an archaeologically validated computer-based 3D-

reconstruction of the ancient Greek city of Pergamon, which was produced by Lengyel and

Toulouse (2011a, b) in the context of the German excellence cluster TOPOI (The

Designing computer-based learning contents… 1141

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Formation and Transformation of Space and Knowledge in Ancient Civilizations) hosted

by the Freie Universität Berlin and the Humboldt-Universität zu Berlin. We took ten eye-

level view images of the reconstruction, each of a different building (see Fig. 1). From

each of these images, three types of film clips were generated: film clips with a static image

(middle frame of the zoom clips), film clips with a 10 % digital zoom-in on the center of

the scene, and film clips with a 10 % digital zoom-out of the center of the scene. A 10 %-

zoom thus represents a minimal but recognizable movement toward or away from the

depicted scene. Each film clip was five seconds long—an approximate duration which

occurred for three of four documentaries used in the study by Schaefer (1997). This

duration time has also been used by other authors doing research on film reception (Uhrig

et al. 2016), and it is what we consider the mean duration for documentary shots.

Measures

Eye tracking

Eye movements of the participants were recorded with a 60 Hz remote eye-tracking system

and the eye-tracking-software iViewX 2.8 and ExperimentCenter 3.4, all from the com-

pany SMI (Senso Motoric Instruments). The presentation software ExperimentCenter 3.4

was installed on two computers with 27-inch displays. Each participant was seated in front

of one of the computer screens; his or her chin was placed on a chin rest to control for

constant eye-to-screen distance, head movements, and also the horizontal visual angle of

52.7�. The system coded a fixation when the gazes of the participants remained at a minimum of 80 ms within an area of a maximum of 100 px. As a measure of gaze

coherence, for each measured time point (60 time points per second) of each film clip, the

total Euclidean distance between the gaze points of the respective participants was cal-

culated in pixel. The recorded eye movements were further processed with the analyzing

software BeGaze 3.4 from SMI and analyzed with regard to dwell times (sum of the

duration of all fixations; in BeGaze named fixation times) on the center of the scene

(central rectangle of 25 % of the whole image area) and on the context (remaining 75 % of

the image around the central rectangle), both standardized on the size of the respective area

(see Fig. 2). Finally, the number of transitions between the center area and the context area

(and vice versa) was calculated for each scene.

Control variables

Certain variables that were mentioned in relation to different arguments in the introduction

may influence the postulated effects and were therefore measured as control variables:

subjective feelings of presence, aesthetics, critical attitude, entertainment, interest, and

informativeness. Feeling of presence was measured by a questionnaire developed by Dinh

et al. (1999) which was translated into German. It contained 13 items that had to be

answered on a 5-point Likert-scale with the following options: 1 = poor, 2 = fair,

3 = good, 4 = very good, 5 = excellent. A mean value was calculated across the 13

items. In addition, the participants were asked to answer single questions on aesthetics,

critical attitude, entertainment, interest, and informativeness with regard to the film clips.

The answers were given on a five-point Likert-scale ranging from 1 = ‘‘not at all’’ to

5 = ‘‘very much’’.

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Procedure

After the participants were seated in front of a computer screen, the experimenter informed

them orally about the experimental procedure as follows: ‘‘[…] Your task is to watch the reconstructions carefully. […] After you have watched all reconstructions, you will be asked some questions about them. […]‘‘(for a complete translation of the instructions see the appendix). After these instructions, the eye-tracking system was calibrated and the

recording was started together with the presentation of the film clips. Each participant

watched all ten reconstructions either as static clips, as zoom-in clips, or as zoom-out clips.

The reconstructions were randomly presented and the film clips were separated by a

fixation cross, visible for five seconds. Finally, participants answered the feeling of pres-

ence questionnaire, the questions on the control variables, aesthetics, informativeness,

critical attitude, entertainment, and interest with regard to the film clips, and demographic

questions on their age, sex, and field of study or profession. The experimental session took

about 15 min.

Data analysis

The data were analyzed by inferential statistics. One way analyses of variance (ANOVA)

were used to calculate the differences between the three conditions with regard to control

variables, dwell times, and transitions. The total Euclidean distance data were not normally

distributed; hence, we used the non-parametric Kruskal–Wallis-Test instead of the one-

Fig. 1 Images of the reconstruction of Pergamon from which film clips were generated

Fig. 2 AOIs (= areas of interest) for the central building and the context

Designing computer-based learning contents… 1143

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way ANOVA. For the correlation between dwell times and transitions, the Pearson pro-

duct-moment correlation coefficient was calculated. Effect sizes were reported in partial

eta squared (gp 2 ) as a widely used coefficient in educational research, which can be

benchmarked against Cohen’s criteria of small, medium, and large effects (Richardson

2011). Cohen (1977) interprets the effect size coefficients d = 0.2 and r = 0.10 to be

small, d = 0.5 and r = 0.30 to be medium, and d = 0.8 and r = 0.50 to be large effects.

According to Fritz et al. (2012), these d- and r- values correspond to the following values

of (partial) eta squared: g2 = 0.01 for small effects, g2 = 0.06 and g2 = 0.14. Statistical power (1 - b error probability) of the different effects was calculated by the software G*Power using an alpha error probability of 0.05. According to Cohen (1992), a statistical

power of 0.80 and higher can be regarded as acceptable.

Results

Control variables

None of the control variables (presence, aesthetics, critical attitude, entertainment, interest,

informativeness) differed significantly between the conditions, all F \ 2.46, all p [ 0.05.

Total Euclidean distance

Total Euclidean distance between the gaze points per participants across all time points,

film clips, and participants (in pixel) was not normally distributed within the conditions.

Hence, the nonparametric Kruskal–Wallis-Test was calculated. The results show that

Euclidean distance differed significantly between the conditions, V2 = 355.59, p \ 0.001. Keep in mind that a low value implies that the viewers tended to focus on a small area in

the picture, thus indicating high gaze coherence. Therefore, in line with Hypothesis 1,

Euclidean distance was lowest in the zoom-in condition (M = 413.76, SD = 150.06),

followed by the static condition (M = 458.41, SD = 157.06), and highest in the zoom-out

condition (M = 480.42, SD = 166.16). The statistical power for this effect is 0.32.

Dwell times

Data were normally distributed within the conditions. Because of the high interdependence

between the dwell times for the center and the context AOIs, two separate analyses were

calculated for the dwell times on the center AOIs and for the dwell times on the context

AOIs. For the dwell times on the center AOI, an analysis of variance (ANOVA) was

calculated with zoom (static vs. zoom-in vs. zoom-out) as the between subject variable.

The results show a significant effect, F (2, 38) = 5.83, p \ 0.01, gp 2 = 0.244. Dwell times

on the center AOI with zoom-in (M = 0.58, SD = 0.07) were significantly longer than

with zoom-out (M = 0.49, SD = 0.07) and with static film clips (M = 0.50, SD = 0.07).

The statistical power for this effect is 0.88. For the dwell times on the context AOI, again

an analysis of variance (ANOVA) was calculated with zoom (static vs. zoom-in vs. zoom-

out) as the between subject variable. The results show a significant effect, F (2, 38) = 5.15,

p \ 0.05, gp 2 = 0.222. The statistical power for this effect is 0.86. The dwell times on the

center AOI with zoom-in (M = 0.08, SD = 0.02) were significantly shorter than with

zoom-out (M = 0.10, SD = 0.02) and with static film clips (M = 0.11, SD = 0.02). The

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dwell times on the center AOI correlated significantly, highly, and negatively with the

dwell times on the context AOI, r = -0.728 p \ 0.001 (Pearson correlation). Dwell times for the center AOI and the context AOI in the different conditions are presented in Fig. 3.

Thus, Hypothesis 2 was partially confirmed by the data.

Transitions

Data were normally distributed within the conditions. A one-way analysis of variance

(ANOVA) was calculated with zoom (static vs. zoom-in vs. zoom-out) as the between

subject variable. The results show a significant main effect for zoom, F (2, 38) = 4.56,

p \ 0.05, gp 2 = 0.202. Participants in the zoom-in condition (M = 4.38, SD = 0.99) made

significantly fewer transitions between the center and the context of the scene and vice

versa than participants in the static condition (M = 5.35, SD = 0.86) and in the zoom-out

condition (M = 5.40, SD = 1.07). The statistical power for this effect is 0.74. There was

no significant difference in the number of gaze transitions between the zoom-out and the

static condition. Transitions correlated significantly and negatively with the dwell times on

the center AOI, r = -0.429, p \ 0.01, but not with the dwell times on the context AOI (Pearson correlations). Thus, Hypothesis 3 was partially confirmed by the data.

Discussion

In the present study, the role of digital zoom as a tool for directing attention while looking

at visual learning material was investigated. In particular, we analyzed whether minimal

digital zoom, similar to a rhetorical device, is able to cue mental zooming. Mental zooming

is involved in various ways in visual attentional processes. By taking archaeological

reconstructions as the material, we hypothesized that minimal zoom-ins should lead to a

lower Euclidean distance between the gaze points per participant across all time points,

film clips, and participants as an indicator of higher gaze coherence, longer dwell times as

an indicator of more elaborative processing, and fewer transitions as an indicator of less

mental integration. Zoom-outs, on the other hand, were expected to have the opposite

effects.

Fig. 3 Dwell times on the center and the context of the presented scene in the different zoom conditions

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First, the results showed that zoom-ins actually led to a lower Euclidean distance of the

gazes of the recipients compared to the static film clips. Zoom-outs, on the other hand, led

to a higher Euclidean distance of gazes compared to the static film clips. The results,

therefore, confirm our hypotheses—H1a as well as H1b—on the effects of zoom on

attentional guidance. However, with 0.32, the statistical power for this analysis was quite

low. Therefore, this result can only be interpreted as a hint that the effect may actually

exist. Based on this finding, we plan to examine this effect in future studies with a larger

sample size and, hence, higher statistical power.

Second, the recipients fixated longer on the center than on the context of the presented

scene. This is not surprising because recipients are used to the fact that important infor-

mation is often presented in the central area of a picture. Tseng et al. (2009), for example,

found this central bias also for natural scenes. More importantly, our study showed that

using zooms for presenting computer-based reconstructions has an influence on the visual

attention of the recipients necessary for conscious processing. With zoom-ins, recipients

fixated longer on the center and shorter on the context of a presentation compared to static

clips. With zoom-outs, on the other hand, there was no significant difference in the dwell

times on the center and no significant difference in the dwell times on the context com-

pared to static film clips. Hence, our hypotheses on the effect of zoom on visual attention

for elaborative processing could only be confirmed for zoom-ins (H2a, H2b), but not for

zoom-outs (H2c, H2d). We can conclude that zoom-ins seem to direct the attention of the

recipients away from the context toward the center of the presented scene. This is a very

certain finding because the statistical power for the analysis on the dwell times for the

center AOIs is 0.88 and the statistical power for the analysis on the dwell times for the

context AOIs is 0.86.

Third, zoom-ins led to a reduction of gaze transitions between the center and the context

of the presented scene. This result may be interpreted as a reduction of processes to

integrate central and contextual information into a coherent mental representation. If

recipients fixate more on the center of the presentation, they may have less cognitive

capacity left for integration processes. Further, zoom-outs did not show significant dif-

ferences in gaze transitions compared to the static condition. Our hypotheses on the

influence of zoom on transitions could, therefore, only be confirmed for zoom-ins (H3a),

but not for zoom-outs (H3b). The statistical power for this effect is 0.74, which is quite

good.

To sum up, zoom-ins were found to decrease the Euclidean distance between gazes as

an indicator of higher gaze coherence and increase dwell times as an indicator of elabo-

rative processing of the center parts of the depictions while decreasing transitions as an

indicator of mental integration of pictorial elements from the center and the context. In

contrast, the patterns of results from zoom-outs and static presentations were similar to a

large degree, indicating that zoom-ins and zoom-outs do not operate in a complementary

fashion. The attentional shift due to zoom-ins shown in our experiment may be explained

by the zoom lens model (Eriksen and James 1986): A minimal zoom-in may cue a mental

zoom-in and thereby guide visual attention and elaborative processing to the contents in the

focus of the zoom and less visual attention and elaborative processing to the contextual

information. The fact that the effect of zoom-outs on dwell times (H2c, H2d) could not be

confirmed might be explained by the initial attentional frame of the viewers, which may

have already covered the entire scene. Future studies are needed to find out whether the

effects of zoom-outs on dwell times occur with more complex material and which other

methods are able to direct the recipients’ attention more to the contextual areas.

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According to the theory of multimedia learning (Mayer 2009, 2014) and its signaling

principle (van Gog 2014), the attention guiding function of zooms as shown in the present

study may be used by instructional designers to foster learning (Zacharia et al. 2015). By

directing learners’ attention via zooms, cognitive resources may be applied more effi-

ciently, which is also in line with the cognitive load theory (Chandler and Sweller 1991;

van Merriënboer and Ayres 2005). Furthermore, the present results, examined with static

learning contents, may also be applicable to different phases of learning with animations

(Lowe and Boucheix 2011). Zoom-ins may be used to foster elaborative processing of

particular details of animations (Phase 2) and integration of these different parts into a

coherent mental model (Phase 3). When using zoom-outs, on the other hand, this seems not

to be possible—at least based on the present findings.

There are nonetheless certain limitations of the present study. As already mentioned, by

recording and analyzing the gaze behavior of the recipients, we gain evidence on the overt

attentional processes (visual attention) which we assumed to represent mental processes.

However, this is not always the case. There may be covert attentional processes which are

not represented by visual attention, and there may be some aspects of visual attention that

do not represent mental attention, such as automated visual attentional processes caused by

the visual changes of the minimal digital zoom. Also, although it is widely accepted to

interpret eye-tracking measures with regard to elaborative processing and mental inte-

gration (Holmqvist et al. 2011), we would like to point out that while eye-tracking mea-

sures directly measure visual attention, they only indirectly measure cognitive processes.

We cannot draw conclusions on higher cognitive processes or knowledge acquisition, thus

making future studies necessary. Such studies should use additional and more direct

measures of cognitive processing combined with eye-tracking data.

Regarding statistical power, from our results, the results for dwell times and the results

for transitions can be regarded as stable. The results on the Euclidean distance have to be

treated with caution because they must first be replicated with larger sample sizes and,

hence, with more statistical power. Nevertheless, they provide a first hint that this effect

may exist.

A further limitation of the present study is that we did not measure learning outcomes in

a direct manner. Prior studies indicate that there is a relationship between camera tech-

niques and knowledge acquisition (Amadieu et al. 2011; Munger et al. 2006; Kipper 1986;

Garsoffky et al. 2007; Meyerhoff et al. 2013) and between attention and knowledge

acquisition (Richter et al. 2016). Nevertheless, in future studies, measuring visual attention

should be accompanied by measuring knowledge acquisition.

Finally, examining the above effects with different zoom points may be interesting for

future research. Going beyond zooms, the effects of different camera movements and

camera perspectives on the reception of 3D-presentations might also be interesting for

future studies.

The present findings are of importance for both theoretical and practical developments.

Regarding theory, our results extend the zoom lens model (Eriksen and James 1986) by

providing empirical evidence that minimal digital zoom-ins (but not zoom-outs) may

function as procedural cues for increased visual attention on the contents in the focus of the

zoom and more elaborative processing of these contents. Hence, digital zoom is not only

able to supplant mental zooming (Salomon and Cohen 1977) but may also trigger mental

zooming when used like a rhetorical device. Both types of usages could possibly be

beneficial in particular learning situations. However, camera zoom as an attention guiding

cue may not be as precise as other forms of cueing such as color coding or naming (Glaser

and Schwan 2015). The cueing function of zooms applies to the whole scene, while other

Designing computer-based learning contents… 1147

123

cues operate more specifically within the picture. On the other hand, Lowe and Boucheix

(2011) suggest that alternatives to visuospatial cues are needed in the field of learning

complex animations. Perhaps zooms could be such an alternative. Further, our study

provides empirical evidence that it is not only advisable to use dynamic compared to static

images in presenting learning contents (Smith and Henderson 2008), but also that the

direction of the dynamic is of particular importance with regard to attention and cognitive

processing. Zoom-ins appear to make the recipients’ gazes more coherent, center their

attention, and lead to more elaborative processing.

For designing computer-based learning contents, our results indicate that zoom-ins can be

used to guide the recipients’ attention to important elements in the center of the presented

scene. However, using zoom-outs to widen recipients’ attention for the integration of central

and contextual information seems not to work, at least not with material low in complexity.

The attention guiding effects that Ken Burns intended with his usage of zoom could therefore

be partly confirmed: Zoom-ins (but not zoom-outs) as part of the Ken Burns Effect are actually

able to direct viewers’ attention. By providing an empirical basis for effects intuitively

assumed and practiced by film makers, our results may encourage 3D-computer-designers to

use these competences as well, now with an empirical basis and, thereby, contribute to the

present trend that the professions of film and 3D-computer-design are merging more and more.

Finally, the present findings may not only contribute to the generation of archaeological

reconstructions but also to the development of learning material in different professions, for

example, architecture, astronomy, biology, physics, engineering, and many more.

Compliance with ethical standards

Conflict of Interest The authors declare that they have no conflict of interest.

Ethical Approval All procedures performed in the study involving human participation were in accordance with the ethical standards of the DGPs (Deutsche Gesellschaft für Psychologie, German Psychological Society) and the APA (American Psychological Association) and have been approved by the local Insti- tutional Review Board.

Appendix: Instruction

Dear Participant,

Thank you for your participation in the present experiment.

In the following, 10 reconstructions of historical buildings of the ancient Greek city

Pergamon will be presented to you, each for five seconds.

Your task is to watch all reconstructions carefully. Between the reconstructions, fixation

crosses are presented. Please fixate them until the next reconstruction appears.

After you have watched all reconstructions, you will be asked some questions about

them.

If you have any questions, please contact the experimenter.

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Manuela Glaser studied psychology at the University of Tübingen (diploma 2006). Since January 2007, Manuela Glaser has been working as a research scientist at the Leibniz-Institut für Wissensmedien in the Realistic Depictions Lab, headed by Prof. Dr. Stephan Schwan. In 2010, she did her Ph.D. on ‘‘Hybrid documentary formats: How narrative elements in archaeological television documentaries influence

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processing, experience, and knowledge acquisition’’. Current research interests are learning with historical reconstructions, edutainment, and informal learning in museums.

Dominik Lengyel graduated in architecture at the Universität Stuttgart in 1997. He worked among others at the architectural offices of Prof. O. M. Ungers in Cologne and Gwathmey Siegel & Associates in New York. Since 1999 he runs his office for architectural visualisations with Catherine Toulouse with public and private clients in industry and cultural and research institutions. From 2002 to 2005 he was substitute and full professor for architectural representations at the University of Applied Sciences Cologne, since 2006 he is full professor for visualisation at the Brandenburgische Technische Universität Cottbus-Senftenberg. His main interests are architectural visualisations and visualisations of uncertain knowledge.

Catherine Toulouse graduated in architecture at the Universität Stuttgart in 1997. She worked among others at the architectural offices of Prof. O. M. Ungers in Cologne and Gwathmey Siegel & Associates in New York. Since 1999 she runs her office for architectural visualisations with Dominik Lengyel with public and private clients in industry and cultural and research institutions. Since 2006 she is assistant professor for visualisation at the Brandenburgische Technische Universität Cottbus-Senftenberg. Her main interests are architectural visualisations and visualisations of uncertain knowledge.

Stephan Schwan earned his doctoral degree in psychology in 1992 and habilitated in psychology in 2000 at the University of Tuebingen. From 2002 to 2004 he was full professor for eLearning at the University Linz. Since 2004 Stephan Schwan is a full professor for research on teaching and learning at the Leibniz-Institut für Wissensmedien in Tuebingen. His main interests are perceptual and cognitive processes during the reception of three-dimensional and interactive visual presentations.

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