Investigating the redundancy principle in immersive virtual reality environments: An eye-tracking and EEG study

Abstract

Background
The increased availability of immersive virtual reality (IVR) has led to a surge of immersive technology applications in education. Nevertheless, very little is known about how to effectively design instruction for this new media, so that it would benefit learning and associated cognitive processing.

Objectives
This experiment explores if and how traditional instructional design principles from 2D media translate to IVR. Specifically, it focuses on studying the underlying mechanisms of the redundancy‐principle, which states that presenting the same information concurrently in two different sensory channels can cause cognitive overload and might impede learning.

Methods
A total of 73 participants learned through a specifically‐designed educational IVR application in three versions: (1) auditory representation format, (2) written representation format, and (3) a redundancy format (i.e. both written and auditory formats). The study utilized advanced psychophysiological methods of Electroencephalography (EEG) and eye‐tracking (ET), learning measures and self‐report scales.

Results and Conclusions
Results show that participants in the redundancy condition performed equally well on retention and transfer post‐tests. Similarly, results from the subjective measures, EEG and ET suggest that redundant content was not found to be more cognitively demanding than written content alone.

Implications
Findings suggest that the redundancy effect might not generalize to VR as originally anticipated in 2D media research, providing direct implications to the design of IVR tools for education.

Full text

ARTICLE
Investigating the redundancy principle in immersive virtual
reality environments: An eye-tracking and EEG study
Sarune Baceviciute | Gordon Lucas | Thomas Terkildsen | Guido Makransky
Department of Psychology, University of
Copenhagen, Copenhagen, Denmark
Correspondence
Sarune Baceviciute, University of Copenhagen,
Øster Farimagsgade 2A, 1353 København K,
Denmark.
Email: sb@psy.ku.dk
Funding information
Innovationsfonden
Abstract
Background: The increased availability of immersive virtual reality (IVR) has led to a
surge of immersive technology applications in education. Nevertheless, very little is
known about how to effectively design instruction for this new media, so that it
would benefit learning and associated cognitive processing.
Objectives: This experiment explores if and how traditional instructional design prin-
ciples from 2D media translate to IVR. Specifically, it focuses on studying the under-
lying mechanisms of the redundancy-principle, which states that presenting the same
information concurrently in two different sensory channels can cause cognitive over-
load and might impede learning.
Methods: A total of 73 participants learned through a specifically-designed educa-
tional IVR application in three versions: (1) auditory representation format, (2) written
representation format, and (3) a redundancy format (i.e. both written and auditory
formats). The study utilized advanced psychophysiological methods of Electroen-
cephalography (EEG) and eye-tracking (ET), learning measures and self-report scales.
Results and Conclusions: Results show that participants in the redundancy condition
performed equally well on retention and transfer post-tests. Similarly, results from
the subjective measures, EEG and ET suggest that redundant content was not found
to be more cognitively demanding than written content alone.
Implications: Findings suggest that the redundancy effect might not generalize to VR
as originally anticipated in 2D media research, providing direct implications to the
design of IVR tools for education.
KEYWORDS
EEG, eye-tracking, immersive virtual reality, learning, redundancy principle
1|INTRODUCTION
Educators and instructional designers around the globe are in search
of new and alternative ways to engage and educate the new generation
of students. Considering the recent popularity of immersive virtual reality
(IVR) and acknowledging its captivating nature, it is not surprising that this
technology is becoming more frequently used in various educational con-
texts (Raditanti et al., 2020). IVR tools have, for instance, already been
incorporated in the teaching of curricula at high school and university
levels (Makransky et al., 2021; Jones, 2018). IVR is also emerging in the
training of professionals in organizational settings (Butussi &
Chittaro, 2018; Chittaro & Buttussi, 2015; Muller Queiroz et al., 2018).
Incipient research investigating digital learning suggests that IVR
can function as a powerful motivational aid (Makransky &
Lilleholt, 2018, Makransky & Petersen, 2019; Chittaro &
Buttussi, 2015; Huang et al., 2020). A recent meta-analysis by Wu
et al. (2020) also found an advantage of IVR lessons compared to less-
immersive learning approaches on learning outcomes. Cummings and
Received: 7 April 2021 Revised: 18 June 2021 Accepted: 11 July 2021
DOI: 10.1111/jcal.12595
J Comput Assist Learn. 2021;1–17. wileyonlinelibrary.com/journal/jcal © 2021 John Wiley & Sons Ltd 1

Bailenson (2016) define immersion as an objective measure of the viv-
idness offered by a system, and the extent to which the system is
capable of shutting out the outside world. Therefore, IVR lessons
accessed through head mounted displays (HMDs) are often referred
to as immersive lessons, and lessons accessed through traditional 2D
monitors are often referred to as less immersive media or non-
immersive media (Wu et al., 2020). The immersion principle in multi-
media learning (Makransky, 2021) and the cognitive affective theory
of immersive learning (CAMIL; Makransky & Petersen, 2021) describe
how the fundamental driver of increased learning outcomes in
immersive media is the use of instructional design principles that are
effective in immersive lessons. Latest research has also shown that
how well IVR promotes learning is greatly dependent on how IVR-
specific content has been designed (Meyer et al., 2019; Baceviciute et
al., 2020; Makransky, 2021; Luo et al., 2021; Parong & Mayer, 2018).
In this direction, recent reviews have highlighted several gaps in IVR
based educational research and propose that future research should:
(1) Use learning theories to guide IVR based application development
and research (Raditanti et al., 2020); (2) Shift attention from VR tech-
nology to VR-based instructional design with a redefined focus on the
effective integration of technology and theory (Luo et al., 2021); and
(3) Use more diversified research designs and methods to improve the
rigour and relevance (Luo et al., 2021).
The CAMIL provides a theoretical framework for understanding
and investigating learning in immersive environments such as IVR. The
CAMIL identifies presence and agency as the two main affordances of
learning in immersive environments builds on existing learning and
motivational theories to describe how presence and agency influence
learning through several affective and cognitive factors such as
interest, motivation, self-efficacy, embodiment, cognitive load, and
self-regulation (Makransky & Petersen, 2021). The model describes
that it is not the medium of IVR that causes specific learning out-
comes, but rather the instructional methods used in IVR that will con-
stitute its effectiveness. The CAMIL builds on empirical evidence that
media interacts with method, meaning that learning methods affect
learning, but certain methods are more or less relevant in IVR. For
instance, research has identified instructional methods such as the
pre-training principle (Meyer et al., 2019, Petersen et al., 2020), and
generative learning strategies such as enactment (Makransky et al.,
2021), and summarization (Klingenberg et al., 2020) to be more effective
in more immersive compared to less immersive learning environments.
Such findings therefore suggest that it is important to conduct research
that specifically investigates how instructional design principles devel-
oped in 2D media generalize to immersive learning environments, rather
than conducting media comparison studies that confound instructional
design factors (Makransky et al., 2019b, Baceviciute et al., 2020). This
knowledge is necessary so that instructional designers can develop effec-
tive learning material for IVR and related learning technologies.
The current experiment investigates issues related to written
and auditory informational representations in educational IVR envi-
ronments(IVREs),asthesecontinuetobeusedastheprimaryvehi-
cles for representing learning content not only in non-immersive,
but also in immersive media (Baceviciute et al., 2021). Specifically,
we focus on the redundancy principle from the cognitive theory of
multimedia learning (CTML), which states that presenting the same
information concurrently in two different sensory channels
(i.e., auditory and visual) can cause cognitive overload and might
impede learning (Mayer, 2014, 2020). Understanding the impact
and underlying mechanisms of visual and auditory redundancy is
important because instructional designers are typically faced with
instructional design decisions related to effective learning informa-
tion representations in immersive educational applications.
Although there is evidence for the redundancy principle in 2D
media (Adesope & Nesbit, 2012), the articles that have investigated
the redundancy principle in IVR (Makransky et al., 2019b; Moreno &
Mayer, 2002) have not found evidence for the principle. Existing
results suggest that redundant information in immersive lessons
could potentially have beneficial as well as detrimental conse-
quences to learning. The redundancy principle was thus selected to
be investigated in this study because there is inconsistency
between the evidence for the principle when comparing 2D and
immersive environments. Furthermore, providing redundant infor-
mation may be specifically relevant in IVR settings where learners
can view and interact with many elements in an immersive
360-degree environment. This is fundamentally different from
learning with a 2D monitor where learners have a visual overview
of an entire environment. In the current study, we use advanced
psychophysiological methods, including electroencephalography
(EEG) and eye-tracking (ET), learning measures, and self-reported
scales to gain a better understanding of the underlying mechanisms
of the redundancy principle in immersive learning.
2|BACKGROUND
2.1 |IVR for learning and education
IVR can be conceptualized in various ways. In this article we refer to it
as a complex media system that on the one hand consists of a unique
technological setup, which encompasses sensory immersion made
available through a head mounted display (HMD; Howard, 2019), and
on the other –of immersive content that capitalizes on technological
immersion to represent pedagogy (Mikropoulos & Natsis, 2011).
While IVR is still not an integrated learning tool, the last decade has
seen the technology become widely explored in various educational
contexts spurred in part by its captivating nature and ability to sepa-
rate the learner from external distractions (Raditanti et al., 2020). IVR
has, for example, been used to supplement teaching at school
(Petersen et al., 2020, Makransky et al., 2021); while others have also
used it for informal learning (Christensen & Knezek, 2016). IVR has
also been applied in various educational levels: from K-12 instruction
to higher education (Makransky et al., 2019a, Makransky et al.,
2021; Jones, 2018; Luo et al., 2021) to professional training in
industrial contexts (Butussi & Chittaro, 2018; Chittaro &
Buttussi, 2015; Muller Queiroz et al., 2018; Tang et al., 2020).
Applications of IVR also span across different fields; however due
2BACEVICIUTE ET AL.

to the unique ability of the technology to facilitate the visualiza-
tion of complex phenomena that is hard to access or to explain
without technological support and very specialized tools
(Jensen & Konradsen, 2018; Johnson-Glenberg, 2019), IVR has
become especially popular in STEM education (e.g., biology, phys-
ics and math; see Raditanti et al., 2020).
Following this emergence of IVR in education, educational psy-
chology and instructional design researchers have begun to examine
whether immersive technology can in fact benefit learning. Evidence
supports its motivational benefits, suggesting that students enjoy
learning digitally more than traditional methods (Makransky &
Lilleholt, 2018; Makransky & Petersen, 2019; Makransky & Petersen,
2019; Makransky et al., 2020; Bogusevschi et al., 2019), and that edu-
cational content is perceived as more engaging when presented in an
immersive format (Makransky et al., 2019b; Parong & Mayer, 2018).
Furthermore a meta-analysis by Wu et al. (2020) provides evidence
that immersive technologies have a small positive effect on knowl-
edge acquisition as well as skill development compared to more tradi-
tional media. This is supported by the meta-analysis by Luo
et al., 2021 who also found a medium effect for HMD-based lessons.
There is however, variance regarding the effectiveness of IVR for
learning, and several studies have identified negative implications of
using IVR in education. Some, for example have discussed the isolat-
ing nature of IVRs (Mütterlein & Hess, 2017), while other studies have
found it to lead to extraneous cognitive load (CL; Makransky et
al., 2019b; Richards & Taylor, 2015).
One challenge is that many studies take a purely techno-centric
approach to IVR based learning, which does not consider that IVR
also incorporates educational content that needs to be strategically
designed and evaluated to promote pedagogy (Baceviciute et
al., 2020; Fowler, 2015; Jensen & Konradsen, 2018; Mikropoulos &
Natsis, 2011). Recent research in this direction has started to pro-
duce empirical evidence for the importance of instructional design
in IVR. One study, for example, exported a non-immersive VR simu-
lation to an immersive format without optimization, and showed
that direct translation of content from 2D media to 3D can lead to
lower learning and a heightened CL to the learner (Makransky et
al., 2019b). In a follow-up study, no diminishing effects were found
on learning when translating learning content from 2D to 3D with
respect to unique affordances of VR (Baceviciute et al., 2021). The
authors concluded that for IVR to be successful in education,
instruction and learning content needs to be specifically designed
to fit the affordances of immersive technology. Similarly, prior
research found that auditory informational representations were
not as effective as written representations when comparing learn-
ing outcomes of retention, self-efficacy, intrinsic CL and extraneous
attention (Baceviciute et al., 2020). EEG frequency comparisons
performed in the study suggested that auditory informational rep-
resentations were also not as cognitively stimulating
(Baceviciute et al., 2020). Other studies that have investigated
the importance of instructional design in IVR have found differ-
ences in learning effectiveness when using different pedagogical
agents in IVR (Makransky et al., 2019c). Studies have also
identified the importance of using scaffolding strategies such as
pre-training (Meyer et al., 2019; Petersen et al., 2020), as well
as generative strategies of summarizing (Parong & Mayer, 2018)
and enacting after an IVR lesson (Makransky et al., 2021). These
results not only suggest that the design of learning content is
imperative for learning efficacy of IVR, but also show that tradi-
tional instructional design principles from non-immersive media
might not always directly translate to IVR applications, necessi-
tating further and more in-depth investigations into instructional
learning content design in this medium.
2.2 |The redundancy principle in multimedia
learning
Contrary to the intuitive belief that presenting the same information
in various formats enhances learning, the redundancy principle states
that redundant information inhibits learning (Mayer, 2014, 2020). This
finding has been observed in numerous studies (Craig et al., 2002;
Gerjets et al., 2009; Kalyuga et al., 2004; Mayer et al., 2001) and is
based on the Cognitive Load Theory (CLT; Sweller, 2011) and CTML.
These theories explain that the redundancy effect occurs due to an
increase in extraneous CL that arises due to concurrent processing of
redundant information. The need to process redundant information
sources generates strong demands on the learners' working memory
(WM), and thus cognitive resources are not spent on learning.
Processing novel information is heavily constrained by working memory
capacity and duration, and without rehearsal can only be stored in short
term memory for a brief period of time. As such, according to CTML,
instructional design should aim to minimize any unnecessary WM load in
the presentation of novel information. Based on this, the redundancy
principle formulated in the CTML (Mayer, 2014; Mayer &
Johnson, 2008) states that redundant information should generally be
avoided during learning, since ‘people learn better when the same informa-
tion is not presented in more than one format’(Mayer, 2014, pp. 19–20).
What information is redundant, however, might depend on the
learning context, as well as the learners' expertise (Mayer, 2014). As an
example, in complex learning scenarios novice learners might use con-
current information representations as supporting explanatory material.
However, as their levels of expertise increase and the need for addi-
tional explanation decreases, this information will eventually become
redundant. A meta-analysis carried out by Adesope and Nesbit (2012)
summarized the data of 57 studies to estimate effect sizes comparing
combined auditory and written redundancy conditions to either
written-only or auditory-only representations. Their analysis shows that
across all studies redundancy slightly improves learning outcomes
(Hedges g=0.15). For example, redundancy conditions had no advan-
tage compared to written-only conditions (g=0.04). On the other
hand, redundancy enhanced learning when contrasted with auditory-
only representation (g=0.29). This advantage stems mostly from stud-
ies where correspondence between the auditory and written text was
low (g=0.99), rather than high, (g=0.21). The prevalence of the
redundancy effect was further moderated by factors such as learners'
BACEVICIUTE ET AL.3

prior knowledge, their freedom in pacing the learning content, or the
simultaneous presentation of other visual information, such as anima-
tions and diagrams (Adesope & Nesbit, 2012). While this meta-analysis
did not specifically investigate the redundancy principle in IVR, its find-
ings suggest that a general applicability of the principle cannot be
supported across different media and educational contexts.
Few research studies have examined the redundancy principle in
IVR. Moreno and Mayer (2002) investigated the redundancy effect in a
VR simulation across two different media conditions (i.e., IVR, and desk-
topVR)andthreedifferentmethodconditions (i.e., auditory text, written
text, and redundancy). There was no difference between the redundancy
and auditory conditions on the outcomes of retention and transfer, but
both conditions significantly outperformed the text-only condition on
these outcomes. The authors concluded that the findings are inconsis-
tent with prior studies on redundancy (Moreno & Mayer, 2002). Their
interpretation is that it is possible that students in the redundancy condi-
tion may have focused on the auditory narration alone. The authors rea-
soned that this might be a consequence of the experiential nature of the
IVRE, making learners less likely to read a text box if they can obtain the
same information by listening to a narration. However, as Moreno and
Mayer (2002) did not have access to gaze data, their interpretation could
not be explored and corroborated. In a recent media and methods exper-
iment (Makransky et al., 2019b) also investigated the redundancy effect
across desktop and immersive versions of VR simulations. In accordance
with the previous study, the authors failed to find evidence for the
redundancy principle across both media conditions. These initial findings
suggest that the redundancy principle, originally conceived in 2D media,
might not be extendable to IVR, but the mechanisms underlying these
findings are not clear.
No studies have investigated whether learners primarily read or
listen to text when learning in redundancy conditions in IVR. There-
fore, in the current study we want to examine whether learners in the
redundancy condition attend more to the auditory or written informa-
tion using ET. This would provide valuable information about the
underlying processes that take place when attending to and learning
from different information representation methods in IVR. Addressing
gaps in existing literature, another aim of this study is to gain greater
insight into the cognitive demands imposed on the learner when
learning with redundant information. In CLT (Sweller, 2011; Sweller
et al., 2011), three dimensions of CL have been proposed: Intrinsic CL
(i.e., intrinsic difficulty of the topic/learning material), extraneous CL (i.-
e., CL imposed by factors external to the learning material,
e.g., instructions, explanations), and germane CL (i.e., effort that is
required for learning). Traditionally, CL has been assessed using singu-
lar self-report items (Ayres, 2006; Cierniak et al., 2009; Paas, 1992;
Salomon, 1984). To combat the lack of a uniformly used scale, Leppink
et al. (2013) developed and validated a CL scale which measures CL
demands more reliably. The self-reported items, however, have limita-
tions (such as self-report bias) which do not provide the full insight of
cognitive processing during learning (Makransky et al., 2019b). To
supplement the self-report items, this study also attempts to measure
CL with EEG and ET.
2.3 |Using EEG to measure cognitive load during
learning
Several studies have explored the use of EEG as an effective online
measure of cognition during learning across media, including IVR
(Antonenko et al., 2010; Makransky et al., 2019b, Baceviciute et al.,
2020; Baceviciute et al., 2021; Örün & Akbulut, 2019). In particular,
frequency-based analyses of EEG data have recently seen traction as
an unobtrusive measure that can be used during learning
(Antonenko & Keil, 2017; Baceviciute et al., 2020; Baceviciute et al.,
2021; Scharinger, 2018). Previous experimental and theoretical work
has focused on oscillations in the Theta and Alpha frequency bands.
These have been consistently demonstrated to be sensitive to the
changes in cognitive processes, such as attention and WM load, which
are relevant for novel information acquisition (Antonenko &
Keil, 2017; Brouwer et al., 2012). Generally, increases in Theta activa-
tion (4–8 Hz) have been previously linked to increased mental effort
(Klimesch, 1999). More specifically, Theta frequency activity in frontal
areas, has been linked to working memory capacity across several
studies (Puma et al., 2018). In these studies, increasing levels of spec-
tral power in the Theta band is proposed to reflect increasing WM
load (Mühl et al., 2015). Parietal Theta, on the other hand, has been
linked to effective long-term memory encoding, suggesting that
increases in parietal Theta could be later linked to successful memory
retrieval, which is vital for learning (Osipova et al., 2006). Given that
redundancy of learning information is theoretically believed to be
more difficult as it elicits higher levels of extraneous WM load, such
literature suggests that the redundancy format would have higher
levels of frontal Theta in comparison with the other conditions.
Oscillatory activation in the Alpha frequency band (8–12 Hz) has
been previously linked to changes in attentional processes (Frey
et al., 2014). Generally, Alpha frequency activation is known to
decrease with attentional engagement (i.e., in wake states), and
increase in states of low cortical arousal (i.e., during sleep)
(Antonenko & Keil, 2017; Klimesch, 1999). Lower levels of Alpha
power could therefore be expected in redundancy conditions, given
that redundant information requires more CL since the inputs from
both sensory modalities would require more attentional resources,
and thereby increase CL.
2.4 |Eye tracking during learning
van Gog and Scheiter (2010) discussed the use of ET as an additional
tool to study the learning process, particularly for research with multi-
media learning. ET allows researchers to look beyond performance
measures to study what media or representations are visually
attended to by learners, thus giving insight into the origin of well-
known effects such as the redundancy effect or the modality effect.
Note, however, that ET offers no explanation of why participants are
attending to stimuli in a certain order or duration (van Gog &
Scheiter, 2010). One example of how ET was used in the framework
4BACEVICIUTE ET AL.

of CTML is the study by Schmidt-Weigand et al. (2010), which investi-
gated the modality effect with animations wherein explanatory text
was either written or auditory. They found evidence for the split-
attention effect in the written condition. Crucial insight was gained by
viewing tie measure determined by ET (i.e., extracted from fixation
and saccade durations), which revealed how participants in the writing
conditions would begin reading but then are forced to divide their
attention between the text and the animation. While retention, trans-
fer and visual memory task scores did not differ between the two
groups, ET showed how participants in the written text condition
spent most of their time on task fixating on the written text rather
than the animation (Schmidt-Weigand et al., 2010). In their study of
the redundancy effect in multimedia web pages, Liu et al. (2011) also
observed this preference for the written text over the explanatory
image material. The authors found significantly more and longer fixa-
tions in the written text condition than in the auditory condition.
However, the redundancy condition group spent less time fixating on
the text than the written text only group. In a similar methodology,
De Koning et al. (2010) employed ET to measure visual attention allo-
cation via relative fixation times on relevant areas of interest (AOIs).
Total fixation times on AOIs were theorized to be an indication of
greater cognitive processing, and as such longer time spent viewing
was thus generally predicted to cause greater learning (De Koning
et al., 2010). A review of the use of ET in research on learning has
since reinforced this notion (Lai et al., 2013).
Even though gaze measures (i.e., fixation length and duration) are
predominant in ET, other ET measurements have also been investi-
gated in WM load and reading studies. For example, blinking has been
proposed to be indicative of mental load (Holland & Tarlow, 1972),
and researchers observed that blinking decreases during cognitive
processing and memory workload (Holland & Tarlow, 1975). This was
explained by the connection of the visual mental operations and the
visual perceptual system. As such, blinking might be suppressed to
enhance visual processing. Stern and Skelly (1984) tested experimen-
tally whether blinking rate and duration vary depending on task
demand and task modality. In two experiments it was shown that blink
rate is significantly affected by task demand, with higher task demand
causing a lower blinking rate. Furthermore, performing a visual task
led to a lower blinking rate than performing an auditory task. In the
context of textual-auditory redundancy, the expectancy therefore
would be for visually richer representations (i.e., those involving writ-
ten text) to produce lower blink rates than auditory representations.
More recently, a systematic review showed the usefulness of blinking
as a measurement of mental load and mental fatigue (Martins &
Carvalho, 2015). Specifically, Martins and Carvalho (2015) found an
inverse relationship of task difficulty and blinking, that is, higher diffi-
culty results in less blinking. Since redundancy of information is
thought to be more cognitively loading that non-redundant informa-
tion, we could therefore assume lower blink rates with concurrent
information representations rather than when attending to non-
redundant learning content.
Although less investigated, saccadic eye movements (i.e., the vol-
untary movement of an eye between two fixation pints) have also
previously been reported as another ET measure to successfully cap-
ture differences in WM load and cognitive processing. Prior studies
have, for instance, already related increases in velocity and length of
saccadic eye movement to higher task difficulty and conversely that
decreases in saccade velocity might indicate tiredness and lower task
performance (Zagermann et al., 2016). Assuming that redundancy of
information increases CL, we would expect higher saccadic movement
when learning with redundant content. In reading research, saccadic
eye movements have for the most part been investigated over mean-
ingless word strings, providing little support for learning-relevant
investigations (Boland, 2004). No comparative studies have been
produced in listening research.
2.5 |Research Questions
Building on prior research from instructional design, IVR and learning,
as well as novel psychophysiological measurement techniques, we aim
to investigate the following four research questions in this study:
•RQ 1: How does redundant information influence the learning out-
comes of retention and transfer in IVR?
•RQ 2: Are redundant information representations perceived to be
more or less cognitively demanding than non-redundant informa-
tion representations when assessed with self-reported CL
measures in IVR?
•RQ 3: How do cognitive processing demands differ when learning
with redundant and non-redundant information representations in
IVR? How these differences are reflected in EEG Theta and Alpha
frequency band activations?
•RQ 4: Are there any differences in visual attention, as observed by
ET, when learning with redundant and non-redundant information
formats in IVR? Do participants pay more attention to learning
irrelevant stimuli in redundant or in non-redundant information?
3|METHODS
3.1 |Participants
In total, 73 fluent English-speaking and normal-sighted participants
(44 female) without prior knowledge of the presented topic and not
diagnosed with any neurological illness or a learning disorder partook
in the experiment. Participants were 19–41 years old (M =23.97,
SD =3.78) and were recruited via university mailing lists and social
media channels. Partaking in the study was voluntary. Participants
signed an informed consent form prior to the experiment. Permission
for conducting the study was obtained from the institutional board.
Due to errors during ET and EEG data collection (e.g., incomplete data
sets, faulty calibration procedures), data of several participants was
excluded from certain analyses in this study. The final sample size
included in the ET data analysis is 68 participants, and in the EEG data
analysis is 63 participants.
BACEVICIUTE ET AL.5

3.2 |Experimental design
Research questions (Section 2.5) were investigated using a between-
subjects design with three experimental conditions wherein learning
material presented was identical, but its representation varied
(see Figure 1). In the first condition (N=25, 15 female) information was
represented as read-out-load text (auditory condition); in the second con-
dition (N=24, 14 female) the same material was displayed as written text
on an overlay reading interface (written condition). Participants in the last
condition (redundancy condition) received both written and auditory
learning content representations from the first two conditions (N=24,
15 female). Group assignment was randomized prior to arrival of partici-
pants through the use of unique participant IDs. Demographics, prior
knowledge, and reading habits were assessed via a pre-test survey. Learn-
ing outcome variables and CL measures were collected immediately after
the IVR learning experience by subjecting participants to a post-test. Psy-
chophysiological cognitive learning measures (ET and EEG) were recorded
during the entire learning experience, not including the pre- or post-test.
3.3 |Experimental procedures
Each participant was tested individually in an experimental psychology
lab. The experimental procedure was as follows (90 min): (1) partici-
pant briefing, (2) signing an informed consent form, (3) mounting of the
EEG headset, (4) EEG signal quality and impedance test, (5) pre-test sur-
vey, (6) introduction to the VR controls, (7) VR HMD mounting, (8) EEG
signal quality and impedance test (9) VR learning experience (15 min),
(10) dismounting of the EEG and the VR HMD, (11) post-test survey,
(12) participant debriefing. During the VR learning experience, the par-
ticipants were seated and asked to avoid unnecessary movements to
maximize the quality of the psychophysiological recordings. Participants
were rewarded for their participation with a gift card valued at approxi-
mately 15 Euros. The procedure was semi-automated with the help of
the iMotions experiment facilitation software.
3.4 |Materials
Experimental materials consisted of an IVR simulation, a pre-test and a
post-test survey, and psychophysiological measurements (i.e., ET and EEG).
3.4.1 | IVR simulation
The Unity3D game development engine was employed to develop the
IVR simulation used in this study. The simulation was run on the HTC
Vive VR system. To represent current virtual learning content remedi-
ation trends (see Baceviciute et al., 2021) and to control for informa-
tion delivery format, the IVR simulation was designed to consist of
two main components: explicit learning content represented in three
different formats (see Figure 1), and an IVE in which those formats
were embedded in.
The IVE in the simulation was developed to represent a virtual
hospital room in order to establish semantic relations with the learn-
ing content used in the study. To simulate a hospital room scenario,
the IVE was equipped with several, archetypal props (i.e., hospital
cabinets, a painting, a TV screen, etc.), and a soundscape matching the
environmental setting. Two virtual characters, a doctor and a patient,
also populated the simulation. Although explicit learning content was
contained to three explicit learning content representations, the IVE
helped to contextualize learning content (see Baceviciute et al., 2021).
The participant's character was not embodied by a virtual avatar. In
the simulation the participant was seated on a virtual chair. The simu-
lation started with the doctor avatar entering the room. Prior to the
display of the learning content, three information snippets were pro-
vided to introduce the participants to the controls of the simulation
and to explain the experimental task.
Explicit learning content used in the simulation was an expository
science text on the topic of Sarcoma cancer. All of the learning content
was developed based on an information pamphlet provided by a
national cancer society, designed to inform the general public and thus
assumed no prior knowledge of the topic. At the start of the simulation,
participants were tasked to gather information on Sarcoma cancer, as if
they were to retell the information to a friend after the experience.
Adapted learning content was split into 24 snippets of text with the
length of 300–400 characters, each of which delivered a unique piece
of information. Following experimental study design, three different
representation means were developed for representing content snip-
pets. For the written condition a static overlay interface showing the
text was superimposed on the scene (Figure 1). In the auditory condi-
tion, identical learning content was played back as a non-diegetic voice
over. The voice over was produced by recording a voice actor reading
out written snippets of text. Audio was delivered to the participants via
FIGURE 1 Different learning content representations used for the written condition (left), auditory condition (middle) and redundancy
condition (right) [Colour figure can be viewed at wileyonlinelibrary.com]
6BACEVICIUTE ET AL.

built-in HTC Vive headphones. In the redundancy condition both repre-
sentations were present, therefore the audio was played back at the
same time as the text was presented to be read on the overlay inter-
face. Throughout all experimental conditions the order and semantic
representation of the snippets was kept identical. In the two conditions
that included written text representations, visual features (i.e., font
type, line spacing, etc.) and formatting (i.e., paragraph structure, inden-
tation, etc.) of the text were also kept consistent. After each snippet,
the participants signalled that they finished processing the information
by pressing a button on the HTC VR controller. The appearance of the
subsequent snippet of text was triggered by a second button press.
Although the participants were able to control the pace of appearance
of the snippets, learning content presentations was for the most part
sequential, that is, participants could not stop, rewind, or replay a given
snippet. Triggers recorded by button presses later served the secondary
purpose of epoching EEG and ET signals.
3.4.2 | Pre-test survey
The purpose of the pre-test was to capture demographic information,
current reading habits, and the level of prior knowledge about
Sarcoma cancer. The prior knowledge test contained seven questions
on the topic of Sarcoma cancer: one 5-point Likert scale question
assessing the overall familiarity with Sarcoma cancer (i.e., ‘Please indi-
cate how familiar would you consider yourself to be with the topic of
Sarcoma cancer’), and six yes/no questions regarding the specific
concepts related to the learning material (e.g., ‘I know what the two
most common types of sarcoma cancer are’). A total prior knowledge
score was calculated by adding all prior knowledge items together.
Additional survey questions asked participants to report their current
mental state and any use of psychoactive drugs (i.e., caffeine, nicotine
and alcohol) on the day of the experiment.
3.4.3 | Learning assessment instruments
To answer RQ 1 (Section 2.5) two tests were customarily designed to
quantify participants' learning outcomes: a knowledge retention test
consisting of 24 multiple-choice questions (one for each snippet from
the simulation), and a knowledge transfer test consisting of three open-
ended questions. The tests were based on methods previously used in
similar studies (e.g., Makransky et al., 2019a, 2019b; Baceviciute et al.,
2020). The goal of the retention test was to measure how well the par-
ticipants retained the information conveyed in the snippets (e.g., Snippet
text: Bone sarcoma occurs in the body's bone tissue, especially around the
shoulder, knee or hip joints. Question: Which bones are most commonly
affected by bone sarcoma? Multiple choice: (A) Bone sarcomas often occur
around the shoulder, knee or hip joints [correct answer], (B) Bone sarcomas
often occur in the bones around the feet or hands, (C) Bone sarcomas often
occur in or around the elbows or wrists, (D) Bone sarcomas often occur
around the chest and/or the back bones). The transfer test, on the other
hand, required that the participants used the knowledge from the overall
learning experience and applied it to a novel context, measuring com-
prehension of the learnt material (e.g., Imagine the scenario –you are an
oncologist and your patient, who is diagnosed with Sarcoma cancer, is not
responding to your treatment plan, what would your next steps be and
why?). Learners were given 3 min to respond to each question. The
knowledge transfer test was administered first, followed by the knowl-
edge retention test. The knowledge transfer test was coded by two
independent evaluators. These graders anonymously scored each item
by summing up all correctly stated components (1–4 points per answer).
Afterwards, both evaluators were invited to an open discussion panel,
where they settled any discrepancies in their scores. A participant's final
transfer score was then calculated by summing the scores of the three
questions (maximum of 12 points). An individual's score on the knowl-
edge retention test was determined by simply adding the correctly
answered multiple-choice items together (maximum of 24 points).
3.4.4 | Self-reported cognitive load scales
Two measures were used to assess participants' self-reported CL experi-
enced during the immersive VR learning simulation (RQ 2). The first mea-
sure was composed of four widely used individual items in CL research:
an item from Paas (1992) focusing on overall mental effort invested dur-
ing learning, an item from Ayres (2006), probing perceived difficulty of
the learning content, an item from Cierniak et al. (2009) measuring the
perceived difficulty of the provided textual format, and an item from Sal-
omon (1984) where participants reported how well they concentrated
during the learning experience. All items were scored on a 9-point Likert
scale. Secondly, we employed a 10-item validated CL instrument devel-
oped by Leppink et al. (2013). This instrument was comprised of three
items for measuring intrinsic CL, three items measuring extraneous CL,
and four items measuring germane CL (Leppink et al., 2013). Participants
reported their answers on 5-point Likert scales.
3.4.5 | EEG measurement
To further gain insight into cognitive processing during learning (RQ3,
Section 2.5), participants' EEG data was collected using the Advanced
Brain Monitoring (ABM) X-10, wireless 9-channel EEG. This device
samples brain data at a rate of 256 hz. The Ag/AgCl electrodes were
placed at Fz, F3, F4, Cz, C3, C4, POz, P3, P4 and referenced to two
connected mastoids, with impedance levels maintained below 10 kΩ.
EEG data was synchronized with the presentation of the learning
material using the ABM external Sync Unit (ESU) and Cedrus Stim
Tracker. Data collection and storage was handled via iMotions bio-
metric data acquisition software.
EEG data pre-processing was conducted using Matlab's EEGlab
toolkit. First, the raw EEG data was filtered with a high-pass filter
(0.5 Hz) and a low-pass filter (100 Hz). The automatic channel rejec-
tion tool from EEGlab was used to reject channels with improbable
signal distributions (probability z-scores above 5). All electrodes were
re-referenced to average references and line noise was removed at
BACEVICIUTE ET AL.7

50 and 100 Hz using a CleanLine filter. Subsequently, manual visual
inspection was performed wherein all irregular noise activity, such as
short bursts stemming from muscle activity, was removed. Indepen-
dent component analysis (ICA) was further used to remove artefacts
stemming from eye-movements and blinks. Artefact removal proce-
dures were semi-automated by combining thorough visual EEG data
analysis and the MARA algorithm (Multiple Artifact Rejection Algo-
rithm). Lastly, to isolate the sections when the participants were
engaging with the learning material, the continuous stream of EEG
data was epoched using triggers generated by the button presses pro-
duced by the participants.
EEG Power Spectral Density (PSD) estimates were calculated using
the discrete Fourier transform (DFT) with a Hanning window of 1 s
width and 50% overlap, enabled by the NeuroSpec toolbox for
MATLAB (Halliday et al., 1995). The resulting data was normalized and
log-transformed in order to minimize skewness in the dataset and to
standardize unit variance. Following prior work (e.g., Baceviciute et
al., 2021; Baceviciute et al., 2020; Klimesch, 1999), for each frequency
band a mean peak frequency estimate was calculated in SPSS. The fol-
lowing limits were applied: 4–7 Hz for Theta and 8–13 Hz for Alpha.
3.4.6 | Eye tracking (ET) data collection and analysis
materials
In order to investigate RQ4, we employed a HTC Vive with Tobii Pro
eye tracking retrofit hardware, which was digitized at 80 Hz. Before
starting the learning experience, each participant performed a five-
point gaze calibration task designed by Tobii, specifically for use in VR
(Tobii, 2020). This task would be re-run until the calibration outcome
provided by the Tobii SDK showed that a good or excellent calibration
had been achieved. A good calibration required a mean distance of
measured gaze from the target calibration point to be less than
40 pixels, whereas the mean difference threshold for achieving an
excellent calibration was less than 20 pixels. All participants managed
to calibrate within these thresholds.
In this study we particularly focused on collecting real-time gaze
data (i.e., fixation and saccades) and on determining participant's blink-
rate during the learning experience. These measures were collected for
the overall learning experience, as well as for three dynamic AOIs speci-
fied for this study (see Figure 2). The first AOI covered the doctor char-
acter, enabling tracking of how much participants focused on the
virtual agent during the learning experience. The second AOI contained
the overlay reading interface and was thus only present in the interface
and redundancy conditions. This AOI was used to measure how much
time participants spent reading, as well as to estimate the cognitive
effort put into reading. The last AOI was placed over the environmental
props collectively and was used to measure observation of the environ-
ment and extraneous attention paid to task-irrelevant objects.
The ET data was processed using an I-VT filter for gaze analysis and
the gaze-data was mapped to the three pre-defined AOIs. As a means of
investigating where participants directed their gaze and attention during
the simulation, we investigated the time spent looking at the AOIs.
Further, we separated the raw data of the eye-tracker into blinks, fixa-
tions and saccades. Counts were normalized to an average per minute to
account for the variable time in the simulation. We compared the overall
blinking rate and the blinking rate while looking at the interface AOI. To
further compare reading styles between the written and redundancy
conditions, we looked at various metrics regarding their eye-movements.
The four measures were saccades per minute, average saccade ampli-
tude, average saccade distance, and average saccade duration. These
were calculated for the entire simulation and the interface AOI. Further-
more, we investigated data regarding fixations for the entire simulation
and for each respective AOI. Two metrics were derived: average fixation
count per minute and average fixation duration.
3.4.7 | Extraneous attention measure
To further understand visual attention demands when learning with
different information representation displays in IVR (RQ 4), an
extraneous visual attention measure was employed. Six open-
ended questions were asked to probe the participants' attention to
task irrelevant stimuli (i.e., painting, clock, TV screen, and patient
number). The questions were focused on assessing if the partici-
pants could remember specific details about these peripheral
objects in the environment (e.g., Question: ‘There was a painting
hanging across from you in the hospital room - which object was
drawn on the painting?’Answer: ‘Flower/Leaf/Plant’). The number of
correct answers was totalled to a final ‘extraneous attention mea-
sure’(maximum score of 13).
4|RESULTS
A comparison of the three groups on the retention and transfer
scores, extraneous attention measure, CL items and scales, EEG
frequency band averages, and ET measures were calculated using
one-way analyses of variance (ANOVAs) in IBM SPSS 2019. In case of
significant differences, a Tukey's post-hoc t-test was performed.
Effect sizes were estimated by calculating Cohen's Delta. Significance
level was set to 0.05 for all analyses.
4.1 |Did the groups differ on basic characteristics?
Before investigating the four research questions, we determined
whether the three experimental groups differed on basic characteris-
tics. Analyses revealed no significant differences between the groups
in prior knowledge, F
(2,70)
=0.502, p=0.608, reading habits,
F
(2,70)
=0.352, p=0.705, or in familiarity with VR, F
(2,70)
=0.635,
p=0.533. Further, a Chi-square test was used to investigate differ-
ences in the proportion of men and women between the groups. No
significant differences were found in gender distribution, X
2(2,
N=73)
=0.088, p=0.957. As such, the results indicate that there
were no significant differences between the learners in the three
8BACEVICIUTE ET AL.

groups on prior knowledge, basic characteristics and gender composi-
tion prior to the experiment.
4.2 |RQ 1: Did redundancy influence learning
outcomes of retention and transfer?
The first objective (RQ1) of this study was to investigate whether dif-
ferent representations of text in an IVR learning environment affect
participants' learning outcomes, as reflected by a knowledge retention
test and a knowledge transfer test. As can be seen in Table 1, we
found a significant difference between the groups in knowledge
retention, (F
(2,70)
=10.011, p< 0.001). Post-hoc analysis revealed
that the auditory (M=15.48, SD =3.75) condition scored signifi-
cantly lower than written (M=18.67, SD =2.30, p=0.001, d=1.0)
and redundancy (M=18.88, SD =2.68, p< 0.001, d=1.0) groups.
There was no significant difference between the written and redun-
dancy groups (p=0.968). We therefore conclude that participants in
the auditory condition remembered less information than those in the
written or redundancy conditions.
A further ANOVA analyses revealed no significant differences in
transfer test scores between the experimental groups (F
(2,70)
=1.191,
p=0.310). That is, participants in the auditory (M=5.48, SD =2.18),
written (M=6.29, SD =1.78), and redundancy (M=5.96, SD =1.52)
conditions did not differ significantly on their ability to apply the knowl-
edge to a new context as assessed in the transfer test. In conclusion, the
redundancy group performed equally well as the written group on both
learning outcomes; and performed better than the auditory group on the
outcome of retention. This is a major empirical finding of this paper.
4.3 |RQ 2: Did redundancy impact self-reported
cognitive load?
The second goal of the present study was to determine how auditory,
written, or redundant text representation influences the CL of
learners in VR. ANOVA results for all CL items and scales included in
this study are summarized in Table 1. No significant differences were
found on the items measuring mental effort, F
(2,70)
=0.541,
p=0.585, form difficulty, F
(2,70)
=2.790, p=0.068, or
FIGURE 2 AOIs used for ET in this study. Yellow area defines the doctor character AOI, red areas –extraneous attention props AOI, and blue
area - the overlay reading interface AOI [Colour figure can be viewed at wileyonlinelibrary.com]
TABLE 1 ANOVA results of post-test
survey measures comparing auditory,
written and redundancy conditions
Auditory Written Redundancy ANOVA
M SD M SD M SD F df p
Retention 15.48 3.75 18.67 2.30 18.88 2.68 10.011 72 0.000**
Transfer 5.48 2.18 6.29 1.78 5.96 1.52 1.191 72 0.310
Mental effort 6.12 1.17 6.29 1.46 5.92 1.10 0.541 72 0.585
Content diff. 5.64 1.63 4.96 1.12 4.33 1.61 4.819 72 0.011*
Form diff. 4.68 1.68 5.25 1.73 4.13 1.54 2.790 72 0.068
Concentration 6.24 1.23 6.79 1.35 6.42 1.44 1.071 72 0.348
Intrinsic CL 3.40 0.71 3.19 0.79 3.24 0.96 0.431 72 0.651
Extraneous CL 2.71 0.94 3.14 0.99 2.36 0.80 4.330 72 0.017*
Germane CL 3.36 0.86 3.52 1.08 3.73 0.55 1.147 72 0.323
Ex. attention 5.04 2.05 3.83 1.66 2.88 2.15 7.46 72 0.001*
*p< 0.05, **p< 0.001.
BACEVICIUTE ET AL.9

concentration, F
(2,70)
=1.934, p=0.348. A significant difference was
found for content difficulty, F
(2,70)
=4.819, p=0.011, where post-
hoc analysis revealed that participants in the auditory condition
(M=5.64, SD =1.63) rated the content difficulty significantly higher
than in the redundancy group (M=4.33, SD =1.61, p=0.008,
d=0.80). No significant differences were found between the written
condition and the other two conditions.
In addition to these individual items, we measured CL with the
scale from Leppink et al. (2013). We found no significant differences
in self-reported Intrinsic CL, F
(2,70)
=0.431, p=0.651, or Germane
CL, F
(2,70)
=1.147, p=0.323. However, there was a significant differ-
ence in Extraneous CL, F
(2,70)
=4.330, p=0.017. Post-hoc analysis
showed significantly lower scores in the redundancy (M=2.36,
SD =0.80) condition compared to the written condition (M=3.14,
SD =0.99, p=0.012, d=0.86). No significant differences were
observed between the auditory group and the other experimental
groups. We thus conclude that self-reported extraneous CL was lower
in the redundancy group compared with the written group, and that
content was perceived to be more difficult in the auditory condition
than in the redundancy condition.
4.4 |RQ 3: Did cognitive demands differ between
the groups, as observed by EEG measures?
Another aim of this study was to understand if cognitive processing
demands differ when learning with redundant and non-redundant
information representations in IVR (RQ 3). To this end we
investigated between-group differences in mean EEG power. For
each of the frequency bands (i.e., Theta, Alpha), a one-way ANOVAs
compared three experimental groups on mean peak frequencies for
each electrode (Table 2, Figure 3). For mean Theta frequencies a sig-
nificant difference between the groups was observed on every single
electrode (F3, F4, C3, C4, P3, P4, Fz, Cz, POz), p=[1
10
; 0.042]. The
significant differences remained for six of the electrodes (F3, P3, P4,
Fz, Cz, POz), after accounting for multiple comparisons using a
Bonferroni correction (0.05/9 =0.0056). Post-hoc comparisons indi-
cated that significant differences are found between the auditory
and redundancy, and auditory and written conditions, suggesting
lowest cognitive demands in the auditory condition. The written
condition showed no significant differences when compared to the
redundancy in Theta, suggesting no significant difference in cogni-
tive demands when comparing these conditions. No significant dif-
ferences between the groups in mean Alpha band activity were
detected.
4.5 |RQ 4: Are there any differences in visual
attention between conditions?
To understand visual attention allocation (RQ 4), this study investi-
gated between-group differences in several ET measurements: blinks,
fixations and saccades. Group means and ANOVA statistics of all ET
variables are summarized in Table 3. Notably, all comparisons regard-
ing the overlay AOI only concern two groups (i.e., written and
redundancy).
TABLE 2 ANOVA results of EEG
Theta and Alpha measures comparing
auditory, written and redundancy
conditions
Auditory Written Redundancy ANOVA
M SD M SD M SD F df p
Theta F3 0.35 0.23 0.15 0.14 0.16 0.18 7.769 62 0.001*
Theta Fz 0.23 0.14 0.04 0.11 0.06 0.14 13.586 62 0.000**
Theta F4 0.35 0.21 0.19 0.17 0.17 0.20 5.153 62 0.009*
Theta C3 0.31 0.14 0.22 0.18 0.18 0.18 3.353 62 0.042*
Theta Cz 0.15 0.15 0.01 0.09 0.01 0.11 12.602 62 0.000**
Theta C4 0.33 0.13 0.22 0.14 0.21 0.20 4.006 62 0.023*
Theta P3 0.26 0.12 0.11 0.12 0.09 0.13 12.930 62 0.000**
Theta POz 0.22 0.15 0.01 0.10 0.04 0.10 31.247 62 0.000**
Theta P4 0.25 0.12 0.07 0.09 0.07 0.14 15.922 62 0.000**
Alpha F3 0.53 0.27 0.39 0.19 0.45 0.27 1.797 62 0.175
Alpha Fz 0.47 0.20 0.35 0.15 0.43 0.15 2.736 62 0.073
Alpha F4 0.53 0.27 0.43 0.20 0.44 0.26 1.048 62 0.357
Alpha C3 0.38 0.23 0.43 0.23 0.42 0.22 0.419 62 0.659
Alpha Cz 0.39 0.25 0.32 0.14 0.37 0.10 0.774 62 0.466
Alpha C4 0.38 0.22 0.45 0.18 0.44 0.21 0.733 62 0.485
Alpha P3 0.32 0.22 0.31 0.17 0.35 0.17 0.298 62 0.743
Alpha POz 0.33 0.23 0.26 0.11 0.32 0.13 0.953 62 0.391
Alpha P4 0.29 0.20 0.27 0.17 0.33 0.14 0.578 62 0.564
*p < 0.05, ** p < 0.001.
10 BACEVICIUTE ET AL.

To gain an insight into which parts of the simulation the partici-
pants attended to, the percentage of time spent looking at the three
AOIs were compared. These percentages were derived by summariz-
ing participants' fixations and their durations: comparing the total
viewing duration with the duration for each AOI specifically. Signifi-
cant differences in viewing durations were found for all three AOIs.
Firstly, for the doctor AOI (F
(2,65)
=635.766, p< 0.001), a post-hoc
test showed a significant difference between auditory (M=78.59,
SD =14.10) as compared to the written (M=0.36, SD =0.27,
p< 0.001, d=7.84) and redundancy (M=1.73, SD =2.10, p< 0.001,
d=7.62) conditions. Yet, no significant difference was observed
between the written and redundancy (p=0.861) groups. This shows
that participants in the auditory condition spent most of their time
observing the doctor character, while in the learners in the written
and redundancy conditions did not attend to the doctor character as
much. Secondly, the redundancy (M=95.78, SD =4.36) group spent
significantly less time than the written (M=98.12, SD =1.28) group
viewing the overlay AOI (F
(1,41)
=5.829, p=0.020, d=0.73). Never-
theless, these results illustrate that in both conditions participants
spent an average of over 95% of the time on viewing the text,
FIGURE 3 EEG power
comparisons between conditions for
all electrode positions for all
participants in Theta (top) and Alpha
(bottom) frequency bands [Colour
figure can be viewed at
wileyonlinelibrary.com]
BACEVICIUTE ET AL.11

suggesting that students in the redundancy condition spent most of
their time reading. Lastly, a significant difference for the extraneous
task-irrelevant objects AOI (F
(2,65)
=31.642, p< 0.001) was observed
with the auditory (M=9.69, SD =7.46) group spending significantly
more time gazing at the task-irrelevant stimuli than the written
(M=0.68, SD =0.61, p< 0.001, d=1.70) or redundancy (M=0.47,
SD =0.53, p< 0.001, d=1.74) groups. The difference between the
written and redundancy groups was not significant (p=0.987).To
summarize, the learners in the written and redundancy conditions
spent most of the time reading the text, whereas the learners in the
auditory condition spent time attending to the doctor character as
well as the task-irrelevant stimuli. Data from blinks and saccades fur-
ther illustrate whether participants in the written and redundancy
conditions spent their time reading.
The group comparisons for fixations and saccades were con-
ducted between all three groups and between the written and redun-
dancy groups for the overlay AOI specifically. Notably, over the
course of the simulation there were significant differences in fixations
per minute (F
(2,65)
=434.053, p< 0.001). These differences occurred
because the auditory (M=56.01, SD =22.52) group had significantly
fewer, but longer fixations than either written (M=203.95,
SD =19.65, p< 0.001, d=7.00) or redundancy (M=194.26,
SD =14.25, p< 0.001, d=7.34) groups. The difference in fixations
on the overlay interface was marginally not significant (F
(1,41)
=4.096,
p=0.050). Additionally, we observed significant differences in overall
saccade count (F
(2,65)
=107.63, p< 0.001). The post-hoc comparison
revealed that participants in the auditory (M=71.16, SD =28.13)
condition moved their eyes significantly less than those in the written
(M=218.85, SD =24.23, p< 0.001, d=5.60) or redundancy group
(M=242.80, SD =67.75, p< 0.001, d=3.31), with no significant dif-
ference between written and redundancy conditions (p=0.176). Sac-
cades inside the overlay AOI showed no significant difference
between written or redundancy either (F
(1,41)
=2.243, p=0.142).
These findings illustrate further that participants in the auditory con-
dition were focused on the doctor and listened, whereas the learners
in the remaining two conditions read the text on the interface. The
gaze patterns for the Overlay AOI were not significantly different
between written or redundancy representations, which suggests they
were reading in a similar manner.
Finally, we observed a significant difference for average blinks per
minute (F
(2,65)
=8.933, p< 0.001), where a further post-hoc investiga-
tion revealed a significant difference between the auditory (M=14.18,
SD =12.15) and both the written (M=3.93, SD =4.17, p<0.001,
d=1.13) and redundancy (M=7.58, SD =6.05, p=0.028, d=0.69)
groups, and a non-significant difference between written and redun-
dancy (p=0.340). However, the difference in average blinks per
minute for the interface AOI between the written (M=3.98,
SD =4.26) and redundancy (M=7.75, SD =6.31) was significant,
F
(1,41)
=5.302, p=0.026, d=0.69. This means that participants in the
written condition blinked on average less while gazing at the overlay
interface than participants in the redundancy condition. Since eye
blinks typically decrease when reading, this indicates that participants
in the redundancy condition read less than in the written condition;
however, they still spent significantly more time reading than partici-
pants in the auditory condition.
Exploring RQ 4 further, ANOVA results comparing the extraneous
attention measure scores between groups is shown in Table 1. This
data reveals a significant difference between the three conditions
(F
(2,65)
=7.459, p< 0.001). Post-hoc analysis showed that significant dif-
ferences occurred between the auditory (M=5.04, SD =2.05) and
redundancy (M=2.88, SD =2.15) conditions (p =0.001, d=1.03). This
provides evidence that participants in the auditory condition retained
more task-irrelevant information that was present in the environment
than those in the redundancy group. No significant differences were
found between written (M=3.83, SD =1.66) and auditory (p=0.88),
nor between written and redundancy conditions (p=0.217).
TABLE 3 ANOVA results of ET measures comparing auditory, written and redundancy conditions
Auditory Written Redundancy ANOVA
M SD M SD M SD F df p
% of time spent in an AOI
% Doctor 78.59 14.10 0.36 0.27 1.73 2.10 635.766 67 0.000**
% Interface 98.12 1.28 95.78 4.36 5.829 42 0.020*
% Extr. attention 9.69 7.46 0.68 0.61 0.47 0.53 31.642 67 0.000**
Overall fixation counts/min
All fixations/min 56.01 22.53 203.95 19.65 194.26 14.25 434.053 67 0.000**
All saccades/min 71.16 28.13 218.85 24.23 242.80 67.75 107.63 67 0.000**
All blinks/min 14.18 12.15 3.93 4.17 7.58 6.05 8.933 67 0.000**
Interface AOI measures
Int. fixations/min ––202.34 19.24 191.78 14.52 4.096 42 0.050
Int. saccades/min ––215.68 23.50 237.89 65.29 2.243 42 0.142
Int. blinks/min –– 3.98 4.26 7.75 6.31 5.302 42 0.026*
*p< 0.05, **p< 0.001.
12 BACEVICIUTE ET AL.

5|DISCUSSION
5.1 |Empirical contributions
The first major finding in this study relates to RQ 1, which investi-
gated the effects of redundancy on learning outcome measures of
knowledge retention and transfer. Contrary to traditional assumptions
summarized in CTML about the redundancy principle in non-
immersive 2D media, our results showed no decrease in learning
outcomes when learning information was presented in a redundant
format in IVR. These results indicate that learners remembered facts
and were able to utilize knowledge learned with the same efficiency
in redundant information representations as in non-redundant infor-
mation representations. Our findings highlighting the advantage of
redundancy representations over auditory representations go hand-
in-hand with the conclusions summarized in a meta-analysis by
Adesope and Nesbit (2012). Even though their findings were in the
realm of 2D media, given that similar results for redundancy were
found in low prior knowledge learners, in system-paced learning mate-
rials, and picture free-materials, it could be argued that all of these
situations represent more complex learning environments, drawing
parallels to IVR. This might imply that redundancy of learning content
in more complex learning environments (e.g., IVR) could in fact be
beneficial for learning, as opposed to redundancy in customary and
less complex media systems (e.g., power point presentations, book
illustrations).
Furthermore, highlighting differences in learning outcomes
between auditory and written information representations, our study
replicates results obtained of prior research (Baceviciute et al., 2020),
wherein auditory information was likewise found to be inferior to
written information in terms of knowledge retention, but not knowl-
edge transfer. Referencing Mayer (2014, 2020) and Baceviciute et
al., (2020), attribute this finding to the transient nature of auditory
information. According to the authors, when learning with auditory
content, participants might not have been able to engage in WM pro-
cesses as successfully as in conditions involving textual representa-
tions, where the participants were able to more easily repeat and
integrate information. They argue that in complex environments, such
as IVR, there might be a greater need to anchor learning than in sim-
pler 2D learning scenarios (Baceviciute et al., 2020).
In regards to self-reported CL outcomes addressed in RQ2, results
show that redundant information representations were not perceived
to be more cognitively demanding than non-redundant information
representations, as observed with both single-item CL items and with
the validated Leppink et al. (2013) instrument. In fact, with the latter
measure, redundant content was found to be least extraneously load-
ing (significantly when compared to written representations). Since no
differences between written and redundant information representa-
tions were observed in learning outcomes, this shows that in this
study, the participants might have used corresponding information
representations more as an aid, rather than perceiving them as an
additive strain to their learning. In addition to that, supporting findings
reported by Baceviciute et al., (2020), our results show that learning
content presented in an auditory representation format was perceived
to be the most difficult from which to learn as compared to other for-
mats. This once again can be attributed to the transient nature of
auditory content, which might influence learner's perceptions of that
content despite the fact that no content manipulations were actually
introduced in the experiment.
Another major finding of this study comes from the obtained EEG
estimates for the Theta frequency band. Specifically, we observed sig-
nificant differences between the redundancy information representa-
tion format and the auditory representation format, and between the
written representation format and the auditory representation format
in the Theta band. Since overall higher Theta activation is normally
associated with increased cognitive load, our results hint that redun-
dancy and written conditions required more mental effort from the
participants when learning in those formats. Previous work in 2D
media has hypothesized that the need to combine redundant informa-
tion sources generates strong demands on the learner's WM capacity,
and therefore it is more difficult for students to remember the infor-
mation acquired (Mayer, 2014, 2020). From our EEG results we see
that as compared to auditory information processing, the participants
did invest more cognitive capacity in redundant information
processing. However, since there was no difference in the EEG Theta
band activity between redundant format and written-only format, we
can assume that the difference in cognitive processing observed when
compared to the auditory condition was not attributed to information
redundancy per se, but is rather a difference that can be ascribed to
the high cognitive demands imposed by written information. In this
direction, Baceviciute et al., (2020) have also found that reading
(as compared to listening) yields overall higher levels of mental work-
load, suggesting that reading might simply be a more cognitively
demanding process than listening. Interestingly, in this study we did
not find any significant differences between conditions in the alpha
frequency band, although it has typically also been described as a reli-
able measure of cognitive demands (Klimesch, 1999). Prior literature
reports that changes in theta but not alpha can be associated with
impairments in WM (e.g., Goodman et al., 2019). In the current study,
this could suggest that written content is not necessarily more cogni-
tively loading, when compared to auditory content, but that it does
impose additional demands on the learner's WM load during learning.
Another major contribution of this study stems from the viewing
duration results for the interface AOI obtained by the ET measures
(RQ 4). Results indicate that participants in both redundancy and writ-
ten conditions spent more than 95% of their time fixated on the inter-
face AOI –a virtual element that was used to display text in the IVR
environment. Contrary to what was previously assumed by Moreno
and Mayer's (2002) study which hypothesized that learners listen and
do not read under redundancy conditions, this shows that participants
still spent most of their time reading content, when both information
representation formats were available. This fact is also supported by
our results obtained from fixation and saccade measures, which both
showed significant differences between the auditory format and
both written representation formats, but not between the redundancy
and written conditions. Similarly, prior literature has also reported
BACEVICIUTE ET AL.13

lower blink rates during visual information processing (Stern &
Skelly, 1984) which was also observed in our study, once again
suggesting that in redundancy participants continue to engage in the
process of reading. These results support previous findings produced
by Schmidt-Weigand et al. (2010) and Liu et al. (2011), who suggest
that when text is placed in front of learners it encapsulates the major-
ity of their attentional resources, not leaving much attentional capac-
ity to engage in other activity (e.g., engage in animations or images).
This is supported by the results from the extraneous attention mea-
sure, as well as time spent on extraneous task-irrelevant objects AOI.
These results showed that the learners in the auditory condition
engage in environmental observations significantly more than learners
in the conditions involving text, which supports the cognitively
demanding nature of a reading task. Significantly higher saccadic eye
movement for both reading conditions found in this study also speaks
to this claim.
Even though most of the participant's time and attentive
resources were spent on reading written content, we did observe sig-
nificant effects in viewing times between redundancy and written
conditions, differentiating written only and written-auditory informa-
tion representations. Firstly, results show that participants in the
redundancy condition spent significantly less time fixating on
the interface AOI than in the written condition. In addition to that,
higher blink rates were found in the redundancy condition. Both of
these findings hint that participants did read less in the redundancy
condition. This, together with the lack of difference observed
between the two textual conditions in the learning results, as well as
in the EEG results, suggests some of their cognitive resources from
the visual modality were most likely successfully offloaded to the
auditory modality.
Lastly, another finding in this study comes from the viewing dura-
tion results for the doctor character AOI, which showed significantly
longer viewing duration times for this AOI in the auditory condition as
compared with two other conditions. Interestingly, even though the
audio recorded in this simulation was not tied to the doctor character,
this implies that participants in the auditory condition were using this
character as an anchor point for grounding their attention while listen-
ing to the auditory information. This confirms the assumptions made
by Baceviciute et al. (2020), which suggested that in complex learning
environments there might be a psychological need to ground transient
auditory information.
5.2 |Limitations and future work
In this study our focus was set on investigating written-auditory
redundancy. However, future studies should investigate different
forms of information redundancy, as it is not clear if findings obtained
in this study would generalize to more diverse contexts. In this study
we purposefully did not embed any learning information in the sur-
rounding IVRE. However, considering that presence in a simulated
world is perhaps among the most powerful affordances offered by
IVREs (Makransky et al., 2021), future studies should consider how
learning information could visually be embedded in an IVRE. This
would allow researchers to investigate different forms of information
redundancy and explore how picture/text redundancy, traditionally
described in 2D media, can be generalized in IVREs. In general, CAMIL
describes how presence and agency are the main affordances of learn-
ing in IVR. By investigating the redundancy principle in this study, we
focus on the role of cognitive load and information processing when
learning in IVR. CAMIL also describes how presence and agency can
lead to more learning through high levels of embodiment. The level of
interaction in the IVR used in this study was quite limited, therefore, it
did not fully take advantage of the affordance of agency, or embodi-
ment which is possible in IVR. Therefore, future research should con-
sider how instructional design features (such as redundant
information) generalize to more interactive learning environments that
make better use high levels of presence and agency which are the
main affordances of learning in IVR.
Furthermore, since we observed that some of the information
was successfully offloaded to the auditory channel, it could be useful
to investigate different written-auditory information couplings, with
varying degrees of auditory-written text correspondence (for instance,
if only some information was presented in a written format, or in an
auditory format). These would lean more towards the signalling effect
described by CTML, wherein information presented in two different
modalities is not fully redundant, but instead is used for emphasizing
and cuing information processing in the other modality. Investigations
with varying degrees of text-audio correspondence have already been
proposed by the review study carried out by Adesope and
Nesbit (2012) for redundancy in 2D media. Less textually-dense
redundancy conditions could especially be relevant for IVR, where
textual representations are typically deemed to be impractical, and
not fully encompassing true power of the immersive media.
In this study, relatively short paragraphs of text were used for the
investigation. Some studies have argued that text length might influ-
ence the redundancy effect (Mayer, 2014), suggesting that future
studies should include investigations on how text length might influ-
ence redundant information processing. In a similar vein, some studies
have suggested that prior knowledge of the learner might influence
the redundancy principle. Specifically, redundancy effects are said to
be heightened in novice learners, as they need to utilize more cogni-
tive processing capacity due to the novelty of learned information
(Mayer, 2014). In our study we controlled for prior knowledge, as all
participants were novice learners. Nevertheless, information that we
used was relatively simple, targeted towards a general-population of
learners. It could therefore, be interesting and pertinent to investigate
if and how redundancy effects generalize to IVR, with increasing
information complexity, and when comparing novice and advanced
learners.
Considering our ET results, which emphasized the cognitively
loading nature of textual information; as well as our EEG findings,
which indicated higher WM demands in both written conditions, we
can make a general assumption that the moment that there is written
information placed in front of learners, they will spend time on it and
read it. This might not be the case in non-learning scenarios, or in
14 BACEVICIUTE ET AL.

scenarios where text is not the essence of the IVR situation. Future
studies should therefore investigate whether these findings translate
to situations wherein written text plays a supporting role, rather
than being at the core of learning. Similarly, this study was solely
focused on healthy learner population and did not consider
learners of different learning backgrounds and styles. As such,
future research should also investigate how underprivileged
learner populations (e.g., learner's with special needs, and learning
disorders, such as ADHD, ADD, Dyslexia, etc.), as well as learners
with different learning backgrounds and styles process written
and auditory information in IVR.
Nevertheless, considering the unique affordances of IVR, such as
presence and agency, (Makransky et al., 2019b, Makransky &
Petersen, 2021; Jensen & Konradsen, 2018; Mikropoulos &
Natsis, 2011), and practical complexities surrounding the development
of this technology, we invite future researchers and instructional
designers to extend their investigations beyond traditional textual and
auditory information representations, and focus more on studying the
efficacy of visual, embodied and dynamic representation forms, that
might be more suited for this complex new learning medium.
6|CONCLUSION
This article summarized a between-subjects experiment, investi-
gating the redundancy principle in an IVR environment for learn-
ing. Results for learning outcomes and various self-reported and
psychophysiological measures of CL indicate that the redundancy
principle might not generalize to immersive technology as origi-
nally anticipated in non-immersive media research. Instead, find-
ings show that when attending to redundant learning content in
immersive environments, learners use less cognitive processing
capacity without compromising learning efficacy. The results
therefore imply that redundancy of learning content in more com-
plex learning environments such as IVR could in fact be beneficial
for learning. This finding also suggests that instructional design
principles, originally discovered in traditional 2D media, might not
directly translate to IVR, calling for further research in the field of
instructional design for immersive media systems.
PEER REVIEW
The peer review history for this article is available at https://publons.
com/publon/10.1111/jcal.12595.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the
corresponding author upon reasonable request.
ORCID
Sarune Baceviciute https://orcid.org/0000-0002-9995-8045
Gordon Lucas https://orcid.org/0000-0002-5626-6890
Guido Makransky https://orcid.org/0000-0003-1862-7824
REFERENCES
Adesope, O. O., & Nesbit, J. C. (2012). Verbal redundancy in multimedia
learning environments: A meta-analysis. Journal of Educational Psychol-
ogy,104(1), 250.
Antonenko, P., Paas, F., Grabner, R., & Van Gog, T. (2010). Using electro-
encephalography to measure cognitive load. Educational Psychology
Review,22(4), 425–438.
Antonenko, P. D., & Keil, A. (2017). Assessing working memory dynamics
with electroencephalography: Implications for research on cognitive
load. In I. R. Z. Zheng (Ed.), Cognitive load measurement and application
(pp. 93–111). Routledge.
Ayres, P. (2006). Using subjective measures to detect variations of intrinsic
cognitive load within problems. Learning and Instruction,16(5),
389–400.
Baceviciute, S., Mottelson, A., Terkildsen, T., & Makransky, G. (2020, April).
Investigating representation of text and audio in educational VR using
learning outcomes and EEG. In Proceedings of the 2020 CHI conference
on human factors in computing systems (pp. 1–13).
Baceviciute, S., Terkildsen, T., & Makransky, G. (2021). Remediating learn-
ing from non-immersive to immersive media: Using EEG to investigate
the effects of environmental embeddedness on reading in Virtual Real-
ity. Computers & Education,164, 104122.
Bogusevschi, D., Muntean, C. H., & Muntean, G. M. (2019). Earth course:
Knowledge acquisition in technology enhanced learning STEM educa-
tion in primary school. In EdMedia+Innovate learning (pp. 1243–1252).
Association for the Advancement of Computing in Education (AACE).
Boland, J. E. (2004). Linking eye movements to sentence comprehension
in reading and listening. In I. C. Manuel & J. C. Clifton (Eds.), The on-line
study of sentence comprehension: Eyetracking, ERP, and beyond
(pp. 51–76). Pychology Press.
Brouwer, A. M., Hogervorst, M. A., Van Erp, J. B., Heffelaar, T.,
Zimmerman, P. H., & Oostenveld, R. (2012). Estimating workload using
EEG spectral power and ERPs in the n-back task. Journal of Neural
Engineering,9(4), 045008.
Butussi, F., & Chittaro, L. (2018). Effects of different types of virtual reality
display on presence and learning in a safety training scenario. IEEE
Transactions on Visualization and Computer Graphics,24(2), 1063–1076.
Chittaro, L., & Buttussi, F. (2015). Assessing knowledge retention of an
immersive serious game vs. a traditional education method in aviation
safety. IEEE Transactions on Visualization and Computer Graphics,21(4),
529–538.
Christensen, R., & Knezek, G. (2016). Blending formal and informal learning
through an online immersive game environment: Contrasts in interac-
tions by middle school boys and girls. In Society for Information Tech-
nology & Teacher Education International Conference (pp. 535–540).
Association for the Advancement of Computing in Education (AACE).
Cierniak, G., Scheiter, K., & Gerjets, P. (2009). Explaining the split-attention
effect: Is the reduction of extraneous cognitive load accompanied by
an increase in germane cognitive load? Computers in Human Behavior,
25(2), 315–324.
Craig, S. D., Gholson, B., & Driscoll, D. M. (2002). Animated pedagogical
agents in multimedia educational environments: Effects of agent prop-
erties, picture features and redundancy. Journal of Educational Psychol-
ogy,94(2), 428.
Cummings, J. J., & Bailenson, J. N. (2016). How immersive is enough? A
meta-analysis of the effect of immersive technology on user presence.
Media Psychology,19(2), 272–309.
De Koning, B. B., Tabbers, H. K., Rikers, R. M., & Paas, F. (2010). Attention
guidance in learning from a complex animation: Seeing is understand-
ing? Learning and Instruction,20(2), 111–122.
Fowler, C. (2015). Virtual reality and learning: Where is the pedagogy?
British Journal of Educational Technology,46(2), 412–422.
Frey, J., Mühl, C., Lotte, F., & Hachet, M. (2014). Review of the use of elec-
troencephalography as an evaluation method for human-computer
BACEVICIUTE ET AL.15

interaction. In PhyCS 2014 - International Conference on Physiological
Computing Systems (2014). Lisbon, Portugal: SCITEPRESS.
Gerjets, P., Scheiter, K., Opfermann, M., Hesse, F. W., & Eysink, T. H.
(2009). Learning with hypermedia: The influence of representational
formats and different levels of learner control on performance and
learning behavior. Computers in Human Behavior,25(2), 360–370.
Goodman, M., Zomorrodi, R., Kumar, S., Barr, M., Daskalakis, Z.,
Blumberger, D., Fischer, C., Flint, A., Mah, L., Herrmann, N., &
Pollock, B. (2019). Changes in theta but not alpha modulation are asso-
ciated with working memory impairments in Alzheimer's dementia and
mild cognitive impairment. Biological Psychiatry,85(10), 213–214.
Halliday, D. M., Rosenberg, J. R., Amjad, A. M., Breeze, P., Conway, B. A., &
Farmer, S. F. (1995). A framework for the analysis of mixed time
series/point process data-theory and application to the study of physi-
ological tremor, single motor unit discharges and electromyograms.
Progress in Biophysics and Molecular Biology,64(2), 237–278.
Holland, M. K., & Tarlow, G. (1972). Blinking and mental load. Psychological
Reports,31(1), 119–127.
Holland, M. K., & Tarlow, G. (1975). Blinking and thinking. Perceptual and
Motor Skills,41(2), 403–406.
Howard, M. (2019). Virtual reality interventions for personal development:
A meta-analysis of hardware and software. Human–Computer Inter-
action,34(3), 205–239.
Huang, W., Roscoe, R. D., Johnson-Glenberg, M. C., & Craig, S. D. (2020).
Motivation, engagement, and performance across multiple virtual real-
ity sessions and levels of immersion. Journal of Computer Assisted
Learning,37(3), 745–758.
Jensen, L., & Konradsen, F. (2018). A review of the use of virtual reality
head-mounted displays in education and training. Education and Infor-
mation Technologies,23(4), 1515–1529.
Johnson-Glenberg, M. C. (2019). The necessary nine: Design principles for
embodied VR and active STEM education. In Learning in a digital world
(pp. 83–112). Springer.
Jones, N. (2018). Simulated labs are booming (pp. S5–S7). Nature Outlook:
Science and Technology Education.
Kalyuga, S., Chandler, P., & Sweller, J. (2004). When redundant on-screen
text in multimedia technical instruction can interfere with learning.
Human Factors,46(3), 567–581.
Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and
memory performance: A review and analysis. Brain Research Reviews,
29(2-3), 169–195.
Klingenberg, S., Jørgensen, M. L., Dandanell, G., Skriver, K., Mottelson, A.,
& Makransky, G. (2020). Investigating the effect of teaching as a
generative learning strategy when learning through desktop and
immersive VR: A media and methods experiment. British Journal of
Educational Technology,51(6), 2115–2138.
Lai,M.L.,Tsai,M.J.,Yang,F.Y.,Hsu,C.Y.,Liu,T.C.,Lee,S.W.,…Tsai, C. C.
(2013). A review of using eye-tracking technology in exploring learning
from 2000 to 2012. Educational Research Review,10,90–115.
Leppink, J., Paas, F., Van der Vleuten, C. P., Van Gog, T., & Van Merriënboer, J. J.
(2013). Development of an instrument for measuring different types of cog-
nitive load. Behavior Research Methods,45(4), 1058–1072.
Liu, H. C., Lai, M. L., & Chuang, H. H. (2011). Using eye-tracking technol-
ogy to investigate the redundant effect of multimedia web pages on
viewers' cognitive processes. Computers in Human Behavior,27(6),
2410–2417.
Luo, H., Li, G., Feng, Q., Yang, Y., & Zuo, M. (2021). Virtual reality in K-12
and higher education: A systematic review of the literature from 2000
to 2019. Journal of Computer Assisted Learning,37(3), 887–901.
Makransky, G., & Lilleholt, L. (2018). A structural equation modeling investiga-
tion of the emotional value of immersive virtual reality in education. Edu-
cational Technology Research and Development,66(5), 1141–1164.
Makransky, G., Borre-Gude, S., & Mayer, R. E. (2019a). Motivational and
cognitive benefits of training in immersive virtual reality based on
multiple assessments. Journal of Computer Assisted Learning,35(6),
691–707.
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019b). Adding immersive
virtual reality to a science lab simulation causes more presence but
less learning. Learning and Instruction,60, 225–236.
Makranksy, G., Wismer, P., & Mayer, R. (2019c). A Gender Matching Effect
in Learning with Pedagogical Agents in an Immersive Virtual Reality
Science Simulation. Journal of Computer Assisted Learning,35(3),
349–358.
Makransky, G., & Petersen, G. (2019). Investigating the process of learning
with desktop virtual reality: a structural equation modeling approach.
Computers & Education,134,15–30.
Makransky, G., Petersen, G. B., & Klingenberg, S. (2020). Can an immersive
virtual reality simulation increase students’interest and career
aspirations in science? British Journal of Educational Technology,51(6),
2079–2097.
Makransky, G., & Petersen, G. B. (2021). The cognitive affective model of
immersive learning (CAMIL): a theoretical research-based model of
learning in immersive virtual reality. Educational Psychology Review,
1–22.
Makransky, G., Andreasen, N. K., Baceviciute, S., & Mayer, R. E. (2021).
Immersive virtual reality increases liking but not learning with a sci-
ence simulation and generative learning strategies promote learning in
immersive virtual reality. Journal of Educational Psychology,113(4),
719–735.
Makransky, G. (2021). The immersion principle in multimedia learning. In
R. E. Mayer & L. Fiorella (Eds.), The Cambridge handbook of multimedia
learning (3rd ed.). Cambdridge University Press.
Meyer, O. A., Omdahl, M. K., & Makransky, G. (2019). Investigating the
effect of pre-training when learning through immersive virtual reality
and video: A media and methods experiment. Computers & Education,
140, 103603.
Martins, R., & Carvalho, J. M. (2015). Eye blinking as an indicator of fatigue
and mental load - A systemematic review. In Occupational Safety and
Hygiene III, p. 10.
Mayer, R. E. (2014). The Cambridge handbook of multimedia learning. Cam-
bridge University Press.
Mayer, R. E. (2020). Multimedia learning. Cambridge University Press.
Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multi-
media learning: When presenting more material results in less under-
standing. Journal of Educational Psychology,93(1), 187.
Mayer, R. E., & Johnson, C. I. (2008). Revising the redundancy principle in
multimedia learning. Journal of Educational Psychology,100(2), 380.
Mikropoulos, T. A., & Natsis, A. (2011). Educational virtual environments:
A ten-year review of empirical research (1999–2009). Computers &
Education,56(3), 769–780.
Moreno, R., & Mayer, R. E. (2002). Verbal redundancy in multimedia
learning: When reading helps listening. Journal of Educational Psychol-
ogy,94(1), 156–163.
Mühl, C., Heylen, D., & Nijholt, A. (2015). Affective brain-computer inter-
faces: Neuroscientific approaches to affect detection. In Oxford hand-
book of affective computing (pp. 217–232). Oxford University Press.
Muller Queiroz, A. C., Moreira Nascimento, A., Tori, R., Brashear
Alejandro, T., Veloso de Melo, V., de Souza Meirelles, F., & da Silva
Leme, M. I. (2018). Immersive virtual environments in corporate
education and training. In Twenty-fourth Americas Conference on Infor-
mation Systems (pp. 1–10). Association for Information Systems.
Mütterlein, J., & Hess, T. (2017). Immersion, presence, interactivity: Towards
a joint understanding of factors influencing virtual reality acceptance and
use. In Twenty-third Americas Conference on Information Systems.
Association for Information Systems, Boston, MA.
Örün, Ö., & Akbulut, Y. (2019). Effect of multitasking, physical environ-
ment and electroencephalography use on cognitive load and retention.
Computers in Human Behavior,92, 216–229.
16 BACEVICIUTE ET AL.

Osipova, D., Takashima, A., Oostenveld, R., Fernández, G., Maris, E., & Jensen, O.
(2006). Theta and gamma oscillations predict encoding and retrieval of
declarative memory. Journal of Neuroscience,26(28), 7523–7531.
Paas, F. G. (1992). Training strategies for attaining transfer of problem-
solving skill in statistics: A cognitive-load approach. Journal of Educa-
tional Psychology,84, 429–434.
Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual
reality. Journal of Educational Psychology,110(6), 785.
Petersen, G. B., Klingenberg, S., Mayer, R. E., & Makransky, G. (2020). The
virtual field trip: Investigating how to optimize immersive virtual learn-
ing in climate change education. British Journal of Educational Technol-
ogy,51(6), 2099–2115.
Puma, S., Matton, N., Paubel, P. V., Raufaste, 
E., & El-Yagoubi, R. (2018).
Using theta and alpha band power to assess cognitive workload in
multitasking environments. International Journal of Psychophysiology,
123, 111–120.
Raditanti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A
systematic review of immersive virtual reality applications for higher
education: Design elements, lessons learned, and research agenda.
Computers & Education,147, 103778.
Richards, D., & Taylor, M. (2015). A Comparison of learning gains when
using a 2D simulation tool versus a 3D virtual world: An experiment to
find the right representation involving the Marginal Value Theorem.
Computers & Education,86, 157–171.
Salomon, G. (1984). Television is “easy”and print is “tough”: The differen-
tial investment of mental effort in learning as a function of perceptions
and attributes. Journal of Educational Psychology,78, 647–658.
Scharinger, C. (2018). Fixation-related EEG frequency band power analysis: A
promising methodology for studying instructional design effects of multi-
media learning material. Frontline Learning Research,6(3), 56–71.
Schmidt-Weigand, F., Kohnert, A., & Glowalla, U. (2010). A closer look at
split visual attention in system-and self-paced instruction in multime-
dia learning. Learning and Instruction,20(2), 100–110.
Stern, J. A., & Skelly, J. J. (1984). The eye blink and workload consider-
ations. In Proceedings of the Human Factors Society Annual Meeting
(Vol. 28, pp. 942–944). SAGE Publications.
Sweller, J. (2011). Cognitive load theory. In Psychology of learning and
motivation (Vol. 55, pp. 37–76). Academic Press.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Measuring cognitive load. In
Cognitive load theory (pp. 71–85). Springer.
Tang, Y. M., Ng, G. W. Y., Chia, N. H., So, E. H. K., Wu, C. H., & Ip, W. H.
(2020). Application of virtual reality (VR) technology for medical practi-
tioners in type and screen (T&S) training. Journal of Computer Assisted
Learning,7(2), 359–369.
Tobii. (2020). Tobii PRO SDK calibration. http://developer.tobiipro.com/
commonconcepts/calibration.html
van Gog, T., & Scheiter, K. (2010). Eye tracking as a tool to study
and enhance multimedia learning. Learning and Instruction,2(20),
95–99.
Wu, B., Yu, X., & Gu, X. (2020). Effectiveness of immersive virtual reality
using head-mounted displays on learning performance: A meta-analy-
sis. British Journal of Educational Technology,51(6), 1991–2005.
Zagermann, J., Pfeil, U., & Reiterer, H. (2016). Measuring cognitive load
using eye tracking technology in visual computing. In Proceedings of
the sixth workshop on beyond time and errors on novel evaluation
methods for visualization (pp. 78–85). ACM.
How to cite this article: Baceviciute, S., Lucas, G., Terkildsen,
T., & Makransky, G. (2021). Investigating the redundancy
principle in immersive virtual reality environments: An
eye-tracking and EEG study. Journal of Computer Assisted
Learning,1–17. https://doi.org/10.1111/jcal.12595
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