Exploring the role of learning activity when learning with video and 
virtual reality: a mixed methods study with airport security officers 
 
MASTER THESIS 
2021 
 
Kaspar Kaufmann 
 
 
 
 
 
 
  
Co-Supervisor: Co-Supervisor: 
Prof. Dr. Adrian Schwaninger Thomas Wyssenbach 
 
Institute Humans in Complex Systems 
School of Applied Psychology 
University of Applied Sciences and Arts Northwestern Switzerland FHNW 
Olten, Switzerland  
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Abstract 
The present study explored how media (video vs. virtual reality) and learning activity (passive 
vs. interactive) affect airport security screeners’ learning experiences by applying a 2 x 2 
factorial between-subjects design. A mixed methods approach was employed to assess the 
screeners’ (n = 26) learning, cognitive load, intrinsic motivation, and technology acceptance. 
Results showed that videos led to slightly higher learning outcomes than virtual reality. While 
screeners believed interactivity to enhance learning, no main effect was discovered. This result 
may have been influenced by increased cognitive load experienced by the screeners through 
interactivity. Intrinsic motivation was significantly higher for screeners learning with interactive 
video, passive virtual reality, and interactive virtual reality compared to passive video. Regarding 
technology acceptance, screeners perceived virtual reality and interactivity to be more useful 
than video and passivity, respectively. Overall, this study offers insight into the potentials of 
multimedia for learning in a practical setting. 
 Keywords: virtual reality, video, multimedia learning, cognitive load, intrinsic 
motivation, technology acceptance 
  
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Zusammenfassung 
Diese Studie untersuchte, wie Medien (Video vs. virtuelle Realität) und Lernaktivität 
(passiv vs. interaktiv) die Lernerfahrungen von Flughafensicherheitsbeauftragten (Screener) 
beeinflussen, indem ein 2 x 2-faktorielles between-subjects Design angewendet wurde. Mit 
einem Mixed-Methods-Ansatz wurde das Lernen, die kognitive Belastung, die intrinsische 
Motivation und die Technologieakzeptanz der Screener (n = 26) untersucht. Die Ergebnisse 
zeigten, dass Videos zu leicht höheren Lernergebnissen führten als virtuelle Realität. Obwohl die 
Screener glaubten, dass Interaktivität das Lernen unterstützt, wurde kein statistischer Haupteffekt 
gefunden. Dieses Ergebnis wurde möglicherweise durch erhöhte kognitive Belastung beeinflusst. 
Die intrinsische Motivation war signifikant höher bei Screenern, die mit interaktiven Videos, 
passiver virtueller Realität und interaktiver virtueller Realität lernten als mit passiven Videos. 
Hinsichtlich der Technologieakzeptanz empfanden die Screener die virtuelle Realität und die 
Interaktivität als nützlicher als Videos bzw. Passivität. Insgesamt bietet diese Studie einen 
Einblick in die Potenziale von multimedialem Lernen in einem praxisbezogenen Kontext. 
Keywords: virtuelle Realität, Video, multimediales Lernen, kognitive Belastung, 
intrinsische Motivation, Technologieakzeptanz 
  
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Exploring the role of learning activity when learning with video and virtual reality: 
a mixed methods study with airport security officers 
With one technological innovation following on the heels of another, possibilities of 
instructional media are steadily evolving. For videos, improved and simplified recording, editing, 
and broadcasting have already led to the mainstream adoption of the medium in formal and 
informal learning environments (de Koning et al., 2018). For immersive virtual reality (VR), on 
the other hand, the prospects of the novel technology are still being explored as it has only 
recently reached consumer-level affordability (Rupp et al., 2019). While both video and VR offer 
unique affordances, they assume the same multimedia principle of using words and pictures for 
instruction (Mayer, 2009). 
An understudied facet of multimedia learning has been the role of interactivity which 
promises enhanced learning effectiveness (Evans & Gibbons, 2007). However, allowing more 
learning activity in a rich virtual environment (VE) of VR may also overwhelm learners and 
distract from the task at hand, thus leading to higher cognitive load (e.g., Knight & Tlauka, 2017; 
Makransky, Terkildsen, et al., 2019; Parong & Mayer, 2018). Apart from cognitive aspects, 
motivational factors and learners’ acceptance of the medium play an important role in 
understanding the utility and impact of the medium when it is applied in an educational context. 
It has been shown that learners generally experience more intrinsic motivation when an activity 
is interesting or enjoyable (Ryan & Deci, 2000). The higher intrinsic motivation may 
consequently improve learning outcomes (Ryan & Deci, 2009) and working memory capacity 
(Schnotz & Kürschner, 2007). Further, learners’ technology acceptance is vital for successfully 
implementing any learning medium (Donkor, 2011). 
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Therefore, the present study explores media (video vs. VR) and learning activity (passive 
vs. interactive) using a 2 x 2 factorial design. By assessing learning outcomes, cognitive load, 
intrinsic motivation, and technology acceptance in a practical setting, valuable insights into the 
use of passive and interactive video and VR are offered. 
Video and VR for learning 
The use of videos in educational settings has increased considerably in the past decades, 
making videos one of today’s most prevalent teaching and learning methods (de Koning et al., 
2018). Due to the wide variety of video types and platforms available, learners of all educational 
levels and settings can access videos (de Koning et al., 2018). In formal learning environments, 
videos have recently found use in combination with other forms of instruction. Thus, videos 
serve as a key component for blended learning (e.g., Coyne et al., 2018), e-learning (e.g., Zhang 
et al., 2006), massive online open courses (e.g., Watson et al., 2017), and flipped classrooms 
(e.g., DeLozier & Rhodes, 2017). Accordingly, interest in instructional video research has been 
rekindled, joining the topics of animation, simulation and VR, which have dominated 
educational research in recent years (Bétrancourt & Benetos, 2018). 
VR is defined as “a computer-mediated simulation that is three-dimensional, 
multisensory, and interactive, so that the user’s experience is ‘as if’ inhabiting and acting within 
an external environment” (Burbules, 2006, p. 37). Typically, VR is differentiated into immersive 
and non-immersive VR (Parong & Mayer, 2018). Immersion is an objective measure dependent 
on the sensory fidelity of a VR system and the extent to which it shuts out the outside world 
(Cummings & Bailenson, 2016). For the present study, VR refers to immersive VR which differs 
from non-immersive VR in hardware used to display the virtual environment (VE; Meyer et al., 
2019). Immersive VR is commonly accessed by a head-mounted display (HMD) using two 
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screens close to the eyes (Makransky & Lilleholt, 2018). In contrast, non-immersive VR 
typically refers to the VE being projected on a computer screen (Lee et al., 2010). 
By learning with a HMD, students and trainees are entirely surrounded by the VE, which 
offers a realistic and lifelike experience (Makransky, Borre‐Gude, et al., 2019). This sense of 
being in the virtual world promises a unique advantage for educational and training purposes and 
is commonly referred to as presence in VR literature (e.g., Witmer & Singer, 1998). Immersion 
and its effect on presence have been researched extensively (see Cummings & Bailenson, 2016, 
for an overview) and VR has consistently shown higher presence than desktop-based learning, 
such as videos (e.g., Makransky, Terkildsen, et al., 2019; Ulrich et al., 2019). 
The role of learning activity 
The importance of learning activity in instructional media can be traced back to the 
notion that learners have to become actively engaged for deep learning to occur (e.g., Mayer, 
2009; Renkl et al., 2007; Wittrock, 1991). Although interactivity has played a core role in 
educational literature, it remains an elusive construct (Mcmillan & Hwang, 2002). Different 
studies have yielded conflicting results regarding the effect of interactivity on learning (Domagk 
et al., 2010). Domagk et al. (2010) argued that these ambiguities might reflect diverging 
definitions of interactivity. In response, they proposed a definition for multimedia interactivity 
aimed at encompassing shared ideas from different disciplines: “Interactivity in the context of 
computer-based multimedia learning is reciprocal activity between a learner and a multimedia 
learning system, in which the (re)action of the learner is dependent upon the (re)action of the 
system and vice versa” (Domagk et al., 2010, p. 1052). Based on this definition, the present 
study assesses learning activity by comparing interactive with passive conditions. While the 
interactive conditions allow learners to control the pace and certain aspects of the multimedia 
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learning system through manipulation (e.g., Moreno & Mayer, 2007), passive (or guided; e.g., 
Roussou & Slater, 2017) conditions are controlled by the system (e.g., Evans & Gibbons, 2007). 
Theoretical background 
Multimedia learning 
Multimedia learning refers to knowledge acquisition from instruction containing words 
(e.g., narration, on-screen text) and pictures in static (e.g., illustrations, diagrams) or dynamic 
(e.g., video, animation) form (Mayer, 2012). The rationale for multimedia instruction, such as 
video and VR, is that people learn better from words and pictures combined than from words 
alone (Butcher, 2014; Mayer, 2017). This principle is one of several empirically-based design 
principles of the Cognitive Theory of Multimedia Learning (CTML) aiming to enhance learning 
(Mayer & Pilegard, 2014). 
While many studies have found instructional videos to be more effective for learning 
compared to traditional educational methods (e.g., Calandra et al., 2006; Kay & Edwards, 2012; 
Lin & Tseng, 2012; Santagata, 2009), other scientific works did not find any improvements in 
learning performance (e.g., Donkor, 2010; Lindgren et al., 2007). Nevertheless, Yousef et al. 
(2014) stated in their meta-analysis of video-based learning that there is an agreement among 
researchers that videos have the potential to improve learning outcome when combined with 
appropriate pedagogical methods. Surprisingly, interactive videos have garnered less scientific 
interest with only few studies exploring their effect on learning (Giannakos, 2013; Giannakos et 
al., 2014). Zhang et al. (2006) found positive impacts on learning outcomes when participants 
could jump directly to any part of the instructional video. However, a recent study found that 
control over pace did not affect students’ learning outcomes when comparing interactive and 
non-interactive videos (Biard et al., 2018). 
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Regarding VR, there have been several systematic reviews in recent years exploring the 
relationship between VR and learning. A recent meta-analysis by Hamilton et al. (2020) reported 
that around half of the 29 reviewed papers demonstrated a positive effect on learning when using 
immersive VR over less immersive pedagogical methods. The review indicated that highly 
complex or conceptual problems requiring spatial understanding and visualisation might benefit 
the most from VR (see also Jensen & Konradsen, 2018; Wu et al., 2020, for further reviews). 
However, none of the meta-analyses looked specifically at the role of interactivity. Therefore, 
similar to videos, indicating that research focusing on the interactive component of VR has been 
relatively scarce. Notably, Zhang et al. (2019) investigated the effect of three different levels of 
interactivity (low, medium, high) on objective and subjective learning of immunology concepts. 
Results showed no evidence of increasing levels of interactivity affecting learning outcomes, 
even though subjective learning results suggested otherwise (Zhang et al., 2019). 
When comparing video and VR, Allcoat and von Mühlenen (2018) showed overall better 
learning of biology knowledge with VR while using the same instructional visuals. They 
attributed the better VR scores to either the immersion or the VR environment’s interactivity and 
suggested further studies comparing VR with other active learning methods. However, in another 
recent study by Meyer et al. (2019) participants learning with video scored significantly higher in 
the knowledge retention test than those using VR. Yet, after a post-test delayed by one week, no 
more differences in knowledge were found between the two media(Meyer et al., 2019). 
Therefore, research has yet to offer conclusive evidence on the superior media for learning. 
  
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Cognitive load 
Consistent with CTML, the cognitive load theory (CLT) proposes that human cognitive 
processing is heavily constrained by limited working memory, inhibiting learning when 
cognitive processing exceeds the learners’ capacity (Sweller et al., 2011). According to CLT, the 
working memory can only process a limited number of information elements at a time while the 
long‑term memory is limitless (Sweller et al., 2011). Traditionally, cognitive load is 
differentiated into three independent sources: intrinsic, extraneous and germane cognitive load 
(e.g., Sweller et al., 1998). 
Intrinsic cognitive load is associated with the intrinsic nature of instructional material and 
thereby determined by both the complexity of the information and the knowledge of the person 
processing that information (Sweller et al., 2019). Therefore, intrinsic cognitive load generally 
can only be influenced by altering the quantity of information or its complexity. 
Extraneous cognitive load is determined by how the learning material is presented and 
what activities the learner needs to perform during the learning task (Sweller et al., 2019). 
Therefore, instructional material should aim to minimise extraneous cognitive load by avoiding 
design elements that distract the learner and hamper the learning process.  
Germane cognitive load emerges during the formation and regulation of mental models, 
thereby facilitating learning and contributing to transfer performance (Paas et al., 2003). 
However, learners can only devote resources to germane cognitive load if extraneous cognitive 
load does not exceed their working memory capacity. 
Based on the given description, no conclusive statements or predictions can be made 
about the cognitive load induced by media or learning activity, as the instructional material and 
its presentation may both influence cognitive processes. Nevertheless, Makransky and Lilleholt 
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(2018) have hypothesised that immersive VR simulations could foster generative processing and 
therefore germane cognitive load by providing a highly realistic experience. On the other hand, 
researchers have suggested that the rich VE and high-fidelity graphics of VR could distract 
learners while increasing cognitive load, thereby possibly diverting the learner from the task at 
hand (e.g., Makransky, Terkildsen, et al., 2019; Parong & Mayer, 2018). Even with equivalent 
graphics and animations, VR could similarly increase extraneous cognitive load compared to 
video by too much interaction, leaving less working memory for learning processes (e.g., Zhang 
et al., 2019) 
Intrinsic Motivation 
Intrinsic motivation refers to doing an activity for its inherent satisfaction, as opposed to 
external products, pressures, or rewards (Ryan & Deci, 2000). Ryan and Deci (2000) argue that 
for intrinsic motivation to occur, an activity must hold intrinsic interest for the learner, appear 
novel or challenging, or hold aesthetic value. Additionally, the concept of intrinsic motivation 
has been used as a measure for enjoyment, liking and curiosity (Lepper et al., 2005). Intrinsic 
motivation entails both personal and situational interest (Linnenbrink & Pintrich, 2002). For 
multimedia learning, this means that the learning activity as wells as the learning environment is 
pertinent. Several studies have shown a positive effect of intrinsic motivation on learning 
outcomes in educational contexts (Ryan & Deci, 2009). Further, motivation can impact cognitive 
load. For instance, Schnotz and Kürschner (2007) have shown that high motivation can 
temporarily increase working memory capacity. 
Surprisingly, a meta-study by Mutlu-Bayraktar et al. (2019) on the cognitive load in 
multimedia learning showed only a few studies that have investigated motivation in the past. 
There is empirical evidence for videos suggesting that interactivity positively affects emotional 
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and motivational factors (e.g., Nikopoulou-Smyrni & Nikopoulos, 2010). Similar results have 
been found for VR, with recent studies reporting positive motivational outcomes when compared 
to less immersive instruction (e.g., Makransky, Borre‐Gude, et al., 2019; Makransky & Lilleholt, 
2018). However, Makransky and Lilleholt (2018) stated that there is still limited empirical 
evidence of how much value immersive VR holds. 
Technology acceptance  
The successful implementation of any new learning medium depends on learners’ 
acceptance and willingness to adopt it (Donkor, 2011; Zhang et al., 2006). In order to assess the 
attitudes and acceptance of learners towards multimedia, Davis (1989) offers a theory-based 
approach with the technology acceptance model (TAM). TAM is grounded on the theory of 
reasoned action (Ajzen & Fishbein, 1977) and argues that the decision to accept or reject a 
system is influenced by two major determinants (Davis, 1989). The first is perceived usefulness 
which is the degree to which a person believes that a system will help them perform better at 
their tasks (Davis, 1989). The second is perceived ease of use which is one’s beliefs about the 
effort needed to use the system. Together, these determinants predict an individual’s attitude 
towards using a system called behavioural intention (Davis, 1989). 
Recent studies have predominantly focused on the acceptance of instructional videos 
within learning platforms such as e-learning and have generally identified positive attitudes 
towards videos within those systems (e.g., Liu et al., 2009; Song & Kong, 2017). In the case of 
VR, several studies investigated which variables influence an individual’s attitude towards using 
this technology. Notably, Chen et al. (2012) and Huang and Liaw (2018) showed that perceived 
usefulness directly and positively impacts the intention of students to use VR. This finding led 
Chen et al. (2012) to the conclusion that VR improves the educational quality and facilitates 
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effective learning. Contradictory findings concerning the effect of perceived usefulness on 
behavioural intention by Lee et al. (2019), however, indicate that context may have a great 
influence on VR perceptions. Further, perceived ease of use, learning motivation and enjoyment 
have all shown to positively affect a learners intention to use VR (Chen et al., 2012; Huang & 
Liaw, 2018). 
Context of the study 
The present study is embedded in the research project Systematic Threat Assessment, 
New Standards, Learning Technology Research - Transfer into Practice (STA2RT) by the Center 
for Adaptive Security Research and Applications in cooperation with the University of Applied 
Sciences Northwestern Switzerland. STA2RT focuses in part on X-ray screening of cabin or 
carry-on baggage at airports. Passenger baggage screening is conducted to prevent terrorist 
attacks and other unlawful interference against civil aviation. So far, predominantly 2D X-ray 
imaging systems were used for cabin baggage screening. With novel 3D imaging technology 
based on computer tomography (CT) set to replace the current X-ray systems, airports face the 
challenge of preparing airport security officers (screeners) through knowledge building and 
training. For this purpose, a multimedia lesson was developed in cooperation with training 
personnel of an airport, focusing on supplementing traditional training protocols with context-
specific 3D CT learning material experienced through multimedia. This setting offers great 
potential to empirically study and compare media (video vs. VR) and learning activities (passive 
vs. interactive) using identical learning material in a setting with high practical significance. 
A quantitative study was scheduled for spring 2020 with a large screener sample at an 
international airport. However, the study had to be postponed indefinitely as a consequence of 
the ongoing COVID-19 pandemic. In lieu thereof, cooperation with two smaller Swiss airports 
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was established. Additionally, the study was enlarged by a qualitative part focusing on the 
screeners’ perception of the administered media and learning activity. Thus, the flexibility and 
small sample of screeners at the cooperating airports was put to an advantage. 
Purpose of the study and research questions 
Regarding the context and theoretical background, the purpose of the present study is to 
explore how media (video vs. VR) and learning activity (passive vs. interactive) affect screeners’ 
learning, cognitive load, intrinsic motivation, and technology acceptance. Thereby, it provides 
preliminary findings on the use of multimedia learning in airport settings and offers important 
contributions to further studies conducted in the STA2RT research project. The following 
research questions are addressed in this study: 
1. How do media (video vs. VR) and learning activity (passive vs. interactive) affect 
learning outcomes, cognitive load, intrinsic motivation, and technology acceptance of 
airport security officers? 
2. How do airport security officers perceive learning with passive video, interactive video, 
passive VR, and interactive VR? 
In order to answer these research questions, this study employed a convergent mixed 
methods design (Creswell & Clark, 2017) Convergent designs intend to obtain different but 
complementary data on the same topic, thus gaining a better understanding of the research 
problem (Morse, 1991). This approach entails separate quantitative and qualitative data 
collection and analysis, thereby ensuring that the methods do not influence each other (Creswell 
& Clark, 2017). The results are then matched and compared in the discussion. This side-by-side 
approach allows identifying similarities and contradictions in the data and adds to a more 
complete understanding (Creswell & Clark, 2017).  
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Method 
Participants 
The study was carried out with 26 screeners from two airports (Airport A: n = 12; Airport 
B: n = 14) in the German-speaking part of Switzerland. All except for one participant screened 
mainly cabin baggage. However, due to the airports’ small size, all participants also performed 
hold baggage and staff screening tasks. The participants’ mean age was 47.31 years (SD = 12.81; 
Airport A: M = 42.50, SD = 14.07; Airport B: M = 51.43, SD = 10.41) and mean work 
experience was 5.31 years (SD = 3.98; Airport A: M = 4.00, SD = 4.13; Airport B: M = 6.43, SD 
= 3.61).1 Of the 26 participants, approximately half were female (Airport A: 50% female; 
Airport B: 43% female). Further, slightly less than half of the participants had experienced VR 
before partaking in this study (Airport A: 42% VR experience; Airport B: 43% VR experience). 
The participants were informed about the study procedures and goals prior to the study. All 
participants gave written informed consent and received monetary compensation based on their 
hourly salary. The study was approved by the institutional ethics review board of the School of 
Applied Psychology, University of Applied Sciences and Arts Northwestern Switzerland. 
Design 
The factors media (video vs. VR) and learning activity (passive vs. interactive) were 
varied in a 2 x 2 between-subjects design, leading to four experimental groups experiencing the 
multimedia lesson with uniform learning content: Passive video, interactive video, passive VR, 
interactive VR. Participants at each airport were randomly assigned to one of the four 
 
 
1 No significant differences for participants’ age or work experience were detected between airports using 
Student’s t tests. 
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experimental conditions, equalling eight groups of three or four participants. After the learning 
intervention, all participants completed a questionnaire measuring learning outcomes, cognitive 
load, intrinsic motivation, and technology acceptance. Finally, a focus group discussion was 
conducted with each of the eight groups. 
Materials 
The multimedia lesson was developed in consideration of the CTML’s instructional 
design principles to manage cognitive load (see Mayer, 2009, for an overview of CTML design 
principles). The multimedia lesson focused on providing novel and experienced 2D X-ray 
screeners with introductory information about 3D CT imaging systems. Based on a revision of 
Bloom’s taxonomy by Anderson et al. (2001), the learning material contained mainly factual 
knowledge (e.g., the 3D CT machine you see in front of you meets the C3 standard of the 
European Civil Aviation Conference) and some conceptual knowledge (e.g., this means that 
liquids and laptops can be left in baggage for the scanning process).  
While keeping learning content uniform, a tailored version of the multimedia lesson was 
developed for each experimental group depending on the administered media and learning 
activity. In line with Moreno and Mayer’s (2007) proposed types of interactivity, the interactive 
versions of the multimedia lesson encompassed control over pace and manipulation. Participants 
were regularly prompted to initialise the next part of the multimedia lesson by pressing a virtual 
button (see Figure 1), thus controlling the pace in which the learning material progressed. 
Further, participants were given the possibility of manipulating a laptop in the last part of the 
multimedia lesson. By rotating a laptop in any direction, participants could view the laptop’s 3D 
CT image from any chosen angle. For passive groups, on the other hand, pace and laptop rotation 
was predetermined (see Figure 2). 
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Figure 1 
Screenshot of the virtual button being pressed in the interactive VR version
 
Note. Participants experiencing the interactive VR version used an Oculus 
touch controller for interactions. 
 
Figure 2 
Screenshot of the rotating laptop in a passive video version
  
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Through head movements, participants of the multimedia lessons’ VR versions had the 
possibility of looking around freely in the VE. As videos do not allow for such control, visuals in 
the video versions adopted a static camera focusing on the relevant learning content. Therefore, 
videos were considered displays of an optimal viewing of the VR versions. 
The multimedia lesson consisted of five consecutive parts: First participants received an 
introduction and tutorial, explaining the multimedia lesson’s aim and the respective media and 
learning activity. This allowed participants to acclimate themselves to the video or VE. In the 
interactive versions, the tutorial further explained how to use the input modalities and let 
participants practice controlling the pace and manipulation of the learning material. The second 
part consisted of a historical presentation of security screening at airports, showing the technical 
progression from preceding 2D X-ray to the novel 3D CT imaging systems. The third part 
informed participants about 3D CT hardware and working principles. In the fourth part, two 
baggage trays were scanned, and participants were presented with the new user interface and 
software features of 3D CT technology. In the last part, participants viewed a rotatable laptop 
and corresponding 3D CT image. 
Procedure 
The study took place at the airports’ facilities. Workstations were set up in a quiet and 
normally lit room, familiar to the participants. The VR versions of the learning material were 
administered with first-generation Oculus Quest HMDs (resolution per display: 1440 × 1600 
pixels). For interactive VR, participants used a standard Oculus touch controller (second-
generation Oculus Touch) corresponding to their handedness. Videos were displayed on Laptops 
with 17.3-inch monitors (resolution: 1920 x 1080 pixels). A standard wired computer mouse 
served the participants as input device for the interactive video condition. The audio was 
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delivered through on-ear headphones for all experimental conditions. In order to reduce visual 
distractions for video conditions, workstations were fitted with a cardboard visual cover. 
Experimental groups first received oral information and instructions concerning the study 
procedure. Then, before starting the multimedia lesson, VR groups were shown how to use the 
VR equipment correctly. This entailed ensuring a comfortable fit of the HMD and adjusting the 
pupillary distance between the HMDs screens. Additionally, participants of the interactive VR 
groups were shown how to grip the VR controller properly. Depending on the experimental 
condition and pace of the participant, the multimedia lesson had a mean total duration of 15.66 
min (SD = 2.73; see Appendix A for an overview of each experimental condition). The tutorial 
specific to each experimental condition had a mean duration of 3.58 min (SD = 2.38). After the 
learning intervention, the participants were immediately given a questionnaire. Following a 
subsequent break of 10 to 15 min, the focus groups were conducted. Each focus group lasted 
around one hour. 
Measures 
Questionnaire 
The quantitative data was measured using a paper-pencil questionnaire (see Appendix B). 
Questionnaire items were administered in German and adapted to the present study if necessary. 
For some items, this involved slightly altering the wording (e.g., changing “task” to “learning 
task”) and translating them when no German version was available (e.g., Beaton et al., 2000). 
For this process, a native bilingual speaker translated the original items from English to German. 
Another native speaker of English and German then translated the items back into English. An 
item revision by the translators followed this process. 
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Learning outcomes were assessed as a dependent measure by administering a learning 
performance test in the questionnaire. The performance test consisted of 12 multiple-choice 
items (e.g., 3D CT machines meet the C3 standard of the European Civil Aviation Conference. 
What items are passengers allowed to leave in their luggage?). Each correct answer scored one 
point while incorrect answers scored zero points, resulting in a maximum score of 12 points.  
Cognitive load was measured by using two separate subjective rating scales. Subjective 
rating scales have shown to be similar in validity and reliability as physiological measurement 
techniques (Szulewski et al., 2017). The main advantage of subjective rating scales is their 
sensitivity and simplicity (Sweller et al., 2019). The first subjective rating scale used in the 
questionnaire was developed by Paas (1992) and was measured on a nine-point Likert scale, 
which is commonly used for this item (Park & Brünken, 2015). Even though the item was 
originally called mental effort (Paas, 1992), it is considered the most frequently used measure for 
cognitive load (Mutlu-Bayraktar et al., 2019). The second instrument applied for assessing 
cognitive load was developed by Klepsch et al. (2017) and contained nine items measured on a 
seven-point Likert scale. This instrument has the advantage of measuring different types of 
cognitive load (intrinsic, extraneous, germane) separately. Differentiated measuring scales have 
found academic interest in recent years, promising better interpretation of the results and linking 
to the theoretical base (Mutlu-Bayraktar et al., 2019). Cronbach's α values reported by Klepsch et 
al. (2017) were .81, .86, and .67 for the intrinsic cognitive load, extraneous cognitive load and 
germane cognitive load scales, respectively. 
Intrinsic motivation was assessed with the interest and enjoyment subscale of the intrinsic 
motivation inventory (IMI; Ryan, 1982). This subscale contained seven items measured on a 
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seven-point Likert scale and is considered the self-reported measure of intrinsic motivation 
(Cortright et al., 2013).  
Technology acceptance was evaluated with the perceived usefulness, perceived ease of 
use, and behavioural intention subscales of the TAM3 by Venkatesh and Bala (2008), an 
extension of the original TAM. Each subscale was measured on a seven-point Likert scale. 
Perceived use and perceived ease of use consisted of four items, and behavioural was composed 
of three items. 
Focus groups 
Focus groups were conducted to gain further insight into the participants’ experiences 
when learning with interactive and passive video and VR. Focus groups allow collecting 
extensive data through active participation and discussion among interviewees (Krueger & 
Casey, 2014). Furthermore, the group setting makes it possible to uncover a broad range of 
perspective and gain a deeper understanding of the issues from the viewpoint of the participants 
(Hennink & Leavy, 2014). In keeping with focus group literature, a discussion guide was crafted 
(e.g., Barbour & Morgan, 2017; Hennink & Leavy, 2014; Masadeh, 2012; see Appendix C). The 
focus groups were conducted in a semi-structured format and led by the author of this study. 
Audio recordings were made of all eight focus groups. Additionally, an observer was present for 
each focus group noting prominent gestures, themes and quotes (e.g., Hennink & Leavy, 2014). 
The opening section of the focus group consisted of a broad question and a brief activity. 
In the activity, participants had to choose their most and least favoured part of the learning 
module. This section meant to build rapport among the participants and make them feel at ease 
before moving to more specific and critical topics (Hennink & Leavy, 2014). Sections two to 
four served as the main focus and intended to ascertain participants’ experiences. Emphasis was 
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laid on using context-specific questions to promote discussion and gain personal insights (e.g., 
given the current Covid-19 pandemic, what would the reaction be if you had to continue using 
this learning media at home?). In the last section, the most important topics were revisited if 
necessary, and participants had the possibility of making final statements. 
Data Analysis 
Quantitative 
All quantitative data was analysed using Jamovi (version 1.2.27). For the rating scales of 
cognitive load, intrinsic motivation and technology acceptance, reliability was explored using 
Cronbach's α (Cronbach, 1951). Values are interpreted as unacceptable below .60, undesirable 
between .60 and .65, minimally acceptable between .65 and .70, respectable between .70 and .80, 
and very good between .80 and .90 (DeVellis, 2016, p. 136). A low value is often an indicator for 
a low number of questions, poor inter-relatedness between items or heterogeneous constructs 
(Tavakol & Dennick, 2011). In this case, Tavakol and Dennick (2011) advise reviewing or even 
discarding items if the low α value is due to poor correlation between items. 
For the measure learning outcome, items of the performance test were analysed using the 
item difficulty index. The item difficulty index ranges from 0% to 100% and refers to the 
percentage of participants who correctly answered the item (Quaigrain et al., 2017) According to 
Boopathiraj and Chellamani (2013), items with a value between 20% and 90% are considered 
acceptable. 
All quantitative measures were investigated using two-way analysis of variance 
(ANOVA). Prior, normal distribution was assessed using Kolmogorov-Smirnov tests and 
quantile-quantile plots (Q-Q plots). Additionally, homoscedasticity was tested with the Levene’s 
test. For the ANOVAs, the experimental groups of both airports were joined. Media (video vs. 
   22 
VR) and learning activity (passive vs. interactive) acted as independent variables, and learning 
outcomes, cognitive load (intrinsic cognitive load, extraneous cognitive load, germane cognitive 
load, mental effort), intrinsic motivation, and technology acceptance (perceived usefulness, 
perceived ease of use, behavioural intention) served as the dependent variables. In order to 
enhance the interpretation of the results, the effect size ω2 was calculated (Cumming, 2013). 
Effect sizes are standardised and objective measures that indicate the magnitude of an observed 
effect (Field, 2018). For the present analysis, ω2 was most suitable as it tends to be less biased 
for small sample ANOVA calculations compared to η2 (Olejnik & Algina, 2003). Effect sizes of 
ω2 are interpreted as small (.01), medium (.06), and large (.14) (Cohen, 1988, p. 368). 
Interactions of ANOVAs with at least a medium interaction effect were further investigated with 
post hoc tests. 
Focus groups 
Qualitative data was transcribed and analysed using MAXQDA (version 20.0.2). Analysis 
followed the principles of systematic qualitative text analysis (Kuckartz, 2014). By applying 
deductive-inductive category building, this approach allows for both a theory-driven and open, 
explorative analysis (Kuckartz, 2014). Deductive categories were derived from thematic groups 
from the theoretical background and focus group discussion guide. Inductive categories, 
consisting of additional relevant information and categorical sub-groups, were identified within 
the collected data. 
In order to enhance the interpretive quality of the data, a systematic analysis approach was 
followed, thereby enhancing the objectivity of the results (Döring & Bortz, 2016). Further, the 
analytic process was documented to allow for easier intersubjective comprehensibility (Kuckartz, 
2014). For reliability, focus group statements were compared within and across focus group 
   23 
sessions (Knodel, 1993). Further, the author discussed each focus group with the observer. Main 
themes and important statements were identified collectively, thereby increasing the validity of 
results (Creswell & Clark, 2017). 
Results 
Questionnaire 
Scale reliability 
Table 1 shows the reliability of the assessed rating scales. Very good reliability was 
found for the intrinsic motivation, perceived usefulness, and behavioural intention scales with 
Cronbach’s α ranging from .86 to .89. The extraneous cognitive load (α = .76) and perceived 
ease of use (α = .73) scales showed respectable reliability. Scales assessing intrinsic cognitive 
load (α = .34) and germane cognitive load (α = - .44) showed unacceptable internal consistency. 
Therefore, one item of the germane cognitive load scale, which correlated negatively, was 
discarded. The two remaining items (α = .84, M = 6.33, SD = 0.75) indicated good internal 
consistency and were therefore used for the analysis of germane cognitive load. 
Table 1 
Means, Standard Deviations, and Cronbach’s α 
Scale M SD Cronbach’s α 
Intrinsic cognitive load (2 items) 2.94 1.49 .34 
Extraneous cognitive load (3 items) 1.44 0.56 .76 
Germane cognitive load (3 items) 5.55 0.74 - .44 
Intrinsic motivation (7 items) 6.35 0.74 .86 
Perceived usefulness (4 items) 5.86 0.98 .89 
Perceived ease of use (4 items) 6.29 0.71 .73 
Behavioural intention (3 items) 6.10 1.04 .89 
Note. n = 26. 
  
   24 
Item difficulty index 
One item with a value of .92 on the item difficulty index was identified outside the 
acceptable range of .20 and .90 (Boopathiraj & Chellamani, 2013). Therefore, the item was 
excluded from further analysis. The remaining 11 items assessing learning outcomes ranged 
from .35 to .89 on the item difficulty index. 
Analysis of variance (ANOVA) 
Interpretation of Q-Q plots (see Appendix D) exhibited potential deviances from the 
distributions of interest deviations from normality for extraneous cognitive load, germane 
cognitive load, perceived ease of use, and behavioural intention. However, Kolmogorov-Smirnov 
tests showed no deviations from normality. Therefore, all scales were used for further analysis. 
Levene’s tests revealed a violation of the assumption of homogeneity by intrinsic cognitive load 
indicating significantly different variances between the groups.2 
Two-way ANOVAs were conducted to assess the impact of media (video vs. VR) and learning 
activity (passive vs. interactive) on learning outcomes, cognitive load, intrinsic motivation, and 
technology acceptance. Table 2 shows the number of participants per condition, means, standard 
deviations, and results of the two-way ANOVAs (according to the American Psychological 
Association, 2020, p. 217).
 
 
2 According to Tabachnick & Fidell (Tabachnick & Fidell, 2019), ANOVAs are robust to violations of 
homogeneity if sample sizes are relatively equal (within a ratio of 4 to 1) and variances between groups do not 
exceed the ratio of 10 to 1. By satisfying both criteria, intrinsic cognitive load was deemed appropriate for further 
analysis. 
   25 
Table 2 
Number of participants, means, standard deviations, and two-way ANOVAs 
Variable Video VR ANOVA 
 n M SD n M SD Effect F ratio df ω2 
LO           
 Interactive 7 7.57 2.99 7 6.57 1.27 MD 1.73 1, 22 .03 
 Passive 6 7.67 2.73 6 6.33 1.51 LA 0.01 1, 22 .00 
       MD x LA 0.04 1, 22 .00 
ICL           
 Interactive 7 2.64 1.95 7 2.79 1.11 MD 0.55 1, 22 .00 
 Passive 6 2.83 1.94 6 3.58 0.74 LA 0.67 1, 22 .00 
       MD x LA 0.25 1, 22 .00 
ECL           
 Interactive 7 1.14  0.26 7 1.57 0.50 MD 0.01 1, 22 .00 
 Passive 6 1.72 0.88 6 1.33 0.37 LA 0.64 1, 22 .00 
       MD x LA 3.69 1, 22 .10 
GCL           
 Interactive 7 6.57 0.45 7 6.07 0.79 MD 0.00 1, 22 .00 
 Passive 6 6.08 1.07 6 6.58 0.58 LA 0.00 1, 22 .00 
       MD x LA 2.88 1, 22 .07 
ME           
 Interactive 7 3.86 2.12 7 3.86 2.97 MD 0.01 1, 22 .00 
 Passive 6 2.50 1.64 6 2.67 1.37 LA 2.25 1, 22 .05 
       MD x LA 0.01 1, 22 .00 
IM           
 Interactive 7 6.61 0.48 7 6.59 0.50 MD 2.97 1, 22 .06 
 Passive 6 5.60 1.07 6 6.50 0.39 LA 4.67* 1, 22 .11 
       MD x LA 3.25 1, 22 .07 
PU           
 Interactive 7 6.07 0.89 7 6.14 0.70 MD 1.30 1, 22 .01 
 Passive 6 5.17 1.09 6 5.96 1.16 LA 2.07 1, 22 .04 
       MD x LA 0.91 1, 22 .00 
PEOU           
 Interactive 7 6.54 0.55 7 6.43 0.55 MD 0.01 1, 22 .00 
 Passive 6 6.04 1.22 6 6.08 0.30 LA 2.17 1, 22 .05 
       MD x LA 0.07 1, 22 .00 
BI           
 Interactive 7 6.29 1.18 7 6.14 1.14 MD 0.30 1, 22 .00 
 Passive 6 5.67 1.15 6 6.28 0.77 LA 0.32 1, 22 .00 
       MD x LA 0.79 1, 22 .00 
Note. Media = MD; LA = learning activity; LO = learning outcomes; ICL = intrinsic cognitive load; ECL 
= extraneous cognitive load; GCL = germane cognitive load; ME = mental effort; IM = intrinsic 
motivation; PU = perceived usefulness; PEOU = perceived ease of use; BI = behavioural intention. 
*p < .05.  
   26 
ANOVAs showed a significant main effect for learning activity on intrinsic motivation 
(passive: M = 6.05, SD = 0.90; interactive: M = 6.60, SD = 0.47), as well as an interaction 
between media and learning activity with medium effect size. Post hoc comparisons 
investigating interactions of ANOVAs revealed significantly lower intrinsic motivation for 
passive video (p = .010) compared to interactive video (p = .025), passive VR and interactive VR 
(p = .012). Figure 3 presents the interaction plot for the intrinsic motivation scale, followed by 
Figures 4 to 11, showing the interaction plots of the non-significant scales.3 
 
Figure 3 
Interaction plot for intrinsic motivation 
 
Note. Intrinsic motivation was assessed on a seven-point Likert scale. 
Error bars represent the standard error of the mean. 
 
 
 
3 Default Jamovi plots are presented in this study. Therefore, interaction plots are truncated on the y-axis 
and do not necessarily show the zero-baseline. While this visual exaggeration aims to aid the presentation of results, 
caution is advised for effect size interpretation (Correll et al., 2020). 
   27 
Figure 4 Figure 5 
Interaction plot for learning outcomes Interaction plot for intrinsic cognitive load 
 
Note. Learning outcomes were assessed on a nine-point Likert scale. Note. Intrinsic cognitive load was assessed on a seven-point Likert 
Error bars represent the standard error of the mean. scale. Error bars represent the standard error of the mean. 
  
Figure 6 Figure 7 
Interaction plot for extraneous cognitive load Interaction plot for germane cognitive load 
  
Note. Extraneous cognitive load was assessed on a seven-point Note. Germane cognitive load was assessed on a seven-point Likert 
Likert scale. Error bars represent the standard error of the mean. scale. Error bars represent the standard error of the mean.  
   28 
Figure 8 Figure 9 
Interaction plot for mental effort Interaction plot for perceived usefulness 
  
Note. Mental effort was assessed on a seven-point Likert scale. Error Note. Perceived usefulness was assessed on a seven-point Likert 
bars represent the standard error of the mean. scale. Error bars represent the standard error of the mean. 
  
Figure 10 Figure 11 
Interaction plot for perceived ease of use Interaction plot for behavioural intention 
  
Note. Perceived ease of use was assessed on a seven-point Likert Note. Behavioural intention was assessed on a seven-point Likert 
scale. Error bars represent the standard error of the mean. scale. Error bars represent the standard error of the mean. 
   29 
Focus groups 
This section describes the perceptions of the participants towards the administered 
experimental condition (passive video, interactive video, passive VR, interactive VR) captured in 
the eight focus groups. 
Learning 
Participants of all groups expressed that they gained knowledge of 3D CT imaging 
systems through the learning material. When asked if the participants could explain to a 
colleague what 3D CT is and how it would affect their screening tasks, passive VR participants 
were diffident. They noted that hands-on experience was required to make conclusive statements 
about the changes 3D CT entailed. 
Participants in interactive video groups believed that being able to interact with the 
learning material positively affected their learning, with one participant stating: “Well, if I can do 
something, it just stays with me”. This sentiment was mirrored in interactive VR groups. Further, 
participants in both passive and interactive VR groups felt that VR facilitated learning processes 
using more bodily senses compared to traditional learning methods. 
Cognitive load 
The learning materials’ difficulty level was perceived as appropriate across all groups, 
and no incomprehensible content was identified. Most participants felt that basic knowledge of 
cabin baggage screening was necessary in order to understand the learning material fully. 
Further, participants noted no or minimal prior knowledge about 3D CT imaging systems. The 
existing knowledge was traced back to personal experiences (e.g., medical procedures) or work-
related information. For example, management mentioning future changes from current X-ray to 
3D CT imaging systems. 
   30 
A major theme addressed exclusively in VR groups was feeling distracted by the VE. 
One passive VR participant with no prior VR experience said: “For me it was the first time. And 
to look around, everything else is more interesting than what is actually important”. Several other 
VR participants mentioned that the VE might have hindered their ability to focus on the learning 
content, especially early in the multimedia lesson. However, participants in interactive VR and 
interactive video groups felt that regularly pressing the virtual button to initiate the next sequence 
helped them focus. Further, interactive video and VR participants felt that control over pacing let 
them adjust the learning pace to their liking. In this context, an interactive VR participant said 
about the learning material: “I liked it a lot. It adapted to my pace. When I understood it fast, it 
was also fast”. 
Intrinsic Motivation 
Most participants stated that they found the learning material interesting and enjoyed the 
overall experience. While passive and interactive video groups mostly referred their statements 
to the learning material, passive and interactive VR groups often alluded to the media they were 
administered. Participants in all VR groups positively described VR as a novelty, or as one 
passive VR participant put it “just something different”. On the other hand, participants in 
passive video groups felt that the administered media and learning activity was not particularly 
exciting, with one participant emphasising that “nowadays, we are used to more”. 
Participants in interactive VR and video groups particularly enjoyed manipulating a 
laptop in the last part of the multimedia lesson. For example, one interactive VR participant said: 
“That [the manipulation of the laptop], of course, was fascinating. How you can grab that and 
turn it around yourself. It’s also the playfulness of it”. Conversely, the same part was conceived 
as rather bland by passive video and VR participants. 
   31 
Technology acceptance 
A majority of participants across all groups found the administered combination of media 
and learning activity useful for learning information related to airport security. Adverse 
sentiments were mainly expressed in passive video groups. Participants felt that the absence of 
human-to-human interactions and not being able to ask questions negatively affected the passive 
videos’ usefulness. Further, participants in passive VR groups voiced displeasure concerning the 
restrictiveness of a HMD, concluding that it would interfere with them taking notes. On the other 
hand, interactive video participants mainly mentioned advantages. Being able to simulate real-
life working conditions (e.g., loud airport environments) and the possibility of using interactive 
video for training purposes (e.g., cabin baggage screening training) were seen as having great 
potential in an airport learning setting. Similarly, passive and interactive VR participants 
predicted simulating real-life working conditions as VR’s most prominent potential for airport 
security training. Overall, participants across all groups noted a preference for the administered 
media and learning activity compared to traditional learning methods currently used at airports 
(e.g., PowerPoint presentations). For example, a passive video participant said: “I'd rather have a 
learning video than PowerPoint, I have to say. Because when I think back to the theory sessions, 
it really was just slide after slide - click, click, click”. 
Participants across all groups generally stated that using the administered media and 
learning activity was uncomplicated. In this regard, the tutorial at the beginning of the learning 
material was deemed important and adequate. However, concerning the administered media, 
participants in passive and interactive VR groups revealed issues regarding the ergonomics and 
display resolution of the HMD.  
   32 
Ergonomics and display resolution in VR 
Ergonomics and display resolution were two additional themes identified from the focus 
groups. Both issues were addressed exclusively in passive and interactive VR groups as they 
corresponded to the HMD used in this study. Participants expressed discontent about the weight 
and fit of the HMD. Particularly female participants had issues finding a comfortable fit using 
the HMD’s head straps. Further, a few participants found themselves sweating excessively when 
wearing the HMD. Concerning display resolution, several participants felt that the HMD did not 
yet display a clear enough picture to warrant future use. 
Discussion 
The present study employed a mixed methods approach in order to explore learning with 
media (video vs. VR) and learning activity (passive vs. interactive) in an airport security setting. 
For this purpose, learning outcomes, cognitive load, intrinsic motivation, and technology 
acceptance were measured using a questionnaire. Further focus groups were conducted to assess 
the perceptions of screeners towards the administered media and learning activity. 
Learning 
With regard to learning, questionnaire data showed a small effect size indicating slightly 
better learning outcomes with video than VR. Focus group data neither support nor refute the 
quantitative results, as participants in all groups believed to have gained knowledge of 3D CT 
imaging systems. Therefore, these findings substantiate the complex relationship between media 
and learning as literature has previously shown. The superior learning outcomes of screeners 
with video compared to VR could be ascribed to the conveyed and assessed knowledge in this 
study. The multimedia lesson aimed at giving screeners introductory information on 3D CT 
imaging systems, focusing mainly on factual knowledge. However, it has been suggested, that 
   33 
the main instructional potential of VR lies in more complex learning tasks (e.g., Hamilton et al., 
2020). Zahn et al. (2004) offer another explanation in suggesting that being unfamiliar with a 
medium might hinder learning. Therefore, the unfamiliarity of many screeners with VR may 
have negatively affected learning.  
Learning activity did not influence learning outcomes. Interestingly, these results stand in 
contrast to focus group data. In interactive video and VR groups, screeners believed that being 
active and using more bodily senses improved their learning. This discrepancy is in line with 
previous literature by Zhang et al. (2019) showing no objective knowledge gain through 
increased interactivity, even though participants believed otherwise. The perceived learning 
advantages of interactivity may be explained due to a higher sense of autonomy through better 
control over the environment in the interactive groups (e.g., Makransky & Lilleholt, 2018).  
Cognitive load 
For cognitive load, two instruments were used for assessment. The differentiated 
measurement instrument showed no effect of media or learning activity on intrinsic cognitive 
load. This result is consistent with focus group data. Across all groups, participants perceived the 
level of difficulty of the learning material as appropriate. Combined, these findings reinforce that 
the learning material did not vary between groups, and learning was not hindered through 
content complexity. 
The extraneous cognitive load scale showed an interaction between media and 
interactivity with a medium effect size. This result is in accordance with the germane cognitive 
load scale which showed an inverse interaction with a medium effect size. These findings imply 
that extraneous cognitive load may have influenced germane cognitive load and therefore 
learning processes of screeners. In the focus groups, participants in VR groups identified the VE 
   34 
as a possible distraction. On the other hand, interactivity was perceived to enhance focus in 
interactive video and VR groups. While these qualitative findings do not explain the quantitative 
results of extraneous and germane cognitive load, they further support the notion that a rich VE 
can distract learners when using virtual reality (e.g., Makransky, Terkildsen, et al., 2019; Parong 
& Mayer, 2018). 
The mental effort instrument revealed a small effect size indicating slightly higher effort 
invested for interactive compared to passive groups. This result neither confirms nor contradicts 
the qualitative and differentiated measurement data but instead offers an additional perspective 
on the effect of interactivity on cognitive load. While higher cognitive load through more activity 
is to be expected (e.g., Zhang et al., 2019), it remains unclear how it affected learning in this 
study. 
Intrinsic motivation 
Quantitative data revealed that screeners in passive video groups were significantly less 
intrinsically motivated than in the other groups. These results are reinforced by the qualitative 
data, as screeners from interactive groups said they particularly enjoyed being able to actively 
manipulate an object in the multimedia lesson. This finding is consistent with studies assessing 
the affective value and motivation of interactive video (e.g., Nikopoulou-Smyrni & Nikopoulos, 
2010). Further, participants of VR groups highlighted their enjoyment and interest when using 
the novel media. VR literature confirms this finding with people favouring VR for motivational 
outcomes compared to less immersive instruction (e.g., Makransky & Lilleholt, 2018). 
Interestingly, the quantitative data also showed that interactive video is similarly beneficial for 
intrinsic motivation as passive and immersive VR. Therefore, added immersion may not be the 
panacea for lacking intrinsic motivation of learners. 
   35 
Technology acceptance 
Regarding technology acceptance, perceived usefulness showed small effect sizes 
indicating that screeners believed interactivity and VR are more useful for their tasks than 
passivity and videos, respectively. Drawing on the qualitative data, these findings can be traced 
back to interactivity and VR offering realistic simulation possibilities of the screeners’ real-world 
tasks. Conversely, drawbacks mentioned almost exclusively in passive video groups were 
missing human-to-human interactions as well as the possibility of asking questions. 
The perceived ease of use subscale showed no difference in the screeners’ belief of effort 
needed to use the assessed media. Regarding learning activity, a small effect size was found 
showing slightly better scores for interactive compared to passive groups. While focus group 
data does not provide an explanation for this finding, previous research has shown that perceived 
control over a system positively affects perceived ease of use (Lee et al., 2007). Considering 
interactive groups had control of pace and certain aspects of the learning material through 
manipulation, perceived control may have led to higher perceived usefulness. 
Regarding behavioural intention, quantitative results showed no differences for media or 
interactivity. In view of behavioural intention being determined by perceived usefulness and 
perceived ease of use, these findings may be explained by a lack of statistical differences found 
in the other technology acceptance scales. 
Ergonomics and display resolution 
Through inductive analysis of focus group data, ergonomics and display resolution were 
identified as major themes when learning with VR. Several participants stated in the focus 
groups that the HMDs used in this study were too heavy for their linking and did not fit 
comfortably. Further, some participants felt that the picture displayed in the HMDs was not clear 
   36 
enough. These findings are relevant, as they might have affected the results of this study. 
Additionally, the findings highlight that even though major technological advancements have 
made in recent years, state of the art HMDs still face some of the same issues that were identified 
over 20 years ago (e.g., Nichols, 1999). 
Practical implications 
This study could not produce strong scientific evidence to support the use of VR for instructional 
purposes in practical settings such as airports. More specifically, when the goal is to convey facts 
and basic concepts to employees, the established and more cost-effective videos might offer 
greater utility. Nevertheless, VR is a novel technology which sparks interest and enjoyment in 
learners. This can be favourable, especially when instruction aims to motivate and excite learners 
for new subject matters. If VR is used, findings of this study suggest that novel learners should 
be given enough time to acclimate to the VE. Additionally, instructional designers and VR 
developers are advised to minimise potential distractors. Further, the findings of this study show 
that learning activity plays a key role for intrinsic motivation, perceived usefulness, and 
perceived learning and should therefore be considered when designing instruction for video and 
VR. 
Limitations and further research 
The sample assessed in this study was small, which limits the generalizability of the 
findings. Moreover, the reduced sample size led to several limitations regarding quantitative 
analysis and results: First, the small sample did not allow for a control group to assess the 
knowledge gain of participants. While qualitative data suggests that participants did gain 
knowledge, future studies may alternatively employ a pretest-posttest design for improved 
assessment of learning processes. Second, groups of both airports with matching media and 
   37 
learning activity were combined for analysis. While t tests showed no significant differences in 
participants’ age or work experience between airports, variance stemming from the respective 
contexts could have been introduced. Thus, in the case of a larger sample, employing analyses of 
covariance may allow for a more sensitive assessment of the group means. Third, while 
Kolmogorov-Smirnov tests suggested normal distributions of the scales, Q-Q plots exhibited 
potential deviations in some instances. Additionally, the intrinsic cognitive load scale did not 
meet the assumption of homoscedasticity. However, in view of Tabachnick and Fidell’s (2019) 
recommendations, the scale was considered suitable for analysis. Fourth, analyses predominately 
yielded non-significant results, which necessitated post hoc tests with no correction. Given a 
larger sample, a correction applied to the α-level, such as the Holm-Bonferroni correction (Holm, 
1979), is advised. 
Learning outcomes were assessed using 12 multiple-choice items, of which one item was 
excluded from analysis for being correctly answered by more than 90% of the participants. As 
the multimedia lessons’ content inherently limits the number of items, future studies using the 
same learning material should revise the discarded multiple-choice item to enhance overall 
reliability of the assessment. 
Regarding instrument selection, the cognitive load instrument by Klepsch et al. (2017) 
showed poor reliability for the intrinsic cognitive load and germane cognitive load scales. 
Considering the advantages of differentiated cognitive load scales for interpreting results and the 
methodological challenges one-item scales pose (e.g., Leppink et al., 2013; van Gog & Paas, 
2008), future studies might consider another differentiated measurement instrument. For 
example, Leppink et al. (2013) offer a validated alternative with their instrument measuring the 
three different types of cognitive load (Cook et al., 2017). 
   38 
Conclusion 
Overall, the present study offers insights into the advantages and disadvantages of passive and 
interactive videos and VR when used in an educational setting. The employed mixed methods 
design allowed to identify differences in perceived and objective learning. Further, interactive 
video, passive VR, and interactive VR led to higher intrinsic motivation than passive video. 
Qualitative data suggests that the novelty of VR and interactions play a crucial role in 
intrinsically motivating learners. However, rich and exciting VE can distract learners and should 
therefore be carefully considered when developing learning material for VR. In the near future, 
the findings of this study will help to improve questionnaires and multimedia material for further 
studies in the STA2RT research project.  
   39 
References 
Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review 
of empirical research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-
2909.84.5.888 
Allcoat, D., & von Mühlenen, A. (2018). Learning in virtual reality: Effects on performance, 
emotion and engagement. Research in Learning Technology, 26, 1–13. 
https://doi.org/10.25304/rlt.v26.2140 
American Psychological Association. (2020). Publication manual of the American Psychological 
Association (7th ed.). 
Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, 
P. R., Raths, J., & Wittrock, M. C. (2001). A Taxonomy for Learning, Teaching, and 
Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives. Pearson. 
Barbour, R. S., & Morgan, D. L. (2017). A New Era in Focus Group Research. Palgrave 
Macmillan UK. 
Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for the Process 
of Cross-Cultural Adaptation of Self-Report Measures. Spine, 25(24), 3186–3191. 
https://doi.org/10.1097/00007632-200012150-00014 
Bétrancourt, M., & Benetos, K. (2018). Why and when does instructional video facilitate 
learning? A commentary to the special issue “developments and trends in learning with 
instructional video”. Computers in Human Behavior, 89, 471–475. 
https://doi.org/10.1016/j.chb.2018.08.035 
Biard, N., Cojean, S., & Jamet, E. (2018). Effects of segmentation and pacing on procedural 
learning by video. Computers in Human Behavior, 89, 411–417. 
   40 
https://doi.org/10.1016/j.chb.2017.12.002 
Boopathiraj, C., & Chellamani, D. K. (2013). Analysis of Test Items on Difficulty Level and 
Discrimination Index in the Test for Research in Education. International Journal of Social 
Science & Interdisciplinary Research, 2(2), 189–193. 
Burbules, N. C. (2006). Rethinking the Virtual. In J. Weiss, J. Nolan, J. Hunsinger, & P. Trifonas 
(Eds.), The International Handbook of Virtual Learning Environments (pp. 37–58). 
Springer. 
Butcher, K. R. (2014). The multimedia principle. In R. E. Mayer (Ed.), The Cambridge 
Handbook of Multimedia Learning (2nd ed., pp. 174–205). Cambridge University Press. 
https://doi.org/10.1017/CBO9781139547369.010 
Calandra, B., Brantley-Dias, L., & Dias, M. (2006). Using Digital Video for Professional 
Development in Urban Schools: A Preservice Teacher’s Experience With Reflection. 
Journal of Computing in Teacher Education, 22(4), 137–145. 
Chen, C., Shih, B., & Yu, S. (2012). Disaster prevention and reduction for exploring teachers’ 
technology acceptance using a virtual reality system and partial least squares techniques. 
Natural Hazards, 62(3), 1217–1231. https://doi.org/10.1007/s11069-012-0146-0 
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Routledge. 
Cook, D. A., Castillo, R. M., Gas, B., & Artino, A. R. (2017). Measuring achievement goal 
motivation, mindsets and cognitive load: validation of three instruments’ scores. Medical 
Education, 51(10), 1061–1074. https://doi.org/10.1111/medu.13405 
Correll, M., Bertini, E., & Franconeri, S. (2020). Truncating the Y-Axis: Threat or Menace? 
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–12. 
https://doi.org/10.1145/3313831.3376222 
   41 
Cortright, R. N., Lujan, H. L., Blumberg, A. J., Cox, J. H., & Dicarlo, S. E. (2013). Higher levels 
of intrinsic motivation are related to higher levels of class performance for male but not 
female students. Advances in Physiology Education, 37(3), 227–232. 
https://doi.org/10.1152/advan.00018.2013 
Coyne, E., Rands, H., Frommolt, V., Kain, V., Plugge, M., & Mitchell, M. (2018). Investigation 
of blended learning video resources to teach health students clinical skills: An integrative 
review. Nurse Education Today, 63, 101–107. https://doi.org/10.1016/j.nedt.2018.01.021 
Creswell, J. W., & Clark, V. L. P. (2017). Designing and Conducting Mixed Methods Research 
(3rd ed.). SAGE. 
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 
16(3), 297–334. https://doi.org/10.1007/BF02310555 
Cumming, G. (2013). Understanding The New Statistics. Routledge. 
https://doi.org/10.4324/9780203807002 
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. 
https://doi.org/10.1080/15213269.2015.1015740 
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of 
information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. 
https://doi.org/10.2307/249008 
de Koning, B. B., Hoogerheide, V., & Boucheix, J. M. (2018). Developments and Trends in 
Learning with Instructional Video. Computers in Human Behavior, 89, 395–398. 
https://doi.org/10.1016/j.chb.2018.08.055 
DeLozier, S. J., & Rhodes, M. G. (2017). Flipped Classrooms: a Review of Key Ideas and 
   42 
Recommendations for Practice. Educational Psychology Review, 29(1), 141–151. 
https://doi.org/10.1007/s10648-015-9356-9 
DeVellis, R. F. (2016). Scale Development Theory and Applications (4th ed.). SAGE. 
Domagk, S., Schwartz, R. N., & Plass, J. L. (2010). Interactivity in multimedia learning: An 
integrated model. Computers in Human Behavior, 26(5), 1024–1033. 
https://doi.org/10.1016/j.chb.2010.03.003 
Donkor, F. (2010). The comparative instructional effectiveness of print-based and video-based 
instructional materials for teaching practical skills at a distance. The International Review of 
Research in Open and Distributed Learning, 11(1), 96–116. 
https://doi.org/10.19173/irrodl.v11i1.792 
Donkor, F. (2011). Assessment of Learner Acceptance and Satisfaction with Video-Based 
Instructional Materials for Teaching Practical Skills at a Distance. International Review of 
Research in Open and Distributed Learning, 12(5), 74–92. 
https://doi.org/10.19173/irrodl.v12i5.953 
Döring, N., & Bortz, J. (2016). Forschungsmethoden und Evaluation in den Sozial- und 
Humanwissenschaften (5th ed.). Springer. https://doi.org/10.1007/978-3-642-41089-5 
Evans, C., & Gibbons, N. J. (2007). The interactivity effect in multimedia learning. Computers 
and Education, 49(4), 1147–1160. https://doi.org/10.1016/j.compedu.2006.01.008 
Field, A. P. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE. 
Giannakos, M. N. (2013). Exploring the video-based learning research: A review of the 
literature. British Journal of Educational Technology, 44(6), 191–195. 
https://doi.org/10.1111/bjet.12070 
Giannakos, M. N., Jaccheri, L., & Krogstie, J. (2014). Looking at MOOCs rapid growth through 
   43 
thelens of video-based learning research. International Journal of Emerging Technologies 
in Learning, 9(1), 35–38. https://doi.org/10.3991/ijet.v9i1.3349 
Hamilton, D., McKechnie, J., Edgerton, E., & Wilson, C. (2020). Immersive virtual reality as a 
pedagogical tool in education: a systematic literature review of quantitative learning 
outcomes and experimental design. Journal of Computers in Education. 
https://doi.org/10.1007/s40692-020-00169-2 
Hennink, M. M., & Leavy, P. (2014). Understanding Focus Group Discussions. Oxford 
University Press. https://doi.org/10.1093/acprof:osobl/9780199856169.001.0001 
Holm, S. (1979). A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian 
Journal of Statistics, 6(2), 65–70. 
Huang, H. M., & Liaw, S. S. (2018). An analysis of learners’ intentions toward virtual reality 
learning based on constructivist and technology acceptance approaches. International 
Review of Research in Open and Distance Learning, 19(1), 91–115. 
https://doi.org/10.19173/irrodl.v19i1.2503 
Jensen, L., & Konradsen, F. (2018). A review of the use of virtual reality head-mounted displays 
in education and training. Education and Information Technologies, 23(4), 1515–1529. 
https://doi.org/10.1007/s10639-017-9676-0 
Kay, R. H., & Edwards, J. (2012). Examining the Use of Worked Example Video Podcasts in 
Middle School Mathematics Classrooms: A Formative Analysis. Canadian Journal of 
Learning and Technology, 38(3), 1–20. https://doi.org/10.21432/t2pk5z 
Klepsch, M., Schmitz, F., & Seufert, T. (2017). Development and validation of two instruments 
measuring intrinsic, extraneous, and germane cognitive load. Frontiers in Psychology, 8, 1–
18. https://doi.org/10.3389/fpsyg.2017.01997 
   44 
Knight, M. J., & Tlauka, M. (2017). Interactivity in map learning: The effect of cognitive load. 
Spatial Cognition & Computation, 17(3), 185–198. 
https://doi.org/10.1080/13875868.2016.1211661 
Knodel, J. (1993). The Design and Analysis of Focus Group Studies: A Practical Approach. In 
D. L. Morgan (Ed.), Successful Focus Groups: Advancing the State of the Art (pp. 35–50). 
SAGE. https://doi.org/10.4135/9781483349008.n3 
Krueger, R. A., & Casey, M. A. (2014). A Practical Guide for Applied Research (5th ed.). 
SAGE. 
Kuckartz, U. (2014). Qualitative Text Analysis: A Guide to Methods, Practice & Using Software. 
SAGE. https://doi.org/10.4135/9781446288719 
Lee, D., Moon, J., & Kim, Y. J. (2007). The effect of simplicity and perceived control on 
perceived ease of use. Association for Information Systems - 13th Americas Conference on 
Information Systems, AMCIS 2007: Reaching New Heights, 3, 1764–1776. 
Lee, E. A.-L., Wong, K. W., & Fung, C. C. (2010). How does desktop virtual reality enhance 
learning outcomes? A structural equation modeling approach. Computers & Education, 
55(4), 1424–1442. https://doi.org/10.1016/j.compedu.2010.06.006 
Lee, J., Kim, J., & Choi, J. Y. (2019). The adoption of virtual reality devices: The technology 
acceptance model integrating enjoyment, social interaction, and strength of the social ties. 
Telematics and Informatics, 39, 37–48. https://doi.org/10.1016/j.tele.2018.12.006 
Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and Extrinsic Motivational 
Orientations in the Classroom: Age Differences and Academic Correlates. Journal of 
Educational Psychology, 97(2), 184–196. https://doi.org/10.1037/0022-0663.97.2.184 
Leppink, J., Paas, F. G. W. C., Van der Vleuten, C. P. M., Van Gog, T., & Van Merriënboer, J. J. 
   45 
G. (2013). Development of an instrument for measuring different types of cognitive load. 
Behavior Research Methods, 45(4), 1058–1072. https://doi.org/10.3758/s13428-013-0334-1 
Lin, C., & Tseng, Y. (2012). Videos and animations for vocabulary learning: A study on difficult 
words. Turkish Online Journal of Educational Technology, 11(4), 346–355. 
Lindgren, R., Pea, R., Lewis, S., & Rosen, J. (2007). Learning from digital video: An exploration 
of how interactions affect outcomes. International Society of the Learning Sciences, 
Proceedings of the 8th international conference on Computer supported collaborative 
learning, 447–449. 
Linnenbrink, E. A., & Pintrich, P. R. (2002). Motivation as an enabler for academic success. 
School Psychology Review, 31(3), 313–327. 
https://doi.org/10.1080/02796015.2002.12086158 
Liu, S. H., Liao, H. L., & Pratt, J. A. (2009). Impact of media richness and flow on e-learning 
technology acceptance. Computers and Education, 52(3), 599–607. 
https://doi.org/10.1016/j.compedu.2008.11.002 
Makransky, G., Borre‐Gude, S., & Mayer, R. E. (2019). Motivational and cognitive benefits of 
training in immersive virtual reality based on multiple assessments. Journal of Computer 
Assisted Learning, 35(6), 691–707. https://doi.org/10.1111/jcal.12375 
Makransky, G., & Lilleholt, L. (2018). A structural equation modeling investigation of the 
emotional value of immersive virtual reality in education. Educational Technology 
Research and Development, 66(5), 1141–1164. https://doi.org/10.1007/s11423-018-9581-2 
Makransky, G., Terkildsen, T. S., & Mayer, R. E. (2019). Adding immersive virtual reality to a 
science lab simulation causes more presence but less learning. Learning and Instruction, 60, 
225–236. https://doi.org/10.1016/j.learninstruc.2017.12.007 
   46 
Masadeh, M. A. (2012). Focus Group: Reviews and Practices. International Journal of Applied 
Science and Technology, 2(10), 63–68. 
Mayer, R. E. (2012). Multimedia Learning (2nd ed.). Cambridge University Press. 
https://doi.org/10.1017/CBO9780511811678 
Mayer, R. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted Learning, 
33(5), 403–423. https://doi.org/10.1111/jcal.12197 
Mayer, R. E., & Pilegard, C. (2014). Principles for managing essential processing in multimedia 
learning: Segmenting, pre-training, and modality principles. In R. E. Mayer (Ed.), The 
Cambridge Handbook of Multimedia Learning, Second Edition (pp. 316–344). 
https://doi.org/10.1017/CBO9781139547369.016 
Mcmillan, S. J., & Hwang, J. S. (2002). Measures of Perceived Interactivity: An Exploration of 
the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of 
Interactivity. Journal of Advertising, 31(3), 29–42. 
https://doi.org/10.1080/00913367.2002.10673674 
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 and Education, 140. 
https://doi.org/10.1016/j.compedu.2019.103603 
Moreno, R., & Mayer, R. E. (2007). Interactive multimodal learning environments: Special issue 
on interactive learning environments: Contemporary issues and trends. Educational 
Psychology Review, 19(3), 309–326. https://doi.org/10.1007/s10648-007-9047-2 
Morse, J. M. (1991). Approaches to qualitative-quantitative methodological triangulation. 
Nursing Research, 40(2), 120–123. https://doi.org/10.1097/00006199-199103000-00014 
   47 
Mutlu-Bayraktar, D., Cosgun, V., & Altan, T. (2019). Cognitive load in multimedia learning 
environments: A systematic review. Computers and Education, 141. 
https://doi.org/10.1016/j.compedu.2019.103618 
Nichols, S. (1999). Physical ergonomics of virtual environment use. Applied Ergonomics, 30(1), 
79–90. https://doi.org/10.1016/S0003-6870(98)00045-3 
Nikopoulou-Smyrni, P., & Nikopoulos, C. (2010). Evaluating the impact of video-based versus 
traditional lectures on student learning. Proceedings of the 7th European Conference on E-
Learning, ECEL 2008, 2, 214–221. 
Olejnik, S., & Algina, J. (2003). Generalized Eta and Omega Squared Statistics: Measures of 
Effect Size for Some Common Research Designs. Psychological Methods, 8(4), 434–447. 
https://doi.org/10.1037/1082-989X.8.4.434 
Paas, F. G. W. C. (1992). Training Strategies for Attaining Transfer of Problem-Solving Skill in 
Statistics: A Cognitive-Load Approach. Journal of Educational Psychology, 84(4), 429–
434. https://doi.org/10.1037/0022-0663.84.4.429 
Paas, F. G. W. C., Renkl, A., & Sweller, J. (2003). Cognitive Load Theory and Instructional 
Design: Recent Developments. Educational Psychologist, 38(1), 1–4. 
https://doi.org/10.1207/S15326985EP3801_1 
Park, B., & Brünken, R. (2015). The Rhythm Method: A New Method for Measuring Cognitive 
Load - An Experimental Dual-Task Study. Applied Cognitive Psychology, 29(2), 232–243. 
https://doi.org/10.1002/acp.3100 
Parong, J., & Mayer, R. E. (2018). Learning science in immersive virtual reality. Journal of 
Educational Psychology, 110(6), 785–797. https://doi.org/10.1109/CDC.2017.8264526 
Quaigrain, K., Arhin, A. K., & King Fai Hui, S. (2017). Using reliability and item analysis to 
   48 
evaluate a teacher-developed test in educational measurement and evaluation. Cogent 
Education, 4(1). https://doi.org/10.1080/2331186X.2017.1301013 
Renkl, A., Atkinson, R. K., Renkl, A., & Atkinson, R. K. (2007). Interactive Learning 
Environments: Contemporary Issues and Trends. An Introduction to the Special Issue. Educ 
Psychol Rev, 19, 235–238. https://doi.org/10.1007/s10648-007-9052-5 
Roussou, M., & Slater, M. (2017). Comparison of the Effect of Interactive versus Passive Virtual 
Reality Learning Activities in Evoking and Sustaining Conceptual Change. IEEE 
Transactions on Emerging Topics in Computing, 8(1), 233–244. 
https://doi.org/10.1109/TETC.2017.2737983 
Rupp, M. A., Odette, K. L., Kozachuk, J., Michaelis, J. R., Smither, J. A., & McConnell, D. S. 
(2019). Investigating learning outcomes and subjective experiences in 360-degree videos. 
Computers and Education, 128, 256–268. https://doi.org/10.1016/j.compedu.2018.09.015 
Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of 
cognitive evaluation theory. Journal of Personality and Social Psychology, 43(3), 450–461. 
https://doi.org/10.1037/0022-3514.43.3.450 
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic 
motivation, social development, and well-being. American Psychologist, 55(1), 68–78. 
https://doi.org/10.1037/0003-066X.55.1.68 
Ryan, R. M., & Deci, E. L. (2009). Promoting self-determined school engagement: Motivation, 
learning, and well-being. In K. R. Wenzel & A. Wigfield (Eds.), Educational psychology 
handbook series. Handbook of motivation at school (pp. 171–195). Routledge. 
Santagata, R. (2009). Designing video-based professional development for mathematics teachers 
in low-performing schools. Journal of Teacher Education, 60(1), 38–51. 
   49 
https://doi.org/10.1177/0022487108328485 
Schnotz, W., & Kürschner, C. (2007). A eeconsideration of cognitive load theory. Educational 
Psychology Review, 19(4), 469–508. https://doi.org/10.1007/s10648-007-9053-4 
Song, Y., & Kong, S. C. (2017). Investigating Students’ Acceptance of a Statistics Learning 
Platform Using Technology Acceptance Model. Journal of Educational Computing 
Research, 55(6), 865–897. https://doi.org/10.1177/0735633116688320 
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. Springer. 
https://doi.org/10.1007/978-1-4419-8126-4 
Sweller, J., Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive Architecture and 
Instructional Design. Educational Psychology Review, 10(3), 251–296. 
https://doi.org/10.1023/A:1022193728205 
Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (2019). Cognitive Architecture and 
Instructional Design: 20 Years Later. Educational Psychology Review, 31(2), 261–292. 
https://doi.org/10.1007/s10648-019-09465-5 
Szulewski, A., Gegenfurtner, A., Howes, D. W., Sivilotti, M. L. A., & van Merriënboer, J. J. G. 
(2017). Measuring physician cognitive load: validity evidence for a physiologic and a 
psychometric tool. Advances in Health Sciences Education, 22(4), 951–968. 
https://doi.org/10.1007/s10459-016-9725-2 
Tabachnick, B. G., & Fidell, L. S. (2019). Using Multivariate Statistics (7th ed.). Pearson. 
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of 
Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd 
Ulrich, F., Helms, N. H., Frandsen, U. P., & Rafn, A. V. (2019). Learning effectiveness of 360° 
video: experiences from a controlled experiment in healthcare education. Interactive 
   50 
Learning Environments, 1–14. https://doi.org/10.1080/10494820.2019.1579234 
van Gog, T., & Paas, F. G. W. C. (2008). Instructional Efficiency: Revisiting the Original 
Construct in Educational Research. Educational Psychologist, 43(1), 16–26. 
https://doi.org/10.1080/00461520701756248 
Venkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on 
Interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-
5915.2008.00192.x 
Watson, S. L., Watson, W. R., Yu, J. H., Alamri, H., & Mueller, C. (2017). Learner profiles of 
attitudinal learning in a MOOC: An explanatory sequential mixed methods study. 
Computers and Education, 114, 274–285. https://doi.org/10.1016/j.compedu.2017.07.005 
Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence 
questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225–240. 
https://doi.org/10.1162/105474698565686 
Wittrock, M. C. (1991). Generative Teaching of Comprehension. The Elementary School 
Journal, 92(2), 169–184. https://doi.org/10.1086/461686 
Wu, B., Yu, X., & Gu, X. (2020). Effectiveness of immersive virtual reality using head-mounted 
displays on learning performance: A meta-analysis. British Journal of Educational 
Technology, 51(6), 1991–2005. https://doi.org/10.1111/bjet.13023 
Yousef, A. M. F., Chatti, M. A., & Schroeder, U. (2014). Video-based learning: A critical 
analysis of the research published in 2003-2013 and future visions. ELmL - International 
Conference on Mobile, Hybrid, and On-Line Learning, June 2015, 112–119. 
Zahn, C., Barquero, B., & Schwan, S. (2004). Learning with hyperlinked videos - Design criteria 
and efficient strategies for using audiovisual hypermedia. Learning and Instruction, 14(3), 
   51 
275–291. https://doi.org/10.1016/j.learninstruc.2004.06.004 
Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker, J. F. (2006). Instructional video in e-learning: 
Assessing the impact of interactive video on learning effectiveness. Information & 
Management, 43(1), 15–27. https://doi.org/10.1016/j.im.2005.01.004 
Zhang, L., Bowman, D. A., & Jones, C. N. (2019). Exploring Effects of Interactivity on Learning 
with Interactive Storytelling in Immersive Virtual Reality. 11th International Conference on 
Virtual Worlds and Games for Serious Applications (VS-Games), 1–8. 
https://doi.org/10.1109/VS-Games.2019.8864531 
 
  
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Appendix A 
Duration of the multimedia lesson for all experimental conditions 
Table A1 
Total duration of the multimedia lesson 
 Video   VR 
 M SD M SD 
 Interactive 15.24 0.50 19.52 1.52 
 Passive 12.52 0.00 14.78 0.48 
Note. Data is depicted in min. 
 
Table A2 
Duration of the tutorial 
 Video   VR 
 M SD M SD 
 Interactive 2.84 0.36 6.91 1.62 
 Passive 0.95 0.00 3.19 0.49 
Note. Data is depicted in min. 
  
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Appendix B 
Paper-pencil questionnaire 
Note. The items “Ich habe mich angestrengt, mir nicht nur einzelne Dinge zu merken, sondern 
auch den Gesamtzusammenhang zu verstehen.” (germane cognitive load) and “Aus welchem 
Grund wurden Röntgengeräte an Flughäfen erstmals eingeführt?” (learning outcomes) were not 
included for ANOVAs due to poor Cronbach’s α and high item difficulty index, respectively. 
 
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Appendix C 
Focus group discussion guide 
Note. The discussion guide also included questions focusing mainly on the formative evaluation 
of the multimedia lesson, which was not assessed in the scope of this study. 
  
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Appendix D 
Q-Q plots of all scales 
Figure D1 
Q-Q plot for learning outcomes 
 
 
Figure D2 
Q-Q plot for intrinsic cognitive load 
6  
  
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Figure D3 
Q-Q plot for extraneous cognitive load 
 
 
Figure D4 
Q-Q plot for germane cognitive load 
 
  
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Figure D5 
Q-Q plot for mental effort 
 
 
Figure D6 
Q-Q plot for intrinsic motivation 
 
  
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Figure D7 
Q-Q plot for perceived usefulness 
 
 
Figure D8 
Q-Q plot for perceived ease of use 
 
 
  
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Figure D9 
Q-Q plot for behavioural intention