Effects of Level of Immersion on Virtual Training Transfer of Bimanual Assembly Tasks

Abstract

The availability of consumer-facing virtual reality (VR) headsets makes virtual training an attractive alternative to expensive traditional training. Recent works showed that virtually trained workers perform bimanual assembly tasks equally well as ones trained with traditional methods. This paper presents a study that investigated how levels of immersion affect learning transfer between virtual and physical bimanual gearbox assembly tasks. The study used a with-in subject design and examined three different virtual training systems i.e., VR training with direct 3D inputs (HTC VIVE Pro), VR training without 3D inputs (Google Cardboard), and passive video-based training. 23 participants were recruited. The training effectiveness was measured by participant’s performance of assembling 3D-printed copies of the gearboxes in two different timings: immediately after and 2 weeks after the training. The result showed that participants preferred immersive VR training. Surprisingly, despite being less favourable, the subjects’ performance of video-based training were similar to training on HTC VIVE Pro. However, video training led to a significant performance decrease in the retention test session 2 weeks after the training.

Full text

Effects of Level of Immersion on
Virtual Training Transfer of Bimanual
Assembly Tasks
Songjia Shen
1
*, Hsiang-Ting Chen
2
, William Raffe
1
and Tuck Wah Leong
1
1
School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia,
2
School of Computer Science, The
University of Adelaide, Adelaide, SA, Australia
The availability of consumer-facing virtual reality (VR) headsets makes virtual training an
attractive alternative to expensive traditional training. Recent works showed that virtually
trained workers perform bimanual assembly tasks equally well as ones trained with
traditional methods. This paper presents a study that investigated how levels of
immersion affect learning transfer between virtual and physical bimanual gearbox
assembly tasks. The study used a with-in subject design and examined three different
virtual training systems i.e., VR training with direct 3D inputs (HTC VIVE Pro), VR training
without 3D inputs (Google Cardboard), and passive video-based training. 23 participants
were recruited. The training effectiveness was measured by participant’s performance of
assembling 3D-printed copies of the gearboxes in two different timings: immediately after
and 2 weeks after the training. The result showed that participants preferred immersive VR
training. Surprisingly, despite being less favourable, the subjects’performance of video-
based training were similar to training on HTC VIVE Pro. However, video training led to a
significant performance decrease in the retention test session 2 weeks after the training.
Keywords: assistive systems, head-mounted display, virtual reality, learning transfer, assembly, training
1 INTRODUCTION
Assembly workers on production lines require constant training to maintain productivity and to
ensure that they continue to work safely. Traditional training is often done at the existing assembly
line in the factory. This has several disadvantages, including a reduction of productivity due to a need
to suspend the assembly line, a potential waste of expensive materials, and the potential for the
trainee to be exposed to physical danger during the training. In this context, virtual reality (VR)
training is a good proposition for the manufacturing industry as it leads to minimum disruptions of
the production environment and a potentially safer training process. Indeed, several companies in
the automotive industry, such as Audi, Ford, and Toyota, have already piloted virtual reality training
as part of their employee training programs (Jiang, 2011;Berg and Vance, 2017).
Decades of VR research has provided evidence of the effectiveness of virtual reality training in a
wide variety of manufacturing and assembly tasks (Bailenson et al., 2008;Berg and Vance, 2017).
Recent works (Oren et al., 2012;Carlson et al., 2015;Murcia-Lopez and Steed, 2018) compared the
effectiveness of virtual training and physical training for bimanual assembly tasks of solving 3D burr
puzzles. The result showed no significant performance difference between the virtual training and
physical training conditions, in terms of assembly times and success rates. However, how the level of
immersion i.e., the objective level of sensory fidelity (Bowman and McMahan, 2007), of the virtual
training affects the learning transfer from the virtual training to physical assembly tasks is still an
Edited by:
Michael Madary,
University of the Pacific, United States
Reviewed by:
Henrique Galvan Debarba,
IT University of Copenhagen, Denmark
Mark Billinghurst,
University of South Australia, Australia
*Correspondence:
Songjia Shen
songjia.shen@student.uts.edu.au
Specialty section:
This article was submitted to
Virtual Reality and Human Behaviour,
a section of the journal
Frontiers in Virtual Reality
Received: 21 August 2020
Accepted: 03 May 2021
Published: 20 May 2021
Citation:
Shen S, Chen H-T, Raffe W and
Leong TW (2021) Effects of Level of
Immersion on Virtual Training Transfer
of Bimanual Assembly Tasks.
Front. Virtual Real. 2:597487.
doi: 10.3389/frvir.2021.597487
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974871
ORIGINAL RESEARCH
published: 20 May 2021
doi: 10.3389/frvir.2021.597487

open question. Previous experimental protocols focused on the
comparison between virtual and physical training. Participants
only experienced the virtual training with identical or similar level
of fidelity e.g., using the same virtual reality headset but with
different types of training instructions and materials.
This paper presents a study that examined the effects of level of
immersion on virtual training transfer of bimanual assembly
tasks. Participants learned the assembly procedure of
functional gearboxes using Video (a pre-recorded video of
animated assembly process shown on a tablet), Mobile VR
(Google Cardboard), and PC VR (HTC VIVE Pro), as shown
in Figure 1. During the Video condition, the participant passively
watched looping videos. The Mobile VR provided a stationary
semi-immersive experience, where the participant can freely look
around the virtual world but can only interact with gearbox pieces
with fuse button and default Cardboard Reticle. Lastly, the PC VR
provided a higher level of immersion, where the participants
manipulated the virtual gearbox pieces with 3D-tracked VIVE
controllers on both hands. These three levels of immersion were
chosen because they represent the most commonly used virtual
training systems with significant differences in input modalities
and cost differences.
The effectiveness of virtual training was measured in two post-
training test sessions which happened immediately after the
training (immediate test) and 2 weeks later (retention test). In
both tests, the participants were instructed to physically assemble
the 3D-printed version of the gearboxes. The measurements
include subjective questionnaire feedback, task completion
time, and number of assembly errors.
This study tests the following hypotheses:
•H1: participants would prefer virtual training with a higher
level of immersion as the learning experience is more engaging.
•H2: virtual training with a higher level of immersion would
yield better assembly performance in both the immediate and
retention test sessions, as the immersive virtual training was
more memorable and could assist memory recall.
•H3: virtual training with direct 3D-input techniques would
yield the best assembly performance, as the hand movements
would improve the memory and retrieval of the assembly
procedure.
This paper contributes to the understanding of the benefitof
immersion (or lack of) toward virtual learning transfer of
bimanual assembly tasks. The study is the first to investigate
the effectiveness of three representative virtual training platforms
i.e., Video,Mobile VR, and PC VR, with a practical gearbox
assembly task. We hope the findings presented in this paper will
lead to the design of new virtual training systems and provide
insights for companies that are interested in deploying virtual
training for their employees.
2 RELATED WORK
Previous works proposed and evaluated virtual reality training
systems in a wide range of domains, such as medical (Stanney
et al., 1998;Ruthenbeck and Reynolds, 2015), manufacturing
(Boud et al., 1999;Wang et al., 2016), and military (Adams et al.,
2001;Bhagat et al., 2016). Here, we highlight some research that
focused on the virtual training of assembly and procedural tasks.
FIGURE 1 | A user learning assembly tasks in Video,Mobile VR,andPC VR conditions and assembling a 3D-printed gearbox on the table.
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974872
Shen et al. Effects of Level of Immersion

Although many of them have showed the training benefits, the
effectiveness of virtual environments remains inconclusive due to
the use of different reference conditions.
(Adams et al., 2001) compared the learning effectiveness
among video, virtual training without haptic feedback, and
virtual training with haptic feedback for assembling a LEGO
biplane model. They reported that virtual training with haptics
had significantly better performance than video. (Gavish et al.,
2015) evaluated video, AR and VR training platforms using an
electronic actuator assembly task and found AR training
resulted in fewer unresolved errors than video watching with
physical practice while there were no significant difference in
performance between VR training and video watching only. Hall
and Horwitz (2001) compared training of device operating
between a VR interface (using head-mounted display and
PINCH gloves) and a 2D interface (using conventional
computer monitor and mouse) and found no significant
difference in performance between the groups. Sowndararajan
et al. (2008) compared an immersive projection display with a
laptop display for memorizing procedures of different
complexity. The result showed that higher immersion
resulted in better training outcomes only for the more
complex procedure. de Moura and Sadagic (2019) evaluated
the effects of various combinations of stereopsis and immersive
display on assembly training with image-based instructions and
reported the immersive stereopsis group outperformed the
others.
Instead of using 2D-based training as a control condition,
other studies compared virtual training with physical training in
real environments. Gonzalez-Franco et al. (2017) studied
collaborative training of assembling aircraft doors in a mixed
reality setup and a conventional face-to-face scenario and found
no significant difference in performance. Funk et al. (2017)
conducted an 11-days study in an industrial assembly
workplace and found that augmented reality was helpful for
untrained workers. Hoedt et al. (2017) evaluated assembly
training in a mixed setup with hand tracking enabled and
found it led to similar performance as the physical training.
Besides, some research also examined effects of different types of
instructions on assembly tasks (Yuviler-Gavish et al., 2011;
Rodríguez et al., 2012).
Despite the mixed findings, researchers have created a range of
applications for assembly and procedural training, considering
the various learning affordances enabled by virtual environments
(Dalgarno and Lee, 2010). Ritter et al. (2001) developed a VR
training application for anatomy education (composing bones
and muscles of a 3D virtual foot) and mechanical engineering
(assembling a car engine). Gerbaud et al. (2008) created a VR
authoring platform based on two previously developed modules
(Mollet and Arnaldi, 2006;Mollet et al., 2007), which could
support defining object behaviors and operation sequences
programmatically for various procedural tasks. Gorecky et al.
(2017) designed and implemented a virtual training system for
automotive manufacturing, focusing on automation of training
content generation. There are also various attempts to integrate
computer-aid design models and virtual assembly (Leu et al.,
2013).
Some recent works used puzzle pieces as the training target.
Such abstract assembly tasks appear to provide better control over
the task difficulty and reduces the potential confounders of pre-
acquired domain knowledge. Shuralyov and Stuerzlinger (2011)
implemented a mouse based system for assembling 3D puzzles on
a desktop monitor and recruited two groups of participants
(novices and experts) based on their time spent on computers
per day and games per week. The result showed that the expert
group assembled the puzzle significantly faster and both groups
spent more time on rotations. Carlson et al. (2015), based on their
previous work (Oren et al., 2012), compared the training
effectiveness of a 6-piece burr puzzle assembly task between
physical training and virtual training (with motion controls
and haptic feedback). The result showed that the participants
receiving physical training outperformed the ones receiving
virtual training in the test right after the training. However,
for the groups with color cues, virtual and physical training
became equally effective in the retention test, 2 weeks after the
training. Murcia-Lopez and Steed (2018) used a similar
experimental protocol as Carlson et al. (2015) and rigorously
examined six conditions that mixed physical and virtual training
elements, using three puzzles with different complexity levels.
The result showed that the virtual training could perform equally
well as the physical training although all training methods led to
poor performance in the retention session.
Complementing these previous studies (Oren et al., 2012;
Carlson et al., 2015;Murcia-Lopez and Steed, 2018), our
experiment used a similar experimental protocol (Murcia-
Lopez and Steed, 2018) while using functional 3D-printed
gearboxes as our assembly training targets, which better
resembled actual assembly in production environments and
still kept good control of task difficulty and certain level of
abstraction. In addition, our experiment specifically examined
the effects of different levels of immersion during the virtual
training. In line with previous research (Young et al., 2014;
Papachristos et al., 2017) on cost-differentiated VR systems,
the experiment aimed to understand the benefits (or the
limitations) of high-end VR system in virtual training. While
the previous works compared the training effectiveness of various
virtual environments with physical environments, it has not been
examined in detail how different levels of immersion affect
training transfer of assembly tasks. Since virtual systems could
require significantly different training costs and times, selecting
appropriate immersion levels for tasks could be crucial to deploy
virtual training in production environments.
3 EXPERIMENTAL DESIGN
Our experiment used a within-subject design where participants
learned the gearbox assembly procedure in three different
conditions of virtual training:
•Video: The participant passively watched a pre-recorded
animation using VLC on a 12-inch tablet in a sitting pose.
The participant could start video play by tapping the video
thumbnail.
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974873
Shen et al. Effects of Level of Immersion

•Mobile VR: The participant wore a low-cost Google Cardboard
viewer powered by a smart phone (Samsung Galaxy S8). The
participant could manipulate the virtual pieces with the fuse
button and the display reticle.
•PC VR: The participant wore a wireless HTC VIVE Pro headset
and held one 3D-tracked controller on each hand. The
participant could grasp and assemble the virtual gearbox
pieces using both hands and could freely move around the
virtual gearbox. The participant completed the training in a
standing pose.
We concur with Dalgarno and Lee (2010) on the view that the
representational fidelity and the interactivity types afforded by
the virtual environment lead to the degree of immersion. These
three different virtual training conditions represent three
different levels of immersion: Low, Mid, High. Table 1
summarizes the major differences in their display
specifications and freedom to interact with the virtual
environments.
The rendering quality of PC VR and Mobile VR conditions
were similar because of the low number of polygons on 3D assets
and virtual environment and the use of plain lighting. The
animations used in the Video condition were rendered and
recorded on PC using the same scene.
Note that unlike previous works (Oren et al., 2012;Carlson
et al., 2015;Murcia-Lopez and Steed, 2018) that used a between-
subject experiment design to mitigate the learning effects. Our
within-subject experiment design let the participant assemble
different gearboxes for each training condition. The pairing of
gearboxes and training conditions was based on a 3×3 Latin
square design. We assume these three gearboxes represent the
same difficulty level to the participants because of the similar
complexity, i.e. almost same numbers of assembly pieces and
steps. Please find more discussion regarding the complexity of
gearbox assembly in Section 5.1.
3.1 Apparatus
3.1.1 3D-Printed Gearboxes
We 3D printed the target gearboxes (see Figure 2A) using the HP
Jet Fusion 4200 3D printer with the material PA-12GB, which is
Nylon 12 with glass bead reinforcement. Each gearbox piece is
stiff and reusable. None of the pieces broke during the whole
user study.
TABLE 1 | The experimental conditions of low, middle and high immersion.
Video Mobile VR PC VR
Resolution 1,920 ×1,080 2,220 ×1,080 2,280 ×1,600
Refresh rate 60 Hz 60 Hz 90 Hz
Field of view —80°110°
User input method —Cardboard reticle 3D inputs
Head movement tracking —3 DoF 6 DoF
Level of immersion Low Mid High
FIGURE 2 | (A) 3D-printed gearboxes, and (B) cost-differentiated VR systems.
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974874
Shen et al. Effects of Level of Immersion

3.1.2 VR Headsets With Different Levels of Immersion
Figure 2B shows the two VR head-mounted displays used in the
experiment, Google Cardboard and HTC VIVE Pro. As of November
2019, Google Cardboard costs around USD 15 while HTC VIVE Pro
is priced at USD 799. Both headsets require an external device to
drive the VR content and support stereoscopic rendering.
The two VR systems have many differences in graphics
characteristics and input methods. HTC VIVE Pro has a
better display with higher resolution, refresh rate, and field of
view. The virtual environment rendering was driven by a
powerful PC. HTC VIVE Pro also allows room-scale VR with
direct 3D inputs and free walking in the space. In contrast, the
phone-based VR system provided a limited field of view, had
limited graphics processing power, and exploited the IMU on the
smartphone to achieve simple inputs (see Table 1).
3.2 Gearbox Assembly
We chose functional gearboxes as our training targets. Figure 3
shows the exploded views of these gearboxes and the corresponding
3D models, downloaded from Thingiverse
1
. These gearboxes were
designed for training purposes and had several classical
mechanisms. The gearbox 1 (left) had a double reduction gear
mechanism that comprised of two pairs of gears. The gearbox 2
(center) mechanism had two bevel gears and two shafts that were
90°apart. The gearbox 3 (right) was a standard worm gearbox with
both worm and worm gear at a gear ratio of 1:30.
All three gearboxes had similar numbers of components and
assembly steps (see Table 2). During the virtual training, any kind
of translation or rotation of the components was counted as one
assembly step. The disparities in assembly steps between Mobile
VR and PC VR were due to the different degree-of-freedom (DoF)
in the input methods. The HTC Vive controller provided a 6 DoF
control and the participant could control both the translation and
rotation of the virtual piece in one step. While the Mobile VR
control was limited to 3 Dof and thus required more steps to
complete the gearbox assembly.
3.3 Virtual Training
3.3.1 Video - Low Level of Immersion
This condition presented a video of animated assembly instructions
for each gearbox (see Figure 4A). When the video begins, it shows
all pieces of a gearbox positioned in a straight line in the lower part
of the view. Then, according to a fixed sequence, they are moved to
the center of the view and are assembled together step by step. Each
step shows how pieces are translated to a proper position or rotated
to a proper orientation. The participant passively watched the
videos and memorized all the assembly moves. When the video is
over, the participant can restart it by tapping its thumbnail in VLC.
The training session ended when the participant announced that
he/she was confident to start assembling the physical gearbox. Since
this was the non-interactive baseline condition, the participant
could not have any interaction (including video playback controls)
while watching the assembly animation.
3.3.2 Mobile VR - Mid Level of Immersion
This condition used the same models and animated instructions
in the VR application we developed (see Figure 4B). When the
application was started, it showed all pieces of a gearbox with
the same initial arrangement as the Video condition. Following
the same sequence, it played the animated instruction of the
assembly step with a semi-transparent version of the piece. The
semi-transparent piece also emitted a blinking yellow light to
FIGURE 3 | Exploded view of the three gearboxes.
TABLE 2 | The numbers of components and assembly steps.
Pieces Steps: V Steps: M Steps: P
Gearbox 1 8 19 19
a
16
a
Gearbox 2 7 19 19 19
Gearbox 3 8 19 20
a
19
a
a
indicates disparities between two VR conditions.
V for Video, M for Mobile VR, and P for PC VR.
1
https://www.thingiverse.com/thing:2995199
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974875
Shen et al. Effects of Level of Immersion

attract the participant’s attention. Once the animation was over,
the application would wait for user’s inputs. Participants needed
to find the corresponding piece and manipulate it according to
the animation. The application would keep checking the piece. If
it had the same position and orientation as the semi-transparent
one, the current step was completed and next step’s animation
would be played. After all the steps were completed, the gearbox
would rotate by itself to indicate the success of assembly.
Interactions were achieved by using one physical button and
head rotation. In the application, there was a small white dot (the
reticle) at the center of the view. When users rotated their heads
with the Google Cardboard headset, the reticle always remained
at the center. If it pointed to a gearbox piece, the white dot would
become a bigger white circle and the color of the piece would
change to yellow, indicating that this piece was interactive. Then
users had to press and hold the physical button on the upper right
corner of the headset to attach the piece to the reticle. If the
attachment was successful, the color of the reticle would change
to cyan and users could manipulate the piece with head rotation.
Due to the limitation of the input method, in each step, a piece
could either translate or rotate, but not both. The application
decided the operation of current step and indicated it via the
animation. In translation steps, pieces being manipulated would
move with the reticle. In rotation steps, head rotation would be
mapped to piece rotation accordingly. After the button was
released, the head motion stopped affecting the piece and the
color of the reticle changed back to white.
Besides gearbox pieces, there were several other objects that
users could interact with. Two virtual buttons were implemented
in the scene. One was the start button in front of the pieces and
the other was the restart button below the pieces. Users could
point to the virtual buttons and click the physical button to start
or restart the animated instructions. Above the pieces, there was a
small assembled burr puzzle, working as a handle. Rotating the
handle would rotate all gearbox pieces together, so users could
view instructions and assemble pieces from different angles if they
wanted.
3.3.3 PC VR - High Level of Immersion
This condition had almost the same training contents as Mobile
VR condition, expect for a few changes of interactions (see
Figure 4C).
Since HTC VIVE Pro had motion tracking controllers, the
reticle was removed and two models of the controllers were added
in the scene. The virtual controllers had the same movements as
the physical controllers. When a virtual controller collided with a
gearbox piece, the piece would be highlighted by yellow color (just
as Mobile VR condition). When the trigger of physical controller
was pulled and held, the piece would be attached to the virtual
controller. If the attachment was successful, the virtual controller
would become transparent. Users then could use the physical
controller to manipulate gearbox pieces with hand movements. If
the trigger was released, the piece was detached and the virtual
controller turned back to normal.
We introduced a compulsory bimanual mode in this
condition. When the first step was completed, the completed
piece would be attached to the controller. Subjects had to use the
other controller to grab new pieces for the following steps and use
both controllers to assemble them together. Subjects could change
the controller for holding. They just need to use the non-holding
controller to collide with the completed pieces and pull its trigger.
Then the roles of the controllers were switched.
3.3.4 Virtual Training Limitation
Note that we did not enable the physical collision simulation in
the virtual training. The size of virtual gearboxes and the
interlocking nature of gears make the collision detection in
Unity 3D unreliable. Instead, the application had a snap-to-fit
mechanism (Carlson et al., 2015;Murcia-Lopez and Steed, 2018),
FIGURE 4 | (A) Video training: a participant passively watching a pre-
recorded animation during the training, (B) Mobile VR training: a participant
interacting with the virtual pieces using reticle at the center of the view and the
magnet selection button on the cardboard, and (C) PC VR training: a
participant interacting with the virtual gearbox via 3D tracked HTC Vive
controllers.
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974876
Shen et al. Effects of Level of Immersion

which would snap a piece to its semi-transparent guide if they
were close enough. More specifically, the application would snap
the piece to its semi-transparent guide if the piece was entirely
inside the bounding box of the guide, which is 5% larger in size,
and the difference between their orientations was within 10°.
All three conditions only allowed a linear progress of training.
The participants could not disassemble the already assembled
parts or skipped certain assembly steps. We intentionally set this
constraint to ensure a similar training experience among
participants.
4 METHODS
Written informed consent was obtained from the individuals for
the publication of any potentially identifiable images or data
included in this article. This study received approval from the
University’s Human Research Ethics Committee.
4.1 Participants
We recruited 23 adults, whose ages ranged from 19 to 38 (mean 
26.78, SD 4.86), of which 9 were females. All subjects were
reimbursed 30 dollars for their time. Among them, three subjects
are excluded from the analysis, including one for the experience
of assembly (having 5–10 h per week for assembly activities), one
for misunderstanding the training protocol (not realizing that the
training tasks could be completed multiple times) and one for the
completion time in the immediate test (479.35 s, which is five
times of the median value of 94.69 and more than two times of the
second largest value of 217.38). About half of the participants (12
out of 20) had no or little experience using VR (less than 5 h)
before the experiment, and five participants had studied
mechanical engineering.
4.2 Experiment Procedure
The experiment consisted of two lab sessions with a 2-week
waiting period. Figure 5 shows a brief overview of the procedure.
The first session trained the subjects in each experimental
condition and tested their performance immediately after each
training phase. In the second session, the subjects returned to the
same room and were tested for their retention without
undergoing any re-training. Before the experiment, the
participants were asked to read an information sheet
describing the assembly tasks and tests and to sign a paper
copy of the consent form.
In the first session, the participant began by completing the
online background questionnaire with the questions about their
experiences of mechanical engineering, gearboxes, VR, 3D
software, video games and assembly activities. These
questionnaires evaluated their existing domain knowledge and
their familiarity with the relevant technologies.
The participant then completed three virtual-training trials,
one for each training condition. Three different gearboxes with
similar assembly difficulties were used in the session, one for
each trial. Each trial consists of three steps: tutorial,training,
and immediate-test (see Figure 5). In the tutorial step, the
subject learned the controls of the training conditions by
completing a sample assembly task. In the training step, the
subject completed the virtual training condition as described in
Section 3.3. The subject could complete the training content as
many times as they needed within 10 min and could end the
training early. In the immediate-test step, the participant
assembled the 3D-printed gearbox as fast as possible. There
was no time limit for the test.
At the end of the first session, the participant completed an
experience evaluation questionnaire to rate the gearbox assembly
difficulty, instruction clarity, ease of use, usefulness and
preference for each condition (see Table 3). The participant
then participated in a semi-structured interview where they
provided verbal responses about their training experiences and
elaborated answers in the questionnaires.
In the second session, the participant first completed a
retention test by reassembling the 3D-printed gearboxes using
the same order in the first session. The participant was then
interviewed about how they recall the assembly process and their
thoughts about the effectiveness of previous virtual training.
In both sessions, the 3D-printed pieces of the gearboxes were
placed on a table and occluded from user’s vision by a cover. The
participants started the gearbox assembly by pressing a virtual
button on a tablet and completed the process by pressing the same
button again.
4.3 Data Collection
The VR application logged the training times of each assembly
task completed in VR and the timestamps of each step. The
training times spent on video were recorded by the researcher.
These data were also used to calculate how many iterations of
FIGURE 5 | Overview of the experiment procedure.
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974877
Shen et al. Effects of Level of Immersion

the training content the participant finished before the
immediate test.
The completion times of physical assembly were recorded by
the tablet. The assembled 3D-printed gearboxes were examined
and disassembled by the researcher after the experiment and the
misassembled pieces were recorded. The completion times and
the numbers of assembly errors were used to evaluate the
effectiveness of the virtual training.
The background questionnaire and the experience
questionnaire were created and administered via Google
Forms. All interviews were video recorded with the
participant’s consent. All the collected data were saved in the
CSV files (see Supplementary Material).
5 RESULTS
5.1 Gearbox Difficulty
Weassumedallthreegearboxesareofthesamedifficulty and
the results appear to confirm our assumption with the
participants rating the difficulty of each gearbox in the post-
experiment questionnaire similarly. The completion time for
each gearbox in the immediate test session was also similar (see
Figure 6). Non-parametric analysis was performed and
Friedman tests showed there was no statistically significant
difference in ratings of difficulty or completion times
between the different gearboxes.
5.2 Training Times and Iterations
Figure 7 shows the training times and iterations of each training
condition. Non-parametric analysis was performed.
A Friedman test showed there was an overall statistically significant
difference in training times between the experimental conditions
(χ2(2)24.4,p<0.001). A post hoc test using Wilcoxon signed-
rank tests with Bonferroni correction showed the significant differences
between Video and Mobile VR (Z−3.920,p<0.001,r0.877)
and between Video and PC VR (Z−3.696,p<0.001,r0.826).
Video has the smallest median of 97.5 s (IQR 91.75–145.25),
followed by Mobile VR with a median of 222.6 s
(IQR 145.6–286.3) and PC VR has the largest median of
252.09 s (IQR 138.01–346.25).
Although a Friedman test showed there was no overall
statistically significant difference in training iterations, Video still
has the smallest median of 1.07 iterations (IQR 1.04–1.58),
followed by Mobile VR and PC VR with 1.87 iterations (IQR 
1–2) and 1.97 iterations (IQR 1–2) respectively.
5.3 Completion Times
Figure 8 shows the completion times of the gearbox assembly in
both the immediate test and the retention test. Non-parametric
analysis was performed for completion times.
5.3.1 Immediate Test
A Friedman test showed there was an overall statistically
significant difference in completion times between the training
TABLE 3 | Experience questionnaire.
Dimension Question Likert scale extremes
Difficulty How difficult do you think are these gearboxes to assemble? 1 (very easy) - 5 (very hard)
Instruction clarity Do you think the instructions are clear and easy to understand? 1 (very confusing) - 5 (very clear)
Ease of use Do you think the applications are easy to use? 1 (very hard) - 5 (very easy)
Usefulness Do you think the applications are helpful for memorizing the assembly steps? 1 (not helpful) - 5 (very helpful)
Preference How likely do you think these three conditions will be your preferred learning method(s)? 1 (not likely) - 5 (very likely)
FIGURE 6 | Gearbox difficulty ratings and completion times for each
gearbox in the immediate test.
FIGURE 7 | Training times and iterations for each experimental
condition. *means significant difference (p<0.05).
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974878
Shen et al. Effects of Level of Immersion

conditions in the immediate test (χ2(2)6.4,p0.041). A post
hoc test using Wilcoxon signed-rank tests with Bonferroni
correction showed the significant difference between Video
and Mobile VR (Z−2.202,p0.027,r0.493).
Video has the smallest median of 76.04 s (IQR 58.36–88.69),
followed by PC VR with a median of 88.9 s (IQR 61.64–112.94)
and Mobile VR has the largest median of 94.69 s
(IQR 78.08–115.6).
5.3.2 Retention Test
A Friedman test showed there was no overall statistically
significant difference in completion times between the training
conditions in the retention test.
PC VR has the smallest median of 88.04 s
(IQR 66.14–151.56), followed by Mobile VR with a median
of 94.94 s (IQR 62.3–181.39) and Video has the largest median
of 103.05 s (IQR 65.32–139.77).
5.3.3 Comparison Between Two Sessions
Wilcoxon signed-rank tests showed there was a significant
difference in completion times of Video between the immediate test
and the retention test (W42,Z−2.352,p0.017,r0.526)
and no significant difference in completion times of the other two
conditions between the sessions. The median value in the Video
condition significantly increased by 35.53% in the retention test.
5.4 Assembly Errors
5.4.1 Qualification of Errors
All subjects were able to assemble the gearboxes in the immediate
and retention tests. Some of the completed gearboxes had a few
minor errors, which could be categorized into three types as
follows:
•Incorrect orientations of pieces: the pieces were placed in the
correct positions with the wrong orientations. This type of error
does not affect how the gearboxes work. Each piece in this
category is counted as one error. There is only one exception for
gearbox 1. It has three interconnected gears, which are treated
as one for this category of error.
•Misplaced pieces: the pieces were placed in the incorrect
positions. The pieces with this type of errors could still work
together as gearboxes in ways which are a bit different from the
ones of the original gearboxes. Each misplacement is counted as
one error, even though it might involve multiple pieces. For
instance, some participant swapped the positions of two gears,
which is counted as one error instead of two errors.
•Unconnected pieces: the pieces were placed in the correct
positions, but not connected to other pieces. The assembled
gearboxes with this type of error could not work. However, it
could be fixed easily. Each disconnection is counted as
one error.
The maximum possible number of errors for a gearbox is the
number of its total pieces (gearbox 1: 8, gearbox 2: 7, and gearbox
3: 8). Figure 9 shows the numbers of assembly errors for each
training condition in the both test sessions according to the
qualification.
5.4.2 Comparison Within Each Session
Non-parametric analysis was performed for numbers of errors.
Within each session, a Friedman test showed there was no overall
statistically significant difference in numbers of errors between
the training conditions.
In the immediate test, Mobile VR has the smallest median of
0.5 (IQR 0–1), followed by Video with a median of 1 (IQR 
0–1) and PC VR with 1 (IQR 0–2). In the retention test, Video
and Mobile VR have the same median value of 1 (IQR 0–2.5)
and PC VR has the largest median of 1.5 (IQR 1–3).
5.4.3 Comparison Between Two Sessions
Wilcoxon signed-rank tests showed there was a significant
difference in numbers of errors of each condition between
the immediate test and the retention test (Video:W6,
Z−2.31, p0.021, r0.517; Mobile VR:W25.5,
FIGURE 8 | Completion times for each condition in the immediate test
and the retention test. *means significant difference (p<0.05).
FIGURE 9 | Numbers of assembly errors. *means significant difference
(p<0.05).
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 5974879
Shen et al. Effects of Level of Immersion

Z−1.982, p0.049, r0.443; and PC VR: W0, Z −3.263,
p<0.001, r 0.73).
The numbers of errors in all experimental conditions
increased significantly in the retention test. The median value
increased by 0.5 in the Mobile VR and PC VR condition, while the
one of the Video condition remained the same.
5.5 Questionnaire Ratings
Figure 10 shows the ratings of instruction clarity, ease of use,
usefulness and preference from the experience questionnaire (see
the rating of gearbox difficulty in Section 5.1). Non-parametric
analysis was performed for ratings.
A Friedman test showed there was an overall statistically
significant difference in rating of preference between the
training conditions (χ2(2)11.03,p0.004). A post hoc test
using Wilcoxon signed-rank tests with Bonferroni correction
showed the significant differences between Video and PC VR
(Z−2.607,p0.01,r0.582)and between Mobile VR and PC
VR (Z−2.682,p0.005,r0.6).
No overall statistically significant difference was found in the
other three ratings between the training conditions.
5.6 Previous VR Experience
Based on their experiences of using VR, the participants were
assigned to two groups: the group having 12 participants with less
experience (0 or less than 5 h) and the group having 8 participants
with more experience (more than 5 h). Within each group, the
same statistical analysis was performed on the data of all
measurements.
5.6.1 Training Times and Iterations (Grouped)
Within each group, a Friedman test showed there was an overall
statistically significant difference in training times between the
conditions (less experience: χ2(2)14,p<0.001; and more
experience: χ2(2)13,p0.002). A post hoc test using
Wilcoxon signed-rank tests with Bonferroni correction showed
the significant differences between Video and Mobile VR
(less experience: Z−3.059,p<0.001,r0.883; and more
experience: Z−2.521,p0.008,r0.891) and between Video
and PC VR (less experience: Z −2.667, p 0.005, r 0.770; and
more experience: Z−2.521,p0.008,r0.891).
Friedman tests showed there was no overall statistically
significant difference in training iterations between the
conditions for both groups.
5.6.2 Completion Times (Grouped)
Friedman tests showed there was no overall statistically significant
difference in completion times between the conditions in the
immediate test or the retention test for both groups.
Wilcoxon signed-rank tests showed a significant difference in
completion times of PC VR between the immediate test and the
retention test among the participants with less VR experience
(W8,Z−2.432,p0.012,r0.702, the median value
increased by 28.07%) and a significant difference in completion
times of Video between the two sessions among the participants
with more VR experience (W 0, Z −2.521, p 0.008, r 0.891,
the median value increased by 42.96%). There was no significant
difference in completion times of the other conditions in the
groups between the sessions.
5.6.3 Assembly Errors (Grouped)
Friedman tests showed there was no overall statistically
significant difference in numbers of errors between the
training conditions in the immediate test or the retention test
for both groups.
Wilcoxon signed-rank tests showed there was a significant
difference in numbers of errors of the PC VR condition between
the immediate test and the retention test within the group having
more VR experience (W0,Z−2.369,p0.031,r0.838,
the median value increased from 0 to 1). There was no
significant difference in numbers of errors of the other
conditions in the groups between the two sessions.
5.6.4 Questionnaire Ratings (Grouped)
Within the group having less VR experience, a Friedman test
showed there was an overall statistically significant difference in
rating of preference between the training conditions (χ
2
(2) 8.048,
p0.018). A post hoc test showed the significant difference
between Mobile VR and PC VR (Z −1.970, p 0.039, r 0.569).
Within the group having more VR experience, a Friedman test
showed there was an overall statistically significant difference in
rating of usefulness between the training conditions (χ
2
(2) 7.583,
p0.023). A post hoc test showed the significant difference
between Video and PC VR (Z−2.369,p0.031,r0.838).
No overall statistically significant difference was found in the
other ratings between the conditions for both groups.
6 DISCUSSION
In summary, participants preferred virtual training with a
higher level of immersion and believed they have learned
more in those conditions (H1 is true). In contrast, and
FIGURE 10 | Ratings of training experiences. *means significant
difference (p<0.05).
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 59748710
Shen et al. Effects of Level of Immersion

surprisingly, traditional virtual training with passive video
playback had the best performance in terms of learning time
and task completion time immediate after training (H2 is false).
However, video training was also the only virtual training
condition that suffered a significant completion time increase
in the retention session. We discuss these findings separately in
the following subsections.
6.1 User Preference and Perception
Most participants appreciated the immersive experience provided
by both HTC VIVE Pro and Google Cardboard. They agreed that
training in an immersive environment was enjoyable during the
interview. They also commented that the immersive training was
memorable and easier to recall during the immediate test. Two
participants even commented that they intentionally stayed
longer in the virtual environment for the VR experience
despite the fact that they had already learned the assembly
procedure.
However, our hypothesis H1, that users would prefer
training with a higher level of immersion, was only partially
correct. Indeed, most participants (16 out of 20) favor virtual
training with HTC VIVE Pro among all conditions. However,
eight participants preferred virtual training with video to
Google Cardboard. Among these eight participants, two
commented that the gearbox assembly was simple [“Iknow
gearboxes.Soit(was)theknowledge(that)helpedme.(I)can
do this very well. I think it’sveryeasy.”]andwatchingvideo
playbacks was sufficient [“When I was studying, what I learnt
was to represent 3D objects in 2D. So it feels that (video
training) was closer to my learning pattern and my way of
understanding 3D. (What I liked the most was) video, for this
reason.”] (they also rated virtual training with video higher than
HTC VIVE Pro); while others commented that interacting with
virtual objects using head movements in Google cardboard was
“unnatural”and “uncomfortable”. It seems that the
inconvenient interaction method of Google Cardboard
interfered with the benefits afforded by the virtual immersive
training environment. Besides, 5 of the 20 participants had
experiences studying mechanical engineering. 4 of them
preferred Video to Mobile VR and the other one rated Video
as high as Mobile VR in terms of preference. Only one of them
preferred PC VR to Video. On the other hand, most of the other
participants preferred PC VR (12 out of 15) or Mobile VR (7 out
of 15) to Video. It seems the participants with relevant
education background tended to favor Video due to their
domain knowledge and previous learning experiences while
the others did not. Similarly, previous VR experiences
might also affect user’s perception of virtual environments.
In terms of rating, only the participants with more VR
experience believed PC VR was significantly more helpful for
training than Video.
6.2 How Immersive is Enough?
According to our data, H2, which hypothesizes that virtual
training in VR would yield better performance (completion
time), appears to be false. The Video condition had a
significantly better performance than Mobile VR.H3 is not
correct. The participants indeed preferred the training with
bimanual 3D inputs, yet performance wise, there was no
significant difference between PC VR condition with 3D inputs
and Video condition with only passive video watching.
This resonates with the landmark paper from Bowman and
McMahan (2007) which discussed several cases where a higher
level of immersion did not result in better task performance. In
particular, when tasks are simple or the visualization are less
complex (Laha et al., 2012;Schuchardt and Bowman, 2007), less
immersive systems might perform as well as the more immersive
ones when considering the task of spatial understanding. Our
result seems to exhibit the same phenomena for training of a
different type of task. In addition, our study also fits the criterion
well as 3D assembly demands spatial understanding and
participants considered the assembly task relatively simple
after the virtual training (see Figure 6). These seem to lead
to a similar conclusion of previous works (Bowman and
McMahan, 2007;Schuchardt and Bowman, 2007;
Sowndararajan et al., 2008;Laha et al., 2012) in the context
of learning transfer, i.e., for assembly tasks that are simple, less
immersive virtual training systems might achieve similar level of
learning transfer as the more immersive ones. This might help
explain why Video led to better performance than Mobile VR in
our experiment. The gearboxes were relatively simple, and the
assembly steps were clear enough from the videos, especially for
those with a mechanical engineering background. Most
participants were probably more used to learning from
videos, considering more than half of them had little
experience using VR. The video training was sufficient and
did not have the issue of inconvenient interaction methods as
Google Cardboard, which eventually produced better results.
Previous works (Carlson et al., 2015;Murcia-Lopez and Steed,
2018) did not explicitly investigate virtual training with different
levels of immersion. Instead, these works focused on the
comparison between the effectiveness of virtual and physical
training. In terms of training media, there are some
similarities between our conditions (Video and PC VR) and
those (PV
I
using videos without assembly practice and V
E
A
using animations with virtual blocks for practice in PC-
powered VR) in the work from Murcia-Lopez and Steed
(2018), except the inclusion of paper instructions and the use
of abstract 3D puzzles. Similarly, their work found no significant
effect on the success rates and testing times among all puzzles
except the most difficult one, where most participants who
experienced only video-training failed to solve the puzzle in
the given time. This result seems to imply the advantage of
VR training over video one for the task with extremely high
complexity e.g., 6-pieces 3D burr Puzzle. We believe it will be
beneficial to examine if such finding still stands in other real-
world training targets more complex than the gearboxes used in
our experiment.
6.3 Immersive Virtual Training Might Help
Recall
The Video condition is the only one having a significant
difference in completion times between the immediate test
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 59748711
Shen et al. Effects of Level of Immersion

and the retention test. The descriptive statistics of task
completion time in both tests are median 76.04 and median 
103.05 respectively, an increase of 35.53% in the average
completion time, while the median values in VR conditions
remain almost the same (Mobile VR: from 94.69 to 94.94, and PC
VR: from 88.9 to 88.04). This result shows that an immersive
virtual training might be beneficial to the training retention.
However, the participants’previous experiences of using VR
might also affect the results. While the participants with more
VR experience still had a significant increase of completion
times in the Video condition between the two sessions (averagely
42.96%), the participants with less VR experience had a
significant increase of completion times in the PC VR
condition between the sessions (averagely 28.07%). It seems
to suggest that lack of familiarity with training media could
diminish the benefits of immersion for retention.
Compared to two previous works (Carlson et al., 2015;
Murcia-Lopez and Steed, 2018) using a similar protocol, this
result echos the finding of Carlson et al. (2015) where the
performance of the VR-trained participants improved in a
retention session 2 weeks after the training. The result from
Murcia-Lopez and Steed (2018) is less conclusive because most
participants failed to solve the puzzles in the retention session.
Indeed, potential confounding factors, such as task complexity,
learning times, and the amounts of time spent with the physical
gearboxes during the immediate test, might have affected the
participant’s performance in the retention sessions. Further
investigation is in dire need to understand how immersive
virtual environments can be used as a tool to consolidate
memory (Krokos et al., 2019).
6.4 Are Gearbox Assembly Tasks Difficult
Enough?
Three functional gearboxes were used in our study to better
represent a real-world assembly line task. Compared with the six-
piece burr puzzle used in previous works (Carlson et al., 2015;
Murcia-Lopez and Steed, 2018), these gearboxes were relatively
simple and attracted the question of whether the training was
effective or even necessary.
To answer this question, we recruited three untrained subjects for
an informal study. For each gearbox, the participant was given two
photos of the assembled physical gearbox from different angles. The
participant could view the photos as long as they needed to
memorize the internal structure and to guess how to assemble.
When he/she was ready for the test, the photos were taken away and
the 3D-printed pieces were given to start the assembly task.
The result (of 9 trials in total) showed that when compared
with the trained subjects, these three untrained subjects took
much longer to complete the assembly tasks (see Table 4) and
committed more serious errors (see Figure 11). It indicated that
the gearboxes were complex enough and the virtual training
indeed increased the assembly performance of the participants.
6.5 Choice of Virtual Training
Despite the fact that our participants considered both VR conditions
to be more engaging, the results of our study are in line with previous
findings that a higher level of immersion does not always yield better
performance (Adams et al., 2001;Hall and Horwitz, 2001;
Sowndararajan et al., 2008;Gavish et al., 2015). The limited
interaction capability provided by Mobile VR could undermine
the benefitsoftheimmersiveexperienceandresultinapoor
training effectiveness. Whereas the traditional video-based training
seemstobeaseffectiveasVRtrainingfortheassemblytaskswith
low-to-median perceived difficulty, at least in the short-term.
To summarize, our study suggested that VR could provide
more engaging learning experiences and prolonged training
outcomes. While the traditional video training has lower costs
and could be more time efficient during the training process
Figure 7. The choice of training media depends on the difficulty
of the task, the constraint on the budget, and the frequency of
training. A hybrid training procedure that mixes the video-based
and VR-based training could be worth investigating.
7 LIMITATION
The within-subject design allows better control of each participant’s
background knowledge. However, the repeated exposures to gearbox
assembly tasks inevitably incur learning effects. We believe the use of
three different gearboxes consisting of different mechanisms should
have mitigated the learning effect. Our result also concurs with
previous works using between-subject designs (Carlson et al., 2015;
Murcia-Lopez and Steed, 2018). Still, it is worth noting the limitation
that it is difficult to disentangle the transfer of knowledge across
conditions in the retention session.
VR conditions in our study lack physics constraints, just like
the other assembly task studies (Carlson et al., 2015;Murcia-
Lopez and Steed, 2018). The subjects might not get the correct
perception of the motor demands for the physical assembly tasks
in the testing phase.
The subjects took the immediate test right after the training
phase. There was no distractor task (Carlson et al., 2015) or short
break (Murcia-Lopez and Steed, 2018) between training and
testing. The training performance might be positively affected
by the recency effect.
The two tests only required the participants to complete the
tasks once. From our observation, the subjects were sometimes
stuck at some step due to motor demands even though they were
aware of how to assemble it. The completion times might be
influenced by these random events during the testing. To reduce
the effect, subjects could be required to complete the tasks for
multiple times and duplicate measurements need to be averaged.
However, participants might prefer VR conditions (H1) and
consider it more helpful due to the novelty effect (Radu and
Schneider, 2019). Thus, there is a definite need for a longitudinal
study to better understand the effectiveness of VR or AR training.
TABLE 4 | The completion times of the untrained and trained subjects in the
immediate test.
Conditions Untrained Video Mobile VR PC VR
Median 233.81 76.04 94.69 88.9
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 59748712
Shen et al. Effects of Level of Immersion

8 CONCLUSION AND FUTURE WORK
We have presented a study that compares the effectiveness of
three different virtual training methods for bimanual gearbox
assembly tasks. Each virtual training method represented a
different level of immersion. The results showed that
participants favored virtual training with a higher level of
immersion and presumed that the immersive virtual
experience helps the recall of assembly procedure. The
performance of the Video condition was surprisingly good.
The completion times of its participants in the immediate test
were significantly better than the Mobile VR condition and had no
significant difference against the PC VR condition, except that its
performance during the retention test exhibited a significant drop
and is worth further investigation. We believe that these results
are important as it provides insights into how levels of immersion
might affect training transfer of bimanual assembly tasks.
The directions of future studies could be evaluating how
various factors affect virtual training with multiple levels of
immersion, including physics constraints, domain knowledge,
VR experiences and task complexity. Another potential future
research could be exploring the design of a hybrid virtual
environment that integrates multiple training media to utilize
the benefits of different levels of immersion. Since tasks or
trainees might require or prefer various immersion levels,
another interesting direction of future work could be creating
an authoring tool to effectively and easily generate training
contents with multiple formats that could be used in different
virtual environments.
DATA AVAILABILITY STATEMENT
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and
accession number(s) can be found in the article/
Supplementary Material.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by University of Technology Sydney Human Research
Ethics Committee. Thepatients/participants provided their written
informed consent to participate in this study. Written informed
consent was obtained from the individuals for the publication of
any potentially identifiable images or data included in this article.
AUTHOR CONTRIBUTIONS
All authors contributed to this paper. SS, H-TC, and TL designed
the study. SS performed the experiment and analyzed the data. SS
and H-TC wrote the draft of the manuscript. WR and TL
reviewed and edited the manuscript.
FUNDING
SS was a PhD candidate at University of Technology Sydney.
This research was supported by an Australian Government
Research Training Program Scholarship and UTS Doctoral
Scholarships.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/frvir.2021.597487/
full#supplementary-material
FIGURE 11 | The gearbox assembled by an untrained subject (right) and the correct example (left).
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 59748713
Shen et al. Effects of Level of Immersion

REFERENCES
Adams, R. J., Klowden, D., and Hannaford, B. (2001). Virtual Training for a
Manual Assembly Task. Haptics-e: Electron. J. Haptics Res. 2, 1–7.
Bailenson, J., Patel, K., Nielsen, A., Bajscy, R., Jung, S.-H., and Kurillo, G. (2008).
The Effect of Interactivity on Learning Physical Actions in Virtual Reality.
Media Psychol. 11, 354–376. doi:10.1080/15213260802285214
Berg, L. P., and Vance, J. M. (2017). Industry Use of Virtual Reality in Product
Design and Manufacturing: a Survey. Virtual Reality 21, 1–17. doi:10.1007/
s10055-016-0293-9
Bhagat, K. K., Liou, W.-K., and Chang, C.-Y. (2016). A Cost-Effective Interactive
3d Virtual Reality System Applied to Military Live Firing Training. Virtual
Reality 20, 127–140. doi:10.1007/s10055-016-0284-x
Boud, A. C., Haniff, D. J., Baber, C., and Steiner, S. J. (1999). “Virtual Reality and
Augmented Reality as a Training Tool for Assembly Tasks,”in 1999 IEEE
International Conference on Information Visualization (Cat. No. PR00210),
32–36. doi:10.1109/IV.1999.781532
Bowman, D. A., and McMahan, R. P. (2007). Virtual Reality: How Much
Immersion Is Enough?. Computer 40, 36–43. doi:10.1109/MC.2007.257
Carlson, P., Peters, A., Gilbert, S. B., Vance, J. M., and Luse, A. (2015). Virtual
Training: Learning Transfer of Assembly Tasks. IEEE Trans. Vis. Comput.
Graphics 21, 770–782. doi:10.1109/TVCG.2015.2393871
Dalgarno, B., and Lee, M. J. W. (2010). What Are the Learning Affordances of 3-D
Virtual Environments?. Br. J. Educ. Tech. 41, 10–32. doi:10.1111/j.1467-8535.
2009.01038.x
de Moura, D. Y., and Sadagic, A. (2019). “The Effects of Stereopsis and Immersion
on Bimanual Assembly Tasks in a Virtual Reality System,”in 2019 IEEE
Conference on Virtual Reality and 3D User Interfaces (VR) (IEEE), 286–294.
doi:10.1109/VR.2019.8798112
Funk, M., Bächler, A., Bächler, L., Kosch, T., Heidenreich, T., and Schmidt, A. (2017).
“Working with Augmented Reality?,”in Proceedings of the 10th International
Conference on PErvasive Technologies Related to Assistive Environments (New
York, NY, USA: ACM, PETRA), 17, 222–229. doi:10.1145/3056540.3056548
Gavish, N., Gutiérrez, T., Webel, S., Rodríguez, J., Peveri, M., Bockholt, U., et al.
(2015). Evaluating Virtual Reality and Augmented Reality Training for
Industrial Maintenance and Assembly Tasks. Interactive Learn.
Environments 23, 778–798. doi:10.1080/10494820.2013.815221
Gerbaud, S., Mollet, N., Ganier, F., Arnaldi, B., and Tisseau, J. (2008). “GVT: a
Platform to Create Virtual Environments for Procedural Training,”in 2008 IEEE
Virtual Reality Conference (IEEE), 225–232. doi:10.1109/VR.2008.4480778
Gonzalez-Franco, M., Pizarro, R., Cermeron, J., Li, K., Thorn, J., Hutabarat, W.,
et al. (2017). Immersive Mixed Reality for Manufacturing Training. Front.
Robot. AI 4, 1–8. doi:10.3389/frobt.2017.00003
Gorecky, D., Khamis, M., and Mura, K. (2017). Introduction and Establishment of
Virtual Training in the Factory of the Future. Int. J. Comput. Integrated
Manufacturing 30, 1–9. doi:10.1080/0951192X.2015.1067918
Hall, C. R., and Horwitz, C. D. (2001). Virtual Reality for Training: Evaluating
Retention of Procedural Knowledge. Ijvr 5, 61–70. doi:10.20870/IJVR.2001.5.1.
2669
Hoedt, S., Claeys, A., Van Landeghem, H., and Cottyn, J. (2017). The Evaluation of
an Elementary Virtual Training System for Manual Assembly. Int. J. Prod. Res.
55, 7496–7508. doi:10.1080/00207543.2017.1374572
Jiang, M. (2011). “Virtual Reality Boosting Automotive Development,”in Virtual
Reality & Augmented Reality in Industry. Editors D. Ma, X. Fan, J. Gausemeier,
and M. Grafe (Berlin, Heidelberg: Springer Berlin Heidelberg)), 171–180.
doi:10.1007/978-3-642-17376-9_11
Krokos, E., Plaisant, C., and Varshney, A. (2019). Virtual Memory Palaces:
Immersion Aids Recall. Virtual Reality 23, 1–15. doi:10.1007/s10055-018-
0346-3
Laha, B., Sensharma, K., Schiffbauer, J. D., and Bowman, D. A. (2012). Effects of
Immersion on Visual Analysis of Volume Data. IEEE Trans. Vis. Comput.
Graphics 18, 597–606. doi:10.1109/TVCG.2012.42
Leu, M. C., ElMaraghy, H. A., Nee, A. Y. C., Ong, S. K., Lanzetta, M., Putz, M., et al.
(2013). CAD Model Based Virtual Assembly Simulation, Planning and
Training. CIRP Ann. 62, 799–822. doi:10.1016/j.cirp.2013.05.005
Mollet, N., and Arnaldi, B. (2006). “Storytelling in Virtual Reality for Training,”in
Technologies for E-Learning and Digital Entertainment. Editors Z. Pan,
R. Aylett, H. Diener, X. Jin, S. Göbel, and L. Li (Berlin, Heidelberg: Springer
Berlin Heidelberg), 334–347. doi:10.1007/11736639_45
Mollet, N., Gerbaud, S., and Arnaldi, B. (2007). “STORM: a Generic Interaction
and Behavioral Model for 3D Objects and Humanoids in a Virtual
Environment,”in Eurographics Symposium on Virtual Environments, Short
Papers and Posters.The Eurographics Association. Editors B. Froehlich,
R. Blach, and R. van Liere doi:10.2312/PE/VE2007Short/095-100
Murcia-Lopez, M., and Steed, A. (2018). A Comparison of Virtual and Physical
Training Transfer of Bimanual Assembly Tasks. IEEE Trans. Vis. Comput.
Graphics 24, 1574–1583. doi:10.1109/TVCG.2018.2793638
Oren, M., Carlson, P., Gilbert, S., and Vance, J. M. (2012). “Puzzle Assembly
Training: Real World vs. Virtual Environment,”in 2012 IEEE Virtual Reality
(VR) ((IEEE)), 27–30. doi:10.1109/VR.2012.6180873
Papachristos, N. M., Vrellis, I., and Mikropoulos, T. A. (2017). “A Comparison between
Oculus Rift and a Low-Cost Smartphone VR Headset: Immersive User Experience
and Learning,”in 2017 IEEE 17th International Conference on Advanced Learning
Technologies (ICALT) (IEEE), 477–481. doi:10.1109/ICALT.2017.145
Radu, I., and Schneider, B. (2019). “What Can We Learn from Augmented Reality
(Ar)?,”in Proceedings of the 2019 CHI Conference on Human Factors in
Computing Systems (New York, NY, USA: ACM, CHI ’19), 544, 1–544.
doi:10.1145/3290605.3300774.12
Ritter, F., Strothotte, T., Deussen, O., and Preim, B. (2001). Virtual 3D Puzzles: a
New Method for Exploring Geometric Models in VR. IEEE Comput. Graphics
Appl. 21, 11–13. doi:10.1109/38.946625
Rodríguez, J., Gutiérrez, T., Sánchez, E. J., Casado, S., and Aguinaga, I. (2012).
“Training of Procedural Tasks through the Use of Virtual Reality and Direct
Aids,”in Virtual Reality and Environments. Editor C. S. Lanyi (Rijeka:
IntechOpen)). chap. 3. doi:10.5772/36650
Ruthenbeck, G. S., and Reynolds, K. J. (2015). Virtual Reality for Medical Training:
the State-Of-The-Art. J. Simulation 9, 16–26. doi:10.1057/jos.2014.14
Schuchardt, P., and Bowman, D. A. (2007). “The Benefits of Immersion for Spatial
Understanding of Complex Underground Cave Systems,”in Proceedings of the
2007 ACM Symposium on Virtual Reality Software and Technology - VRST ’07
(New York, NewYork, USA: ACM Press)), 1, 121. doi:10.1145/1315184.1315205
Shuralyov, D., and Stuerzlinger, W. (2011). “A 3D Desktop Puzzle Assembly
System,”in 2011 IEEE Symposium on 3D User Interfaces (3DUI) (IEEE),
139–140. doi:10.1109/3DUI.2011.5759244
Sowndararajan, A., Wang, R., and Bowman, D. A. (2008). “Quantifying the Benefits
of Immersion for Procedural Training,”in Proceedings of the 2008 Workshop on
Immersive Projection technologies/Emerging Display Technologiges - IPT/EDT
’08 (New York, New York, USA: ACM Press), 1. doi:10.1145/1394669.1394672
Stanney, K. M., Mourant, R. R., and Kennedy, R. S. (1998). Human Factors Issues in
Virtual Environments: A Review of the Literature. Presence 7, 327–351. doi:10.
1162/105474698565767
Wang, X., Ong, S. K., and Nee, A. Y. C. (2016). A Comprehensive Survey of
Augmented Reality Assembly Research. Adv. Manuf. 4, 1–22. doi:10.1007/
s40436-015-0131-4
Young, M. K., Gaylor, G. B., Andrus, S. M., and Bodenheimer, B. (2014). “A
Comparison of Two Cost-Differentiated Virtual Reality Systems for Perception
and Action Tasks,”in Proceedings of the ACM Symposium on Applied
Perception - SAP ’14 (New York, New York, USA: ACM Press), 83–90.
doi:10.1145/2628257.2628261
Yuviler-Gavish, N., Yechiam, E., and Kallai, A. (2011). Learning in Multimodal
Training: Visual Guidance Can Be Both Appealing and Disadvantageous in
Spatial Tasks. Int. J. Human-Computer Stud. 69, 113–122. doi:10.1016/j.ijhcs.
2010.11.005
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Shen, Chen, Raffe and Leong. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic practice.
No use, distribution or reproduction is permitted which does not comply with
these terms.
Frontiers in Virtual Reality | www.frontiersin.org May 2021 | Volume 2 | Article 59748714
Shen et al. Effects of Level of Immersion