# A Large-Scale Feasibility and Ethnography Study of Screen-based AR and 3D
Visualization Tools for Anatomy Education: Exploring Gender Perspectives in
Learning Experience

Roghayeh Leila Barmaki1§, Kangsoo Kim2, Zhang Guo1, Qile Wang1,
Kevin Yu3, Rebecca Pearlman4, and Nassir Navab3,4 § The study was conducted
during the postdoctoral fellowship of Roghayeh Leila Barmaki at Johns Hopkins
University. Correspondence to<EMAIL_ADDRESS>1 University of Delaware, Newark,
DE, USA  2 University of Calgary, Calgary, AB, Canada  3 Technical University
of Munich, Munich, Germany  4Johns Hopkins University, Baltimore, MD, USA

###### Abstract

Anatomy education is an indispensable part of medical training, but
traditional methods face challenges like limited resources for dissection in
large classes and difficulties understanding 2D anatomy in textbooks. Advanced
technologies, such as 3D visualization and augmented reality (AR), are
transforming anatomy learning. This paper presents two in-house solutions that
use handheld tablets or screen-based AR to visualize 3D anatomy models with
informative labels and in-situ visualizations of the muscle anatomy. To assess
these tools, a user study of muscle anatomy education involved 236 premedical
students in dyadic teams, with results showing that the tablet-based 3D
visualization and screen-based AR tools led to significantly higher learning
experience scores than traditional textbook. While knowledge retention didn’t
differ significantly, ethnographic and gender analysis showed that male
students generally reported more positive learning experiences than female
students. This study discusses the implications for anatomy and medical
education, highlighting the potential of these innovative learning tools
considering gender and team dynamics in body painting anatomy learning
interventions.

###### Index Terms:

Screen-based Augmented Reality, Collaborative Learning, Evaluation
Methodologies, Human-Computer Interface, Gender and Ethnography.

##  I Introduction

Human anatomy and physiology are vital parts of medical education that involve
complex functional structures and movements of the human body. Comprehensive
learning of anatomy and physiology provides a thorough understanding of human
body function, enabling more effective treatment of abnormal or disease states
[1]. However, the complexity of the course poses challenges for students in
achieving their desired learning outcomes. Several factors associated with the
learning experience can influence these outcomes, including the learning
tools, the quality of the material, the student’s prior experience, and their
emotional concerns [2, 3, 4].

The most common practice for students in anatomy education is to use textbooks
with static images, but this cannot provide the students with a realistic
first-hand and interactive experience, which may not be effective for their
learning experience and performance [5]. Despite the effectiveness of
traditional methods for training anatomy, such as dissection or prosection,
these methods have become less feasible nowadays due to limited resources,
large class sizes, and mainly the absence of face-to-face learning
experiences, to name a few constraints. Due to such limitations, most medical,
dental, and other allied health schools have recently declined the practical
laboratory hours for anatomy[6, 7]. In anatomy and physiology education,
spatial visualization is likely essential for students to learn the dynamics
of anatomical structures and spatial relationships to surrounding structures.
The traditional visualization of human anatomy in 2D textbook views
insufficiently captures the complexity of human anatomy as students are often
required to mentally reconstruct 3D spatial relationships, which presents a
considerable challenge.

Virtual/augmented reality (VR/AR) can provide information on dynamics and
spatial relationships interactively and intuitively by employing 3D virtual
skeletons and organs and adding a virtual information layer on top of the
physical body. While various medical training scenarios have used these
technologies [8, 9, 10], the use of computer-generated 3D models, in
particular, allows students to rotate and locate structures from various views
and perspectives in anatomy learning. Such dynamic visualization techniques
improve student visual-spatial abilities[11, 12, 13]. Moreover, virtual 3D
visualizations offer more accessible opportunities to engage and explore
anatomical structures than traditional cadaver-based learning for their
repeatability and monitoring capabilities.

Body painting has been shown as an effective tool for learning anatomy and
associated clinical skills [14, 15]. It is a motivating and creative
experience for students that provides memorable visual images and encourages
multisensory and active participation. While body painting suits all students,
cultural sensitivity, gendered considerations, and careful negotiation may be
necessary to ensure all students are comfortable carrying out the activities.

Inspired by renown methods of anatomy education, in this work, we propose a
user study in an actual educational laboratory setting that uses 3D
visualizations in an anatomy body painting learning task. In a controlled,
team-based large-scale study with $236$ participants, we compare our in-house
tablet-based 3D visualizations (_Tablet-3D_), and our screen-based AR
(_Screen-AR_) [16] with the conventional paper-based _Textbook_. We aimed to
find any interplay between the participants’ performance outcomes with
learning tools, gender, and group gender compositions. Our research aims to
address the following research questions:

  * •

RQ1: Do tablet-based visualizations and AR technology improve students’
learning experience compared to the traditional textbook in anatomy education?

  * •

RQ2: Do tablet-based visualizations and AR technology increase the learning
outcomes, e.g., test scores or knowledge retention, in anatomy education?

  * •

RQ3: Are there any particular benefits of AR technology in anatomy education
experience over tablet-based visualizations?

  * •

RQ4: Is the student’s gender a factor to interplay with the effects of digital
technology, or in general for the learning experience?

To answer these research questions, we first introduce our technological
tools, tablet-based interactive visualization application and large screen-
based AR tool, which can show dynamic anatomical information with interactive
life-size 3D virtual models on top of a physical body. We then report the
findings from our large-scale study with $236$ students in teams of two who
were participated in the body painting activity using three learning tools.

We found that students who used our Tablet-3D and Screen-AR conditions had
more positive (anatomy) learning experiences than those who used a textbook,
according to their self-reported outcomes. In addition, we analyzed the
potential effects of the participant’s gender on the performance, which will
be elaborated more in the following.

##  II Related Work

###  II-A 3D Technologies for Anatomy Education

Anatomy is a complex subject that cannot be learned only from textbooks[17].
Traditional anatomy training is based on the dissection and pro-section of the
human body, which provides tangible haptic interactions and realistic
environment settings[18, 19]. Despite the advantages of dissection, it equally
raises concerns. Studies have shown that the learning outcomes and the quality
of dissections may be affected by the quality of the material, students’ prior
experience, and emotional concerns[20, 7]. In particular, both inexperienced
and experienced medical and healthcare students are frequently appalled by the
fear of death and the unnatural smell of cadavers during the dissection[21,
7]. Instead of using deceased bodies, clay models provide an alternative
solution for educators. DeHoff et al.[22] found that compared with animal
dissections, students had a better learning experience with clay models.
However, clay models cannot present complex anatomical regions or the
functional movements of the structures in the anatomical domain. Additionally,
they impose challenges in transportation and storage. Moreover, anatomy course
lab hours have gradually decreased in the past decades [6, 7], bringing more
challenges to anatomy education.

As a response to the barriers and changes over time, anatomical learning
platforms increasingly adapt from traditional methods to digital technology.
Like any other reshaping processes, some anatomy scholars argue that dynamic
visualization compensates for students’ low spatial abilities by providing an
explicit external representation of the system[23, 11, 13, 24]. Increasingly
powerful and accessible computer hardware allow 3D visualizations to replace
or supplement traditional teaching in healthcare regarding lectures, cadavers,
and textbooks[25, 26, 27, 28]. Donnelly et al.[29] investigated the use of
Virtual Human Dissector© (VHD) software, interactive teaching tools for cross-
sectional anatomy, capable of reconstructing 3D views from 2D images, in
anatomy education with self-directed learning and found no significant
difference when compared with a students group that learns from using
prosection, models, and textbooks. Kennan & Awadh [30] discussed the effective
utilization of visual 3D learning technologies as self-learning resources in
the context of cross-sectional anatomy. They proposed integrating the use of
the 3D VHD system with Sectra[31], a medical imaging device, to enhance the
understanding of cross-sectional anatomy.

Lim et al.[32] used 3D-printed models instead of traditional cadaveric
specimens during the learning of external cardiac anatomy. Although 3D
printing technology can provide teaching materials, the 3D printed model is
limited by the complexity of anatomical regions. Equally, researchers found
that 3D visualization methods improved student performance by providing
multiple anatomical views and different perspectives of 3D rotating models,
even on 2D screens [25]. Mobile-based applications and web-based 3D games have
also been used as efficient learning tools for the study of human skeletal,
muscular, and cardiovascular systems to explore more spatial information about
the 3D anatomical models [26, 27].

VR and AR techniques have been adopted into anatomy education in recent
years[28, 33, 34, 35]. As dynamic visualizing tools, they engage students in
an immersive environment with audio and visual interactions and stereoscopic
3D models to have a better functional understanding of the anatomical
structure and its movement within the 3D body space[36, 37, 38, 39].
Duncan‐Vaidya and Stevenson[40] have found that experience from AR positively
influences the learning process of skull anatomy on a similar level as
traditional tools such as textbooks or plastic skull models. Increasing the
frequency of learning instances and interactions with models and specimens are
advantages of teaching anatomy in AR[10]. Kolla et al.[41] examined the
effectiveness of VR technology in anatomy education and compared it to
traditional teaching methods like lectures and cadaveric dissection. 28 first-
year medical students used a VR headset to identify anatomical structures,
drew them on a virtual skeleton, and then provided feedback through surveys.
Their results indicated that VR was highly supported by the students,
demonstrating its potential as a valuable tool for learning human anatomy and
as a useful complement. However, all study conditions were conducted within VR
settings. In a different study, the AR controlled group that was randomly
selected from a biochemistry course suggests that AR educational apps
motivated them to understand the visualized processes[42]. Despite the
popularity of using VR/AR methods in anatomy education, a review paper [43] of
152 articles did not identify conclusive evidence of their efficiency over
traditional anatomy education methods.

###  II-B Measures of Anatomy Learning Experience

To analyze the learning experience and evaluate the effectiveness of VR/AR
applications, various methods for data collection and different measures were
used in previous research. Kurniawan and Witjaksono [44] evaluated the
usefulness of the mobile-based AR application by employing the attitude
questionnaire to analyze user perception. Nainggolan et al. [17] evaluated the
interactivity level of VR controller based on the user’s agreement and
satisfaction levels, and found that the use of the VR controller in the
anatomy learning system was very interactive and satisfactory. Tanjung et al.
[45] conducted a comparison learning experience study to evaluate the level of
acceptability and satisfaction towards three anatomical learning systems. In
our previous AR anatomy learning research [16, 33, 46, 47] and another study
by Duncan‐Vaidya and Stevenson [48] pre- and post-knowledge quizzes, and a
usability questionnaires were used for data collection and analysis of the
effectiveness of the AR tool.

###  II-C Gender Effects in Education

In different education domains, gender differences have been discussed in
recent studies. Understanding gender effects in education is not conclusive,
and it varies based on different disciplines and tasks [49, 50]. In the
science, technology, engineering, and mathematics (STEM) domain, some non-
positive learning experiences for females have been reported due to gender
biases at the technical level [51, 52]. In the business domain, females’
higher management ability in group tasks was highlighted [53, 54, 55].
Previous research has also shown that during cognitive tests, females have
better information-processing skills than males [56, 57]. In Andersson’s study
[58] on explicit spatial and verbal collaborative memory performance, the
author reported better retention performance for females, but he argued that
there was no main gender effect on team performance. Prinsen et al.[59] noted
that, in computer-mediated communication [60] and computer-supported
collaborative learning settings [61, 12, 46], learning performance for
different genders might change by various role distributions. In this paper,
we will discuss the ethnographic observations related to gender differences
(from individual and group composition standpoints. We need to acknowledge
that we recruited students from a large laboratory classroom with pre-assigned
teams thus, it was not feasible to form ethnographically balanced teams for
all study conditions.

##  III Methods and Materials

In this section, we will describe the details of the conducted study with our
proposed tablet-based 3D visualization and screen-based AR learning tools.
Relevant hypotheses were established and evaluated to address our general
research questions introduced in Section I. This study was approved by the
Institutional Review Board (Protocol #HIRB00005021).

###  III-A Digital Anatomy Learning Tools

To investigate the effects of tablet- and AR-based learning tools in anatomy
education, we prepared in-house tablet-based 3D visualization and screen-based
AR for our user study. The learning tools are presented in Figure 1. For
Tablet-3D, we developed an interactive Android application for hand-held
devices, which visualizes 3D virtual models of body muscles and labels on a
tablet (see Figure 1(b)). The visualization of the 3D anatomy included
regional and full-body anatomy models. Participants could rotate, adjust zoom
levels, toggle between color-coded virtual labels for enhancing readability,
and highlight relevant muscle groups for our user study (see Figure 1(c)). The
developed application was installed on Samsung Galaxy Tab 2 with a 10.1-inch
screen.

For Screen-AR, we deployed our screen-based AR tool onto a large TV display
with a mounted Kinect v2 camera. Screen-AR used a split-screen to show an AR
view on the left side and a focused view of the anatomical model on the right
side. Furthermore, body tracking allowed Screen-AR to superimpose 3D life-size
virtual models of human anatomical systems, such as the muscular system, and
labels atop the participant’s mirrored body. Participants could navigate
between the relevant muscle groups using a hand-held clicker. Depending on the
muscle group, the virtual camera responsible for rendering the scene on the
right screen automatically follows and zooms into the selected muscles.
Screen-AR consists of an Alienware 17 Laptop with GTX 1070m GPU, a 55-inch
Samsung TV, and a Microsoft Kinect v2 tracking sensor, and all devices were
mounted on a mobile TV cart. We predefined the same muscle groups for
Tablet-3D and Screen-AR for consistency.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 1: Three study conditions with different learning tools: (a) Textbook,
(b) Tablet-3D, and (c) Screen-AR, and the images that captured the actual
anatomy learning interventions with each of those learning tools: muscle
painting activity in (d) Textbook, (e) Tablet-3D, and (f) Screen-AR.

###  III-B Team-based Learning Intervention

After consultation with our laboratory instructor, Dr. Pearlman about the
possibilities of testing our anatomy learning tools with her large classroom,
we were introduced to a mandatory team-based lab activity about human muscle
painting[62]. Muscle painting, as a form of body painting, has been shown to
be one of the common exercises in a medical curriculum[63, 16], and students
execute the muscle painting activity typically with printed 2D visualization
of human musculature from the lab manual. So, we prepared our learning tools
based on these muscle painting activities, aiming to use them as a replacement
for the 2D anatomy textbook and evaluate their learning efficacy and student
performance. During the intervention, participants worked in a team of two
people to collaboratively learn and teach among peers about human muscle
groups through a body painting activity. They were asked to identify and paint
major muscle parts on their body with washable painting supplies while using
one of a randomly assigned (see Section III-C) learning tool. Once one of the
participants completed the painter’s role, they switched their role to be a
paintee with the learning partners. We showed 40 muscles and labels to the
participants to provide extensive anatomical landmarks. However, we only asked
them to identify 12 major muscles (same for all conditions) and use
appropriate body paint to colorize the muscles’ location on their arms and
legs.

###  III-C Study Design

We conducted a user study using a between-subjects design with three learning
tool conditions. A brief description of each learning tool is explained in the
following.

  * •

Textbook: As a traditional learning method, participants used a textbook
during the team-based anatomy learning intervention described in Section
III-A. They identified the corresponding muscle parts on their partner’s body
while checking the location of target muscles from the textbook (Figure 1(a,
d)). The textbook could be carried while performing the muscle painting
activity.

  * •

Tablet-3D: Participants used our tablet-based 3D anatomy application during
the learning intervention. The participants could carry the tablet that
visualizes 3D virtual anatomy models and labels, while performing the painting
activity (Figure 1(b, e)).

  * •

Screen-AR: Participants used our large screen-based AR anatomy tool during the
activity. They could see the virtual 3D models and labels directly overlaid on
their own bodies through the large screen (Figure 1(c, f)).

The study was performed alongside 17 anatomy lab sessions with a maximum
capacity of 20 students in each lab. Considering the large scale of the study,
participants could be randomly assigned to one of the three learning tool
conditions by scheduling a single condition for each lab session. This
assignment method is termed hierarchical or clustered randomization, which is
a common practice in educational studies and clinical trials[64].

###  III-D Participants

We initially recruited 319 student participants (male: 128, female: 191),
although we needed to exclude a subset of the data (described in the
following). The recruitment took place via an online flyer from a laboratory
course of General Biology as part of undergraduate premedical curricula. The
recruitment was part of a general biology lab course, and the students were
randomly assigned by their instructor into teams of two to four to perform
their assignments throughout the semester. The initial anatomy learning
intervention had teams of sizes two to four, however, teams of size two were
only considered for this work. Larger teams had different task distribution in
team, thus excluded. So, our final subset data of interest included 236
participants (95 males and 141 females; age $M\\!=\\!19.77$, $SD\\!=\\!1.81$)
in 118 teams of size two within three age-balanced groups with the following
gender compositions. Male pairs: 62 with age $M\\!=\\!19.45$,
$SD\\!=\\!1.035$, Female pairs: 108 with age $M\\!=\\!19.71$,
$SD\\!=\\!1.583$, and Mixed pairs: 66 with age $M\\!=\\!20.17$,
$SD\\!=\\!2.527$). Table I shows the number of participants per condition.

TABLE I: The number of participants in each study condition. Learning Tool | Male | Female | Total
---|---|---|---
Textbook | 27 | 45 | 72
Tablet-3D | 43 | 45 | 88
Screen-AR | 25 | 51 | 76
Total | 95 | 141 | 236

###  III-E Procedure

The study procedure was as follows. An online flyer was sent to all
undergraduate students in the General Biology lab, inviting them to
participate. Students needed to complete the body painting activity regardless
of our study for their lab credits, but they could opt in to participate in
our study to perform it slightly differently. After the oral consent process,
participants individually completed an online pre-questionnaire, which asked
for their demographic information and evaluated their prior anatomy knowledge.
Then they entered the learning intervention room with their pre-assigned
teammates. Each team only used one of the learning tools described in Section
III-C to complete the learning task, because of between-subjects study
arrangements. During the painting activity, the participant in the painter’s
role tried to find appropriate muscles on the teammate’s body (who played the
role of a paintee) to be painted while navigating different muscle parts.
Participants in the Tablet-3D or textbook conditions used either the
interactive application’s 3D visualizations or the laboratory manual anatomy
figures to complete the activity. Particularly for the AR system, the
participant in the paintee’s role was asked to stand closer to the Kinect body
tracking sensor to have appropriate digital anatomical illustrations
superimposed on their body. The painter used the muscle overlay information on
the TV screen to paint. After completing the painting activity, all
participants completed the online post-questionnaire individually, which asked
about their interaction with the learning tools and evaluated their anatomy
knowledge retention. Before concluding the study session, they presented their
painted limbs to their lab assistants. The activity, including learning
intervention and questionnaire completion, took approximately 20–30 minutes.

###  III-F Measures and Hypotheses

This section describes the measures we used for the study, which were
collected through questionnaires. We also introduce several hypotheses that we
established based on the measures and our research questions in Section I. We
used online questionnaires on the Qualtrics platform (Provoto, UT) for
designing and collecting pre- and post-questionnaires.

####  III-F1 Learning Experience

We prepared nine subjective questions to examine the participants in the
anatomy learning intervention using different learning tools. The questions
were presented on a five-point Likert scale (1: Strongly Disagree to 5:
Strongly Agree) except for the “willingness to recommend” measure, which had a
scale of 0 to 10 (10 means a strong willingness). Our questions for each
measure is described below. Textbook, Tablet-3D, or Screen-AR was replaced by
learning tool statement, depending on the assigned condition.

  * -

Easy to Paint: “Using learning tool was easy and straightforward for
completing the muscle painting activity.”

  * -

Easy to Find Muscles: “I recognized and found the location of muscles easily
using learning tool.”

  * -

Satisfaction: “Using learning tool for the muscle painting activity was
satisfying.”

  * -

Learning Perception: “I learned a lot about muscle anatomy using/interacting
with learning tool.”

  * -

Enjoyment: “I found using learning tool to be enjoyable.”

  * -

Effort to Focus: “I had to make an effort to keep my mind on the activity.”

  * -

Lost Track of Time: “Time seemed to pass very quickly during the painting
activity.”

  * -

Willingness to Recommend: “I would recommend that my friends use learning tool
to study human muscles.”

  * -

Learning Motivation: “Using learning tool increased my enthusiasm for learning
more about human anatomy.”

Considering the potential benefits of interactive visualizations in our 3D
visualization tool, and more intuitive and direct information display on the
real body in the AR tool [65], we established the following hypotheses for the
measures:

  * •

H1: The participants in the Tablet-3D condition or the Screen-AR condition
will have more positive ratings than those in the Textbook condition for all
the measures.
(Textbook $<$ Tablet-3D, Screen-AR)

  * •

H2: The participants in the Screen-AR condition will further have more
positive experience than those in the Tablet-3D condition for all the
measures. (Tablet-3D $<$ Screen-AR)

As Figure 1 shows, the anatomy models in all learning tools are male-based.
Compared to female students, male students can easily recognize and access the
muscles according to the same anatomical structures. Also, given the prior
research has shown that females tend to be more conservative about being
touched [66], we established the following hypotheses to understand the role
of the gender, gender composition:

  * •

H3: The male participants (male pairs) in the study will report more positive
ratings than the female participants (females or mixed pairs), particularly in
the measures like easy to paint and easy to find muscles.
(Female $<$ Male) and (Mixed $<=$ Females $<$ Males)

####  III-F2 Short-term Knowledge Retention

To examine the learning performance in anatomy education, we evaluated the
participant’s anatomy knowledge retention. We collected the participants’
anatomy knowledge scores from pre- and post-tests, and calculated the score
gain by subtracting the pre-test score from the post-test score.

  * -

Pre-Score: The pre-test was a matching test with five questions in a provided
diagram of the human anatomy muscle system. In the anatomy diagram, 15 regions
of the body were highlighted, and participants were asked to match five muscle
labels provided in the pre-test with one of these 15 body regions. The number
of correct matches was reported as the pre-test score in the range of [0, 5].

  * -

Post-Score: The post-test had a different (lateral) view of human anatomy,
with a matching test similar to the pre-test. The number of correct matches
was reported as the post-test score in the range of [0, 5]. Both pre- and
post-tests were designed and evaluated by anatomy instructors to be at the
same level of difficulty.

  * -

Score Gain: Score gain, as the difference between pre- and post-test scores,
was calculated in [-5, 5] range.

Based on the positive outcome that we anticipate in the Tablet-3D and Screen-
AR conditions, we established the following hypotheses similar to H1 and H2
regarding the learning performance (or the improvement of anatomy knowledge
retention):

  * •

H4: The participants in the Tablet-3D condition or the Screen-AR condition
will have a higher score gain than those in the Textbook condition.
(Textbook $<$ Tablet-3D, Screen-AR)

  * •

H5: The participants in the Screen-AR condition will further have a higher
score gain than those in the Tablet-3D condition.  (Tablet-3D $<$ Screen-AR)

##  IV Results

This section reports our analysis results considering the hypotheses we
established in Section III-F. Since we are also interested in the possible
effects of participant’s gender on our measures, we have two factors to
consider in our analysis: (1) learning tool and (2) participant’s gender. We
first conducted two-way ANOVAs with these two factors to see if there is any
interaction effect between the factors on both and performance measures. We
did not find any significant interactions between the learning tool and the
participant gender; thus, we focused on the main effects of each factor using
one-way ANOVAs ($\alpha\\!=\\!0.05$). Multiple comparisons with Bonferroni
correction were conducted for post-hoc tests.

###  IV-A Learning Experience

Here, we report the results of our analysis on the measures for both learning
tool, participant gender, and group gender composition factors. The detailed
results for each measure are described below, and the overviews of the
learning tool effects and the gender effects are summarized in Table II.

##### Easy to Paint

The one-way ANOVA for the measure of “easy to paint” showed a significant
effect of the learning tool ($F(2,233)\\!=\\!7.786$, p $=$ 0.0005;
$\eta_{\text{p}}^{2}\\!=\\!0.063$ \- medium to large effect size). The post-
hoc tests revealed that Tablet-3D ($M\\!=\\!4.67$, $SD\\!=\\!0.656$) had a
higher score than Textbook ($M\\!=\\!4.26$, $SD\\!=\\!0.872$; p $=$ 0.009) or
Screen-AR ($M\\!=\\!4.18$, $SD\\!=\\!1.016$; p $=$ 0.001). This suggests that
the participants felt the Tablet-3D condition was easier to perform the muscle
painting activity than the other two conditions. The analysis for the
participant gender did not show any significant effect.

##### Easy to Find Muscles

For the “easy to find muscles” we did not find any significant effect of the
learning tool. We found that this measure was significantly different among
three group gender compositions ($F(2,233)\\!=\\!5.62$, p $=$ 0.004; male
pairs: $M\\!=\\!4.645$, $SD\\!=\\!0.630$; female pairs: $M\\!=\\!4.398$,
$SD\\!=\\!0.875$; mixed pairs: $M\\!=\\!4.136$, $SD\\!=\\!1.006$). Based on
the post-hoc comparison with the Bonferroni test, the group with same gender
compositions (males pairs and females pairs) were easier to perform the muscle
painting activity than the mixed pairs (p $=$ 0.005). Moreover, we found a
significant effect of the participant’s gender on this measure, too
($F(1,234)\\!=\\!7.065$, p $=$ 0.008; $\eta_{\text{p}}^{2}\\!=\\!0.029$ \-
small to medium effect size). The result showed that the male participants
($M\\!=\\!4.55$, $SD\\!=\\!0.632$) reported a significantly higher score for
the easiness of finding muscles than the female participants ($M\\!=\\!4.26$,
$SD\\!=\\!0.937$).

TABLE II: The summary of Learning Experience inferential results. (* p $<$
0.05, ** p $<$ 0.01, *** p $<$ 0.001.)

Variable | F | p | Sig | $\eta_{p}^{2}$
---|---|---|---|---
Easy to Paint |  |  |  |
Learning tool | 7.786 | 0.0005 | *** | 0.063
Gender | 1.854 | 0.175 |  |
Easy to Find Muscles |  |  |  |
Gender | 7.065 | 0.008 | ** | 0.029
Gender composition | 5.62 | 0.004 | ** |
Satisfaction |  |  |  |
Learning tool | 0.207 | 0.813 |  |
Gender | 1.564 | 0.212 |  |
Gender composition | 3.91 | 0.021 | * |
Learning Perception |  |  |  |
Learning tool | 0.009 | 0.991 |  |
Gender | 6.245 | 0.013 | * | 0.026
Gender composition | 3.63 | 0.028 | * |
Enjoyment |  |  |  |
Learning tool | 9.87 | 0.0001 | *** | 0.078
Gender | 10.008 | 0.002 | ** | 0.041
Gender composition | 4.03 | 0.019 | * |
Effort to Focus |  |  |  |
Learning tool | 1.679 | 0.189 |  |
Gender | 0.598 | 0.44 |  |
Gender composition | 1.84 | 0.161 |  |
Lost Track of Time |  |  |  |
Learning tool | 0.332 | 0.718 |  |
Gender | 0.413 | 0.521 |  |
Gender composition | 0.36 | 0.696 |  |
Willingness to Recommend |  |  |  |
Learning tool | 4.596 | 0.011 | * | 0.038
Gender | 4.323 | 0.039 | * | 0.018
Gender composition | 1.19 | 0.306 |  |
Learning Motivation |  |  |  |
Learning tool | 7.441 | 0.0007 | *** | 0.06
Gender | 4.907 | 0.028 | * | 0.021
Gender composition | 2.95 | 0.055 |  |

##### Satisfaction

We did not find any significant effects of both the learning tool and the
participant gender. However, this “satisfaction” measure was significantly
different in gender compositions ($F(2,233)\\!=\\!3.91$, p $=$ 0.021). Among
three different gender compositions (male pairs: $M\\!=\\!4.484$,
$SD\\!=\\!0.671$; female pairs: $M\\!=\\!4.259$, $SD\\!=\\!0.921$; mixed
pairs: $M\\!=\\!4.045$, $SD\\!=\\!0.999$), Bonferroni test showed that male
pairs had significantly higher satisfaction than mixed pairs (p $=$ 0.017).

##### Learning Perception

For this measure, we did not find a significant effect of the learning tool,
but there was a main effect of the participant gender with a statistical
significance ($F(1,234)\\!=\\!6.245$, p $=$ 0.013;
$\eta_{\text{p}}^{2}\\!=\\!0.026$ \- small to medium effect size). This showed
again that the male participants ($M\\!=\\!4.08$, $SD\\!=\\!0.834$) had a
higher score than the female participants ($M\\!=\\!3.76$, $SD\\!=\\!1.062$).
Also, a significant effect for the “learning perception” measure was observed
($F(2,233)\\!=\\!3.63$, p $=$ 0.028) among gender compositions of male pairs
($M\\!=\\!4.177$, $SD\\!=\\!0.758$), female pairs ($M\\!=\\!3.778$,
$SD\\!=\\!1.026$), and mixed pairs ($M\\!=\\!3.803$, $SD\\!=\\!1.084$).
Bonferroni test indicated that male pairs had a significantly higher score
than female pairs (p $=$ 0.034).

##### Enjoyment

For the “enjoyment” we found main effects of the learning tool, the
participant gender, and group gender composition factors. For the learning
tool ($F(2,233)\\!=\\!9.870$, p $=$ 0.0001; $\eta_{\text{p}}^{2}\\!=\\!0.078$
\- medium to large effect size), we further compared the conditions and found
that Textbook ($M\\!=\\!3.78$, $SD\\!=\\!1.051$) had a lower score than
Tablet-3D ($M\\!=\\!4.32$, $SD\\!=\\!0.865$; p $=$ 0.001) and Screen-AR
($M\\!=\\!4.41$, $SD\\!=\\!0.897$; p $=$ 0.001). For the gender effect
($F(1,234)\\!=\\!10.008$, p $=$ 0.002; $\eta_{\text{p}}^{2}\\!=\\!0.041$ \-
small to medium effect size), the result showed that the male participants
($M\\!=\\!4.42$, $SD\\!=\\!0.807$) had a higher score than the female
participants ($M\\!=\\!4.02$, $SD\\!=\\!1.038$). For the gender composition
(male pairs: $M\\!=\\!4.468$, $SD\\!=\\!0.804$; female pairs: $M\\!=\\!4.037$,
$SD\\!=\\!1.049$; mixed pairs: $M\\!=\\!4.152$, $SD\\!=\\!0.932$), a
significant difference was observed ($F(2,233)\\!=\\!4.03$, p $=$ 0.019). The
Bonferroni post-hoc test showed that the male pairs had a significantly higher
score than the female pairs (p $=$ 0.016).

##### Effort to Focus

There were no significant effects found in the “effort to focus” measure.

##### Lost Track of Time

For learning tool and gender-related factors, there were also no significant
effects for this measure.

##### Willingness to Recommend

We found a main effect of the learning tool for the “willingness to recommend”
($F(2,233)\\!=\\!4.596$, p $=$ 0.011; $\eta_{\text{p}}^{2}\\!=\\!0.038$ \-
small to medium effect size). The post-hoc tests revealed that Textbook
($M\\!=\\!6.96$, $SD\\!=\\!2.185$) had a lower score than Tablet-3D
($M\\!=\\!7.82$, $SD\\!=\\!2.003$; p $=$ 0.024) and Screen-AR ($M\\!=\\!7.83$,
$SD\\!=\\!1.865$; p $=$ 0.028). The gender effect was also found for this
($F(1,234)\\!=\\!4.323$, p $=$ 0.039; $\eta_{\text{p}}^{2}\\!=\\!0.018$ \-
small to medium effect size), which showed that the male participants
($M\\!=\\!7.89$, $SD\\!=\\!1.825$) had a higher score than the female
participants ($M\\!=\\!7.33$, $SD\\!=\\!2.164$). On the contrary, no group
gender composition effect was identified.

##### Learning Motivation

We found the main effects of the learning tool and participant gender. The
effect of the learning tool was significant ($F(2,233)\\!=\\!7.441$, p $=$
0.0007; $\eta_{\text{p}}^{2}\\!=\\!0.060$ \- medium effect size), and the
post-hoc tests showed that Textbook ($M\\!=\\!3.57$, $SD\\!=\\!1.032$) had a
lower score than Tablet-3D ($M\\!=\\!3.94$, $SD\\!=\\!0.987$; p $=$ 0.044) and
Screen-AR ($M\\!=\\!4.17$, $SD\\!=\\!0.839$; p $=$ 0.001). The gender effect
was also significant ($F(1,234)\\!=\\!4.907$, p $=$ 0.028;
$\eta_{\text{p}}^{2}\\!=\\!0.021$ \- small to medium effect size), showing
that the male participants ($M\\!=\\!4.07$, $SD\\!=\\!0.902$) had a higher
score than the female participants ($M\\!=\\!3.79$, $SD\\!=\\!1.020$). Based
on Bonferroni test, we further compared the pairs and found that the male
pairs had a significantly higher score than the female pairs for “learning
motivation” (p $=$ 0.048).

###  IV-B Learning Performance

To ensure valid knowledge retention evaluation, we analyzed pre-score
differences among learning tool conditions and gender groups. This involved
assessing the balance in prior anatomy knowledge within learning tool groups
and gender compositions. We did not find any differences in the pre-score
measure among the learning tool conditions. However, we found a significant
difference between the male and female participants (p $=$ 0.019), which
showed the male participants ($M\\!=\\!2.05$, $SD\\!=\\!1.283$) had a higher
pre-score than the female participants ($M\\!=\\!1.69$, $SD\\!=\\!1.070$).
While controlling the pre-score as a covariate, we conducted one-way ANCOVA to
investigate the effects of the learning tool, the participant’s gender, or the
group gender composition; however, no significant results were found.Among
different learning tool conditions, compared with the score gain
($M\\!=\\!0.069$, $SD\\!=\\!1.437$) and the post-score ($M\\!=\\!1.92$,
$SD\\!=\\!1.441$) in the Textbook condition, participants in the Tablet-3D
condition achieved the highest score gain ($M\\!=\\!0.10$, $SD\\!=\\!1.447$)
with post-score ($M\\!=\\!1.82$, $SD\\!=\\!1.282$); Screen-AR groups achieved
the second highest score gain ($M\\!=\\!0.079$, $SD\\!=\\!1.512$) with post-
score ($M\\!=\\!2.04$, $SD\\!=\\!1.501$). With respect to the participant’s
gender, females achieved a higher score gain ($M\\!=\\!0.20$,
$SD\\!=\\!1.455$) with post-score ($M\\!=\\!1.89$, $SD\\!=\\!1.342$) than
males’ score gain ($M\\!=\\!-0.084$, $SD\\!=\\!1.456$) with post-score
($M\\!=\\!1.97$, $SD\\!=\\!1.491$). Among three group gender compositions,
compared with the score gain ($M\\!=\\!-0.145$, $SD\\!=\\!1.401$) and the
post-score ($M\\!=\\!1.887$, $SD\\!=\\!1.307$) of the male pairs, female
groups achieved the highest score gain ($M\\!=\\!0.269$, $SD\\!=\\!1.464$)
with post-score ($M\\!=\\!1.852$, $SD\\!=\\!1.288$); mixed pairs achieved the
second highest score gain ($M\\!=\\!0$, $SD\\!=\\!1.488$) with post-score
($M\\!=\\!2.06$, $SD\\!=\\!1.654$). There are no main or interaction effects
on the post-score and the score gain among the three factors: learning tool,
gender, and group gender composition.

##  V Discussion

In this section, we summarize our findings based on the results while
connecting them to our established hypotheses. We also discuss the
justifications and implications of our findings and associate them with
previous research.

##### Digital Tools Improved Learning Experience

We found several significant effects of the learning tool for some of our
measures, which we reported in Section IV-A. In general, the results show that
the Tablet-3D and the Screen-AR could provide more positive experience scores
than the Textbook, e.g., for the measures of “enjoyment,” “willingness to
recommend,” and “learning motivation,” which partly supports our H1. These
results align with previous literature findings that showed 3D visualization
technologies increased students’ engagement in anatomy learning[36, 38]. The
positive scores in some of our measures could be related to the novelty of the
VR/AR technology as an anatomy learning tool, and the intuitive and
interactive 3D models could also be an important factor in influencing the
perception of the learning experience. Some qualitative comments from the
participants in the Tablet-3D condition or the Screen-AR condition post-
session also support our reasoning for the positive scores. For example, some
of the participants in Tablet-3D or Screen-AR said:

> “Interface could be refined a little; the model was great.”
> “It was an enjoyable and educational experience.”
> “Super cool to be the model and see my body’s muscles.”
> “It was fun to paint a muscle and helped put it into the context of reality
> instead of simply seeing it on a page.”

Interestingly, however, we found no significant benefits of the Screen-AR
compared to the Tablet-3D, which we expected in our H2. Instead, we found a
higher score in the Tablet-3D condition than the Screen-AR condition for the
“easy to paint” measure. Based on the participants’ feedback below, we
realized that some participants complained about the intermittent misalignment
of virtual contents in the Screen-AR condition.

> “The calibration was not great and it was hard to actually see the muscle
> projected onto myself and my partner.”
> “Sometimes it was hard to see which muscle was being pointed.”
> “It was a little hard to distinguish where in the muscle was sometimes
> because the overlay wasn’t actually exact/not proportional to my body.”

This implies that the participants were quite sensitive to the accuracy and
reliability of body tracking for AR content registration, which could be
crucial for the learning experience. Some major contributors to this AR
accuracy issue include the 3D virtual models, which followed a male anatomy
limb proportions rather than female, and the physical proximity of the painter
and paintee in front of the body tracking system.

##### No Influence on Learning Performance

Positive effects of our digital tools on learning performance based on prior
research was expected [28, 67, 16]. As we reported in SectionIV-B, there were
no significant effects of the learning tool or the participant’s gender for
the learning performance, e.g., the anatomy knowledge retention, which means
no evidence to support our H4 and H5. However, prior research showed that
positive experience could increase learning performance and retention [28,
67], and our proposed 3D visualization and AR learning tools did promote more
positive compared to the traditional textbook-based learning. In that sense,
we are inspired to conduct further research on different learning analytic
metrics. Additionally, the intervention that the participants had in our study
was only a one-time session for about 10–15 minutes, which could not be enough
to reveal the positive effects of AR on learning performance. As most learning
science studies require longitudinal studies for their efficacy, we will
propose a longitudinal study in future.

##### Male Users Noted More Positive Experience

Beyond investigating the learning tool effects, we also established an
interesting question about gender effects in anatomy education, as introduced
in H3 in Section III-F. We found significant effects of the participant gender
on various measures: “easy to find muscles,” “learning perception,”
“enjoyment,” “willingness to recommend,” and “learning motivation.” For all
those measures, male participants reported higher (more positive) scores than
female participants, which partly supports our H3. Considering gender
composition, similar patterns were observed on the “easy to find muscles,”
“learning perception,” and “enjoyment” measures. When comparing gender
compositions, we found statistically significant differences between male
pairs and mixed pairs, as well as between male pairs and female pairs. Male
pairs reported the highest learning experience scores. These finding also
provides partial support for H3. We should note that this effect was not
associated with a specific learning tool. There could be some aspects of our
learning intervention that female participants did not like compared to male
participants, which seemed to be the painting part, not the learning tool.
Female participants in our study might be more conservative about the body
painting activity than males. According to prior research [14, 66], unequal
engagement of students in body painting activities may be due to cultural,
social, and religious barriers concerning body image, nudity, gender,
vulnerability, and embarrassment. Not surprisingly, such tendencies were
reflected in the after-session feedback from some female participants.

> “Painting was unnecessary, but it was cool to see the muscles on my body.”
> “[The activity may] take into account clothes and girls.”
> “I am not a fan of body painting. However, I love the program and think that
> it is really cool.”’
> “I don’t like having paint on my skin.”

These observations emphasize the importance of gender perspectives in the
design. It also recommends exploring a new learning module, one that doesn’t
involve body painting.

##### Future Work and Limitations

The overarching goal of our study was to explore and understand the possible
effects of using advanced learning technologies on anatomy learning and
student performance. Identifying anatomical parts in a real body, as in our
current study, is primarily acknowledged in clinical anatomy. While not
immediately applicable to sessions involving cadavers or anatomy models,
exploring this avenue, such as using a Kinect sensor positioned above a
patient bed, presents an interesting future path. Our discussion unveils that
we require multi-session, long-term, and modified studies to draw more
conclusive findings for knowledge retention and task completion time. As
noted, some students liked the anatomy learning tools but disliked the body
painting activity, so the selection of body painting as baseline activity may
have been a confounding factor. In addition, this paper covered dyadic teams
and we plan to analyze our data with participants in larger teams while
controlling for demographics of age, gender, and prior knowledge level to form
more uniform teams. We also plan to expand student learning analysis by
collecting and using multi-modal measurements for more effective and objective
learning tool evaluations.

##  VI Conclusion

This paper investigated the effects of digital anatomy learning tools, using
our in-house tablet-based 3D visualization system and the screen-based AR
application overlaying 3D anatomy structures onto the digital mirror image of
students. We conducted a large-scale study with 236 premedical students, and
compared our digital learning tools with conventional textbook. Our results
indicated that both digital learning tools could improve the learning
experience, particularly in “enjoyment”, “willingness to recommend”, and
“learning motivation”. We also found that male participants generally reported
more positive experiences than females. Our findings include further
considerations of learners’ gender and performance in body painting anatomy
education. Based on the enhanced, progressive short-term learning performance
and enjoyment in our study, we expect a similar trend in the long-term
retention of future 3D technology.

## Acknowledgment

We wish to express our gratitude to the study participants and laboratory
assistants. We wish to acknowledge the support from the support of the Johns
Hopkins University Science of Learning Institute, and National Science
Foundation for award #2321274. Any opinions, findings, and conclusions
expressed in this material are those of the authors.

## References

  * [1] S. Blanchard, “Anatomy and physiology,” in _Introduction to Biomedical Engineering (Second Edition)_ , second edition ed., ser. Biomedical Engineering, J. D. Enderle, S. M. Blanchard, and J. D. Bronzino, Eds. Boston: Academic Press, 2005, pp. 73–125.
  * [2] P. J. O’Byrne, A. Patry, and J. A. Carnegie, “The development of interactive online learning tools for the study of anatomy,” _Medical Teacher_ , vol. 30, no. 8, pp. e260–e271, 2008.
  * [3] R. A. Green, L. Y. Whitburn, A. Zacharias, G. Byrne, and D. L. Hughes, “The relationship between student engagement with online content and achievement in a blended learning anatomy course,” _Anatomical sciences education_ , vol. 11, no. 5, pp. 471–477, 2018.
  * [4] A. Y.-C. C. Chan, O. T. Cate, E. J. Custers, M. S. van Leeuwen, and R. L. Bleys, “Approaches of anatomy teaching for seriously resource-deprived countries: A literature review.” _Education for Health: Change in Learning & Practice_, vol. 32, no. 2, 2019.
  * [5] B. C. Leung, M. Williams, C. Horton, and T. D. Cosker, “Modernising anatomy teaching: Which resources do students rely on?” _Journal of medical education and curricular development_ , vol. 7, pp. 1–7, 2020.
  * [6] K.-k. Leung, K.-S. Lu, T.-S. Huang, and B.-S. Hsieh, “Anatomy instruction in medical schools: connecting the past and the future,” _Advances in Health Sciences Education_ , vol. 11, no. 2, pp. 209–215, 2006, publisher: Springer.
  * [7] A. Winkelmann, “Anatomical dissection as a teaching method in medical school: a review of the evidence,” _Medical education_ , vol. 41, no. 1, pp. 15–22, 2007, publisher: Wiley Online Library.
  * [8] C. Lovis, “Mixed and Augmented Reality Tools in the Medical Anatomy Curriculum,” _Digital Personalized Health and Medicine: Proceedings of MIE 2020_ , vol. 270, pp. 322–326, 2020, publisher: IOS Press.
  * [9] R. Marmulla, H. Hoppe, J. Mühling, and G. Eggers, “An augmented reality system for image-guided surgery: This article is derived from a previous article published in the journal International Congress Series,” _International journal of oral and maxillofacial surgery_ , vol. 34, no. 6, pp. 594–596, 2005, publisher: Elsevier.
  * [10] M. Romand, D. Dugas, C. Gaudet-Blavignac, J. Rochat, and C. Lovis, “Mixed and augmented reality tools in the medical anatomy curriculum,” _Studies in Health Technology and Informatics_ , vol. 270, no. June, pp. 322–326, 2020.
  * [11] T. Huk, “Who benefits from learning with 3D models? The case of spatial ability,” _Journal of computer assisted learning_ , vol. 22, no. 6, pp. 392–404, 2006, publisher: Wiley Online Library.
  * [12] L. Lipponen, “The challenges for computer supported collaborative learning in elementary and secondary level: Finnish perspectives,” 1999, publisher: International Society of the Learning Sciences (ISLS).
  * [13] M. Stieff, “Mental rotation and diagrammatic reasoning in science,” _Learning and instruction_ , vol. 17, no. 2, pp. 219–234, 2007, publisher: Elsevier.
  * [14] N. E. Cookson, J. J. Aka, and G. M. Finn, “An exploration of anatomists’ views toward the use of body painting in anatomical and medical education: An international study,” _Anatomical sciences education_ , vol. 11, no. 2, pp. 146–154, 2018.
  * [15] C. M. Diaz and T. Woolley, ““learning by doing”: a mixed-methods study to identify why body painting can be a powerful approach for teaching surface anatomy to health science students,” _Medical Science Educator_ , vol. 31, no. 6, pp. 1875–1887, 2021.
  * [16] R. Barmaki, K. Yu, R. Pearlman, R. Shingles, F. Bork, G. M. Osgood, and N. Navab, “Enhancement of Anatomical Education Using Augmented Reality: An Empirical Study of Body Painting,” _Anatomical sciences education_ , vol. 12, no. 6, pp. 599–609, 2019.
  * [17] F. Nainggolan, B. Siregar, and F. Fahmi, “User experience in using vive controller as a controller in anatomy learning system in virtual reality environment,” in _Journal of Physics: Conference Series_ , vol. 1566, no. 1. IOP Publishing, 2020, p. 012096.
  * [18] J. Snelling, A. Sahai, and H. Ellis, “Attitudes of medical and dental students to dissection,” _Clinical Anatomy: The Official Journal of the American Association of Clinical Anatomists and the British Association of Clinical Anatomists_ , vol. 16, no. 2, pp. 165–172, 2003, publisher: Wiley Online Library.
  * [19] R. B. Gunderman and P. K. Wilson, “Exploring the human interior: The roles of cadaver dissection and radiologic imaging in teaching anatomy,” _Academic Medicine_ , vol. 80, no. 8, pp. 745–749, 2005, publisher: LWW.
  * [20] R. B. Trelease, G. L. Nieder, J. Dørup, and M. S. Hansen, “Going virtual with QuickTime VR: new methods and standardized tools for interactive dynamic visualization of anatomical structures,” _The Anatomical Record: An Official Publication of the American Association of Anatomists_ , vol. 261, no. 2, pp. 64–77, 2000, publisher: Wiley Online Library.
  * [21] J. C. McLachlan, J. Bligh, P. Bradley, and J. Searle, “Teaching anatomy without cadavers,” _Medical education_ , vol. 38, no. 4, pp. 418–424, 2004, publisher: Wiley Online Library.
  * [22] M. E. DeHoff, K. L. Clark, and K. Meganathan, “Learning outcomes and student-perceived value of clay modeling and cat dissection in undergraduate human anatomy and physiology,” _Advances in physiology education_ , vol. 35, no. 1, pp. 68–75, 2011, publisher: American Physiological Society Bethesda, MD.
  * [23] T. A. Hays, “Spatial abilities and the effects of computer animation on short-term and long-term comprehension,” _Journal of educational computing research_ , vol. 14, no. 2, pp. 139–155, 1996, publisher: SAGE Publications Sage CA: Los Angeles, CA.
  * [24] J. P. Chickness, K. M. Trautman-Buckley, K. Evey, and L. Labranche, “Novel development of a 3d digital mediastinum model for anatomy education,” _Translational Research in Anatomy_ , vol. 26, p. 100158, 2022.
  * [25] K. Yammine and C. Violato, “A meta-analysis of the educational effectiveness of three-dimensional visualization technologies in teaching anatomy,” _Anatomical sciences education_ , vol. 8, no. 6, pp. 525–538, 2015, publisher: Wiley Online Library.
  * [26] N. Golenhofen, F. Heindl, C. Grab-Kroll, D. A. Messerer, T. M. Böckers, and A. Böckers, “The use of a mobile learning tool by medical students in undergraduate anatomy and its effects on assessment outcomes,” _Anatomical sciences education_ , 2019, publisher: Wiley Online Library.
  * [27] R. R. Lemos, C. M. Rudolph, A. V. Batista, K. R. Conceição, P. F. Pereira, B. S. Bueno, P. J. Fiuza, and S. S. Mansur, “Design of a Web3D Serious Game for Human Anatomy Education: A Web3D Game for Human Anatomy Education,” in _Handbook of Research on Immersive Digital Games in Educational Environments_. IGI Global, 2019, pp. 586–611.
  * [28] H. Maresky, A. Oikonomou, I. Ali, N. Ditkofsky, M. Pakkal, and B. Ballyk, “Virtual reality and cardiac anatomy: Exploring immersive three-dimensional cardiac imaging, a pilot study in undergraduate medical anatomy education,” _Clinical Anatomy_ , vol. 32, no. 2, pp. 238–243, 2019, publisher: Wiley Online Library.
  * [29] L. Donnelly, D. Patten, P. White, and G. Finn, “Virtual human dissector as a learning tool for studying cross-sectional anatomy,” _Medical teacher_ , vol. 31, no. 6, pp. 553–555, 2009.
  * [30] I. D. Keenan and A. ben Awadh, “Integrating 3d visualisation technologies in undergraduate anatomy education,” _Biomedical Visualisation: Volume 1_ , pp. 39–53, 2019.
  * [31] D. Barrack, D. Horn, and B. Benninger, “A step by step visual guide to using the sectra visualization table for 1st and 2nd year medical students,” _The FASEB Journal_ , vol. 29, pp. 692–8, 2015.
  * [32] K. H. A. Lim, Z. Y. Loo, S. J. Goldie, J. W. Adams, and P. G. McMenamin, “Use of 3D printed models in medical education: a randomized control trial comparing 3D prints versus cadaveric materials for learning external cardiac anatomy,” _Anatomical sciences education_ , vol. 9, no. 3, pp. 213–221, 2016, publisher: Wiley Online Library.
  * [33] F. Bork, R. Barmaki, U. Eck, K. Yu, C. Sandor, and N. Navab, “Empirical study of non-reversing magic mirrors for augmented reality anatomy learning,” in _2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)_. IEEE, 2017, pp. 169–176.
  * [34] J. N. Silva, M. Southworth, C. Raptis, and J. Silva, “Emerging applications of virtual reality in cardiovascular medicine,” _JACC: Basic to Translational Science_ , vol. 3, no. 3, pp. 420–430, 2018, number: 3 Publisher: JACC: Basic to Translational Science.
  * [35] J. L. Bacca Acosta, S. M. Baldiris Navarro, R. Fabregat Gesa, S. Graf _et al._ , “Augmented reality trends in education: a systematic review of research and applications,” _Journal of Educational Technology and Society, 2014, vol. 17, núm. 4, p. 133-149_ , 2014.
  * [36] M. Hackett and M. Proctor, “The effect of autostereoscopic holograms on anatomical knowledge: a randomised trial,” _Medical education_ , vol. 52, no. 11, pp. 1147–1155, 2018, number: 11 Publisher: Wiley Online Library.
  * [37] C. Jacob, S. von Mammen, T. Davison, A. Sarraf-Shirazi, V. Sarpe, A. Esmaeili, D. Phillips, I. Yazdanbod, S. Novakowski, S. Steil _et al._ , “Lindsay virtual human: Multi-scale, agent-based, and interactive,” in _Advances in Intelligent Modelling and Simulation_. Springer, 2012, pp. 327–349.
  * [38] J.-M. Luursema, W. B. Verwey, P. A. Kommers, and J.-H. Annema, “The role of stereopsis in virtual anatomical learning,” _Interacting with Computers_ , vol. 20, no. 4-5, pp. 455–460, 2008, publisher: Oxford University Press Oxford, UK.
  * [39] B. Preim and P. Saalfeld, “A survey of virtual human anatomy education systems,” _Computers & Graphics_, vol. 71, pp. 132–153, 2018.
  * [40] E. Duncan-Vaidya and E. Stevenson, “The Effectiveness of an Augmented Reality Head-Mounted Display in Learning Skull Anatomy at a Community College.” _Anatomical Sciences Education_ , 2020.
  * [41] S. Kolla, M. Elgawly, J. P. Gaughan, and E. Goldman, “Medical student perception of a virtual reality training module for anatomy education,” _Medical Science Educator_ , vol. 30, pp. 1201–1210, 2020.
  * [42] J. Barrow, C. Forker, A. Sands, D. O’Hare, and W. Hurst, “Augmented Reality for Enhancing Life Science Education,” in _VISUAL 2019-The Fourth International Conference on Applications and Systems of Visual Paradigms_ , 2019.
  * [43] D. Chytas, M. Piagkou, T. Demesticha, G. Tsakotos, and K. Natsis, “Are extended reality technologies (erts) more effective than traditional anatomy education methods?” _Surgical and Radiologic Anatomy_ , vol. 44, no. 9, pp. 1215–1218, 2022.
  * [44] M. H. Kurniawan, G. Witjaksono _et al._ , “Human anatomy learning systems using augmented reality on mobile application,” _Procedia Computer Science_ , vol. 135, pp. 80–88, 2018.
  * [45] F. Fahmi, K. Tanjung, F. Nainggolan, B. Siregar, N. Mubarakah, and M. Zarlis, “Comparison study of user experience between virtual reality controllers, leap motion controllers, and senso glove for anatomy learning systems in a virtual reality environment,” in _IOP Conference Series: Materials Science and Engineering_ , vol. 851, no. 1. IOP Publishing, 2020, p. 012024.
  * [46] R. Barmaki and Z. Guo, “Deep neural networks for collaborative learning analytics: Evaluating team collaborations using student gaze point prediction,” _Australasian Journal of Educational Technology_ , vol. 36, no. 6, pp. 53–71, 2020.
  * [47] F. Bork, R. Barmaki, U. Eck, P. Fallavolita, B. Fuerst, and N. Navab, “Exploring non-reversing magic mirrors for screen-based augmented reality systems,” in _2017 IEEE virtual reality (VR)_. IEEE, 2017, pp. 373–374.
  * [48] E. A. Duncan-Vaidya and E. L. Stevenson, “The effectiveness of an augmented reality head-mounted display in learning skull anatomy at a community college,” _Anatomical Sciences Education_ , vol. 14, no. 2, pp. 221–231, 2021.
  * [49] L. Fernandez-Sanz and S. Misra, “Analysis of cultural and gender influences on teamwork performance for software requirements analysis in multinational environments,” _IET software_ , vol. 6, no. 3, pp. 167–175, 2012, publisher: IET.
  * [50] J. Wegge, C. Roth, B. Neubach, K.-H. Schmidt, and R. Kanfer, “Age and gender diversity as determinants of performance and health in a public organization: the role of task complexity and group size.” _Journal of Applied Psychology_ , vol. 93, no. 6, p. 1301, 2008, publisher: American Psychological Association.
  * [51] L. A. Meadows, D. Sekaquaptewa, and M. C. Paretti, “Interactive panel: Improving the experiences of marginalized students on engineering design teams,” in _ASEE Annual Conference & Exposition: Excellence in Education_, vol. 26, 2015, p. 1.
  * [52] N. Nagappan, L. Williams, M. Ferzli, E. Wiebe, K. Yang, C. Miller, and S. Balik, “Improving the CS1 experience with pair programming,” _ACM SIGCSE Bulletin_ , vol. 35, no. 1, pp. 359–362, 2003, publisher: ACM New York, NY, USA.
  * [53] J. B. Bear and A. W. Woolley, “The role of gender in team collaboration and performance,” _Interdisciplinary science reviews_ , vol. 36, no. 2, pp. 146–153, 2011, publisher: Taylor & Francis.
  * [54] M. De Paola, F. Gioia, and V. Scoppa, “Teamwork, Leadership and Gender,” IZA Discussion Papers, Tech. Rep., 2018.
  * [55] A. H. Eagly and L. L. Carli, “The female leadership advantage: An evaluation of the evidence,” _The leadership quarterly_ , vol. 14, no. 6, pp. 807–834, 2003, number: 6 Publisher: Elsevier.
  * [56] P. Rabbitt, C. Donlan, P. Watson, L. McInnes, and N. Bent, “Unique and interactive effects of depression, age, socioeconomic advantage, and gender on cognitive performance of normal healthy older people.” _Psychology and aging_ , vol. 10, no. 3, p. 307, 1995, number: 3 Publisher: American Psychological Association.
  * [57] K. W. Schaie and S. L. Willis, “Age difference patterns of psychometric intelligence in adulthood: generalizability within and across ability domains.” _Psychology and aging_ , vol. 8, no. 1, p. 44, 1993, publisher: American Psychological Association.
  * [58] J. Andersson, “Net effect of memory collaboration: How is collaboration affected by factors such as friendship, gender and age?” _Scandinavian journal of Psychology_ , vol. 42, no. 4, pp. 367–375, 2001, number: 4 Publisher: Wiley Online Library.
  * [59] F. R. Prinsen, M. L. Volman, and J. Terwel, “Gender-related differences in computer-mediated communication and computer-supported collaborative learning,” _Journal of Computer Assisted Learning_ , vol. 23, no. 5, pp. 393–409, 2007, number: 5 Publisher: Wiley Online Library.
  * [60] S. C. Herring, _Computer-mediated communication: Linguistic, social, and cross-cultural perspectives_. John Benjamins Publishing, 1996, vol. 39.
  * [61] E. Lehtinen, K. Hakkarainen, L. Lipponen, M. Rahikainen, and H. Muukkonen, “Computer supported collaborative learning: A review,” _The JHGI Giesbers reports on education_ , vol. 10, p. 1999, 1999, publisher: University of Nijmegen Nijmegen.
  * [62] E. N. Marieb and P. B. Jackson, _Essentials of Human Anatomy & Physiology Laboratory Manual_. Pearson/Benjamin Cummings, 2006.
  * [63] P. G. McMenamin, “Body painting as a tool in clinical anatomy teaching,” _Anatomical sciences education_ , vol. 1, no. 4, pp. 139–144, 2008.
  * [64] L. Davis, F. G. Hamza-Lup, J. Daly, Y. Ha, S. Frolich, C. Meyer, G. Martin, J. Norfleet, K.-C. Lin, C. Imielinska, and others, “Application of augmented reality to visualizing anatomical airways,” in _Helmet-and Head-Mounted Displays VII_ , vol. 4711. International Society for Optics and Photonics, 2002, pp. 400–405.
  * [65] T. Blum, V. Kleeberger, C. Bichlmeier, and N. Navab, “mirracle: An augmented reality magic mirror system for anatomy education,” in _2012 IEEE Virtual Reality Workshops (VRW)_. IEEE, 2012, pp. 115–116.
  * [66] G. M. Finn and J. C. McLachlan, “A qualitative study of student responses to body painting,” _Anatomical sciences education_ , vol. 3, no. 1, pp. 33–38, 2010.
  * [67] D. T. Nicholson, C. Chalk, W. R. J. Funnell, and S. J. Daniel, “Can virtual reality improve anatomy education? A randomised controlled study of a computer-generated three-dimensional anatomical ear model,” _Medical education_ , vol. 40, no. 11, pp. 1081–1087, 2006, publisher: Wiley Online Library.