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Integration of Virtual Reality in Secondary STEM Education

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While the next generation of educational technologies (ET), such as monitor-based (MB) and virtual reality (VR) applications, are still in their infancy, they do show promise for improving education. In this study, we compared MB and VR educational technologies as alternative supplemental learning environments to traditional classroom instruction using lectures, textbooks, and physical labs. We conducted the study in four high school chemistry classes, as chemistry education is well-suited for visually enhanced explanations for learning abstract concepts, and provides a solid testing situation for current and extended reality ET. Ultimately, this research project serves as a foundation to determine whether ETs have the potential to engage high school students in their STEM classes. Successful integration of ET into the public school system curricula may be a viable solution to engage students in STEM education.
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Integration of Virtual Reality in Secondary STEM
Education
Eric Nersesian, Adam Spryszynski, Michael J. Lee
Department of Informatics
New Jersey Institute of Technology
Newark, NJ USA
{eric.nersesian, as2569, mjlee}@njit.edu
Abstract—While the next generation of educational technolo-
gies (ET), such as monitor-based (MB) and virtual reality (VR)
applications, are still in their infancy, they do show promise
for improving education. In this study, we compared MB and
VR educational technologies as alternative supplemental learning
environments to traditional classroom instruction using lectures,
textbooks, and physical labs. We conducted the study in four
high school chemistry classes, as chemistry education is well-
suited for visually enhanced explanations for learning abstract
concepts, and provides a solid testing situation for current and
extended reality ET. Ultimately, this research project serves as
a foundation to determine whether ETs have the potential to
engage high school students in their STEM classes. Successful
integration of ET into the public school system curricula may be
a viable solution to engage students in STEM education.
Index Terms—STEM Education, Educational Technology,
Computer Aided Instruction, Virtual Reality, Chemistry
INTRODUCTION
Recent research shows educational technologies having
documented improvements over traditional educational media
to a student’s visual short-term memory, abstract reasoning,
spatial cognition, and multitasking abilities [7]. Modern ed-
ucational approaches require these student skill sets to be
well-developed [6], and has led to the United States Office
of Educational Technology to identify the need for better
implementations of immersive, and engaging technologies
with proven capability to integrate into classrooms and their
curricula [30]. Educational delivery methods will need the
option to supplement the traditional methods of live teacher and
textbook instruction with technological alternatives, especially
for students entering Science, Technology, Engineering, and
Mathematics (STEM) fields since they are more likely to be
engaged with high-end technologies [19].
Global comparisons of STEM education show that the
United States (US) ranked in the top 13 countries globally
at the elementary school level [28]. US global ranking drops
6% when compared at the middle school level, and drops
an additional 11% when compared at the high school level
[28]. This decrease of US global STEM rankings as students
mature, with the largest decrease occurring at the high school
level, places the US below the global average. Research
points to many factors contributing to this reduction in
STEM education performance, and recognizes that a major
factor is the discrepancy in the high number of multimedia
experiences focusing on entertainment, compared to education
[11]. Successful integration of educational technology into the
public school system curricula may be a viable solution to
engage students in STEM education and help the United States
keep pace with global standards for STEM education.
The current generation of monitor-based (MB) educational
technologies, such as simulations, animations, and video games,
has been well explored as learning tools within the STEM
fields [18], [26]. While the next generation of educational
technologies, such as virtual reality (VR), are still in their
infancy, they do show a strong inclination toward education
[24], collaboration [29], and simulation applications [10]. VR
has the potential for strong content immersion [33], allowing
the user to interact directly with a simulation and focus on the
information presented to them. This new educational medium
may help shape how educators express ideas and how students
acquire them.
There is, however, a lack of research into extended realities
as a learning tool for widespread educational technology appli-
cations [11]. Research shows that simulations and visualization
of the topic improves understanding and engagement [35].
Therefore, we believe that visualizing chemistry processes will
have enormous benefits to chemistry learners, and provide a
solid testing situation for current and extended reality educa-
tional technologies in STEM education, since simulation has
proven to be beneficial for students to engage and comprehend
chemistry lessons [15], [35].
These learning challenges, that educational technology
simulations can aid, indicate that chemistry represents an
immediately comparable domain for measuring the likelihood
of educational technology benefits for STEM as a wider
field. Comparing well-researched MB simulations against the
less researched VR simulations within a simulation-oriented
educational environment, may yield necessary research data
to examine viability of VR as an educational technology. To
address this gap in research, we conducted a study in public
high school chemistry classes, grades 9-12, comparing the
effectiveness of three modes of supplemental instruction: 1) a
VR educational technology, 2) a MB educational technology,
and 3) traditional textbook, and physical lab work. This
research seeks to examine the educational value of educational
technology within a formal STEM classroom.
RELATED WO RK S
Issues with contextualizing—or effectively demonstrating
and visualizing—chemistry content is a constant concern for
educators, and is characterized as a disconnect between the
student’s perspectives and the pedagogical frameworks of
the topic [9], [22], [37]. Attempts to resolve this contextual
disconnect range from instructional frameworks [32], to social
media integration [23], and other educational technologies.
Laboratory work is another solution to these contextualization
issues as it provides a connection between the chemistry content
and "the real world" [20].
When used properly, non-lecture based physical labora-
tory work (or labs) improve overall learning outcomes [34],
positively effect student engagement [4], and contextualize
the topic [40]. Yet there are challenges to overcome with
implementing labs, including safety and cost. A common
solution to these challenges is virtual simulations designed to
mimic the experience of physical lab work [31]. An extensive
review by Brinson suggests that non-traditional labs, such
as virtual and remote implementations, are as effective as
traditionally implemented physical labs [12]. Even partial
emulations of lab experiences are shown to have positive effects
on the learning process. For example, Herga used animations
and dynamic simulations of chemistry models to positively
impact students’ formation of mental models [21].
MB Educational technologies often provide virtual laboratory
simulations, where certain parts of the physical lab experience
are emulated digitally to help achieve a specific learning
goal. Some examples are labSimuLab, ChemVLab+, MatLab
simulations, and Multimodal Virtual Chemistry Laboratory
(MMVCL). MMVCL is a multimodal simulation experience
designed for easy procedure guidance where students can
get feedback on their chemical mixing procedure from either
textual or tactile modes of interaction [39]. ChemVLab+ is
a desktop MB application that allows students to solve real-
world lab problems [16]. Designers of ChemVLab+ focused
on integrating science practices into an authentic context while
providing immediate feedback with simulated measuring tools.
labSimuLab is a MB learning tool where certain lab processes
are accelerated to allow more time for interpretation of results
by students [25]. Al-Moameri studied an approach of using
MatLab simulations as an core around which the textbook
was designed. Using MatLab for the simulations was proven
to be an practical solution given it’s availability and ease of
integrating computing with visualizations [2].
Gamification is also used in MB educational technology
to contextualize chemistry and increase student engagement
with curricula. For example, "Say My Name" is a gamified
MB educational technology designed to help learners practice
chemistry nomenclature [14]. Other examples include Bayir’s
three gamified MB educational technologies aimed at different
categories of chemistry concepts, which were well received
by both teachers and students, and supported the author’s
aim of increasing exposure to chemistry terms and facilitating
contextualization of chemistry learning [8].
Beyond MB implementations of laboratory simulations,
augmented reality (AR), mixed reality (MR), and VR-based
360 video technologies emulate the laboratory experience and
can also be used as standalone learning media. For example,
Akcayir demonstrated a physics laboratory AR experience, and
showed it was effective in increasing laboratory skills, improved
students’ work speed, and allowed for more discussion time
[1]. AR applications can also support learning in scenarios that
do not require fully immersive environments. An example of
this is a chemistry AR educational technology for colorimetric
titration, which was proven to be effective while remaining
cost-efficient [38].
These results align with existing literature as confirmed
by Cheng & Tsai, whom compiled a literature review of
AR educational technologies classifying them into image and
location-based AR [13]. Image-based AR delivers affordances
including spatial ability, practical laboratory skills, and concep-
tual understanding. Location-based AR provides opportunities
for supporting inquiry-based learning using collaborative role-
play gaming [13]. MR is yet another educational technology
that Barrett described as providing a more tactile experience. In
his study, he implemented an MR table as a collaborative edu-
cational technology tool for undergraduate laboratory sessions
[4]. Finally, VR-based 360 videos in the classroom showed
to be a viable educational alternative to standard videos with
stronger student immersion for contextualization [3].
METHOD
This study had three teaching conditions: MB, VR, and
traditional. It compared MB and VR educational technolo-
gies as a supplemental educational medium, specifically for
comprehension and engagement of chemistry topics, to the
traditional educational medium of textbook, and physical
lab work. It spanned a total of 18 weeks, and divided four
chemistry classes semi-randomly into three subject groups per
educational medium. The study used educational technologies,
textbooks, and physical lab work that have been developed by
professionals using established K-12 chemistry curriculum
concepts. We used standardized metrics to quantitatively
compare all three of our teaching methods.
Environment
The study took place in Dwyer Technical Academy (DTA),
an urban technology magnet school in Elizabeth, NJ, USA. DTA
has a total of 1,208 students, with approximately 300 students
per each of the four grade levels. The student population is
a diverse mixture of ethnic backgrounds in a lower income
urban district with a high percentage of immigrants and first-
generation Americans. 70% of the school’s student population
is Hispanic, with 61.8% stating Spanish as the primary language
spoken at home.
The participants were third-year students at DTA enrolled
in the four chemistry classes running that year. All chemistry
classes had the same teacher, whom agreed to have the study run
in his classes and helped integrate the weekly experiment time
into his course. Students in the classroom already had regular
access to technology, which were school-supplied laptops. The
four chemistry classes met for 90 minutes on alternating days
of the week, two in the morning and two in the afternoon.
Experimental learning time occurred for 18 weeks when the
weekly curriculum aligned directly with the learning content
of the MB and VR educational technologies. Experimental
usage of supplemental learning materials occurred twice a
week during this 18 week period in 20 minute self-contained
sessions at the end of the class’ lab time. During the experiment,
the students covered the following topics: Atoms as Building
Blocks of Matter, Arrangement of Electrons in Atoms, Periodic
Law, and Chemical Bonding. Each week during this period,
the researchers met with the teacher to align the experimental
learning time with that week’s lesson plans.
Inclusive Semi-Randomized Subject Groups
The teacher requested to make all students feel included in
the study and to limit the impact of the study on the students’
educational experience. He was concerned that students who
wanted to be involved in the study, but could not get their
parents’ permission (since they were minors) would feel left
out of the study. To help mitigate this issue, we integrated all
of the study-related assessments into the regular class structure
(i.e., anyone could participate in the voluntary assessments).
Then we randomly assigned participants into the VR or MB
experimental conditions, and had the remaining students take
the class undisturbed (i.e., in the control condition). The teacher
anonymized all the student information, including grades,
before sending the information to us.
We used a two step sampling method for subject placement in
the experimental groups. Both phases were based on probability
sampling, and therefore the samples are demographically
representative of the larger population of DTA. The first phase
was based on the school administration’s course enrollment
procedure. They used a simple random sampling method to
assign third-year students, who wanted to take chemistry, into
one of the four available course time slots
1
. Next, we collected
consent and assent forms from students (from all four classes).
From these, we randomly assigned these participants into either
1
This may have not been completely random, as the administration adjusted
individual schedules based on a student’s conflicts with other courses.
Fig. 1. Subjects in the MB educational technology group, using their laptops
to access their MB chemistry application.
the VR or MB groups. Students who opted out, left the study,
or transferred late into class, participated in the unmodified
version of the course as the control group. Each class had 24-27
students within it, for a total chemistry student population of
103. We placed 29 students in the MB educational technology
group, and 29 students in the VR educational technology group.
By the end of the study 23 from the MB group and 24 from
the VR group remained (5 and 4 students dropped out of
their groups during the duration of the study, respectively).
Of the 45 control group participants, 8 did not participate
in the voluntary assessments (for a total of 37 participants).
Unfortunately, in the excitement before the commencement
of the study, the teacher (erroneously) informed the students
about which groups they were assigned to (before we could
give them them the pre-test).
Classroom Experiment Structure
When experimental learning time was hosted, it was initiated
with a class discussion on current learning topics. The class was
then split into the three concurrently-running subject groups:
traditional educational mediums (control), MB educational tech-
nology, and VR educational technology. The teacher, assistants,
and researchers then circulated between the groups, hosting
conversations with the students as they used the supplemental
learning materials. Before the study, we trained the teacher
and assistants on best practices for each of the educational
mediums, so that everyone was prepared to help guide students
with learning or technical issues. This was meant to provide fair
and equal evaluation of all three educational mediums based
on how their respective educational capabilities were designed.
Prior to the study, we talked with representatives from each of
the software companies to learn about their preferred methods
of using their educational technology products in a classroom
environment, and were granted their approval for using their
products in our study. The teacher, whom was informed on
all the medium’s best practices, created the final classroom
structure for using these educational mediums as to not change
the pedagogical nature of the existing chemistry class.
We used a standard, nationally recognized textbook that was
already used in the classroom, called "Modern Chemistry" by
Holt et al. [17]. The teacher selected physical labs from a list
Fig. 2. Subjects in the VR educational technology group, using VR headsets
to access their VR chemistry application.
of regularly run labs in his chemistry classes. For the MB edu-
cational technology, we used Collisions
2
by PlayMada Games,
which is a collection of 2-dimensional MB games, grounded
in the rules of chemistry, that can be used to introduce, teach,
and review key concepts in secondary chemistry education.
Collisions aims to help students visualize and interact with
chemistry concepts through fun and challenging games that
integrate into class curriculum and is available on web, iPad,
and Android tablets.
For the VR educational technology, we used MEL Chemistry
VR
3
by MEL Science, which is a collection of interactive
lessons that start in a lab setting and zooms into the molecular
level, seeking to connect the macro and micro worlds into an
overall concept that can be understood by the chemistry student.
MEL Chemistry VR is available on Samsung GearVR, Oculus
Go, Google Daydream, and Google Cardboard, and aims to
remove the student from their physical environment so that
they can focus on the chemistry lessons without distractions.
The teacher selected the textbook chapters, physical labs, and
modules of the educational technologies to use each week.
He designed the use of each of the educational mediums as
self-contained lab time relating to the current class topic, and
meant to be used as supplemental learning tools alongside
instructor-led lessons.
Engagement and Knowledge Assessments
All students completed three sets of assessments as voluntary,
non-graded classroom assignments. They completed one assess-
ment one week prior to the experiment starting (the pre-test),
one week after the experiment concluded (the post-test), and
again eight weeks after the experiment concluded (the retention-
test). These sets of assessments included an attitude test and
knowledge tests. The teacher also provided the researchers
with anonymized, final exam scores and final class grades at
the end of the school semester.
2PlayMada Games’ Collisions: http://app.playmadagames.com/Collisions/
3MEL Science VR: https://melscience.com/vr/
Fig. 3. A screenshot of the MB app covering electron orbits.
To create the knowledge assessment test, we consulted with
the high school chemistry teacher to select an existing, validated
chemistry assessment. The teacher designed the learning
assessments from a bank of standardized test preparation
questions in the class textbook. We used the same 23 multiple
choice question learning assessment for the pre, post, and
retention assessments. These assessments only covered the
topics that were taught across all three study conditions. Aside
from the timing (and ordering of questions and answer choices
to avoid ordering effects), all knowledge assessments were
identical. Two example questions:
Which particle has the least mass?
a. electron c. proton
b. neutron d. all have the same mass
Which is the maximum number of electrons that can occupy a
3s orbital?
a. 1 c. 6
b. 2 d. 10
We used pre-existing chemistry attitude assessments [5]. The
chemistry assessment included eight questions on a seven-point
Likert-like scale from negative (-3), neutral (0), and positive
(+3) value choices. Three example questions:
Chemistry is... {hard(-3)...neutral(0)...easy(+3)}
{frustrating(-3)...neutral(0)...satisfying(+3)}
{complicated(-3)...neutral(0)...simple(+3)}
RESULTS
We provide quantitative results comparing the learning
outcomes from our three groups. Throughout this analysis,
we use nonparametric Chi-Squared and Wilcoxon rank sums
tests with
α=0.05
confidence, as our data were not normally
distributed. For post-hoc analyses, we use the Bonferroni
correction for three comparisons: (α/3=0.0167).
Fig. 4. A representation of the VR app covering atom configuration.
VR Students Initially Have Highly Positive Attitudes
We compared the students’ self-reported rating about their
attitudes toward chemistry prior to the beginning of the study.
Each students’ attitude score was calculated as the sum of
their responses to the 8 attitude questions. Examining the
students’ pre-test attitude scores reveal that there is a significant
difference in chemistry attitudes between conditions (
χ2(2,N=
86) = 7.3517,p< .05
). Post-hoc analysis with Bonferroni
correction revealed that the VR vs. control conditions (
W=
10.0858,Z=2.47398,p< .05/3
) were significantly different
(with the VR group students scoring higher). However, compar-
ing scores within the post-test (
χ2(2,N=86) = 3.6609,n.s.
)
and the retention-test (
χ2(2,N=86) = 2.7779,n.s.
) did not
show a significant difference between groups.
Next, we compared students’ chemistry attitudes between
assessments, to see if there were changes in attitudes over the
course of the study, by condition. Comparing the difference
between students’ post-test and pre-test attitude scores across
the conditions did not reveal statistically detectable differences
in chemistry attitudes between conditions (
χ2(2,N=86) =
5.5220,n.s.
). We also did not find any statistically detectable
differences in chemistry attitudes between students retention-
test vs. pre-test scores (
χ2(2,N=86) = 5.9434,n.s.
) or
retention-test vs. post-test scores (
χ2(2,N=86) = 2.4132,n.s.
).
No Differences in Learning Assessments
Overall, participants did poorly on the pre-test exams, with
a median score of 5 out of 23 questions correct (21.7%) across
all three conditions. This was expected, as the assessment
tested students’ chemistry knowledge before they started
their first chemistry class. We compared the pre-test scores
across the conditions and found no significant difference
(
χ2(2,N=86) = 0.5126,n.s.
), confirming that all of our
participants’ chemistry knowledge was roughly equivalent prior
to the learning activities.
Although the median scores rose slightly (indicating learn-
ing), students also did poorly overall on the post-tests, with
Fig. 5. Boxplot of students’ final exam scores, by condition.
the highest median score among the conditions (which was
the VR group) being 7 out of 23 questions correct (30.4%).
Comparing the post-test scores (
χ2(2,N=86) = 3.1695,n.s.
)
and the retention-test scores (
χ2(2,N=86) = 0.9533,n.s.
)
across the conditions do not reveal that there any significant
differences between conditions.
Next, we compared students’ chemistry knowledge between
assessments, to see if there were changes in knowledge scores
(i.e., learning) over the course of the study, by condition.
First we compared the difference between the post-test and
pre-test attitude scores. Comparing the difference between
students’ post-test and pre-test scores across the conditions
did not reveal statistically detectable differences in chemistry
knowledge between conditions (
χ2(2,N=86) = 3.2252,n.s.
).
We also did not find any statistically detectable differences in
chemistry knowledge between students retention-test vs. pre-
test scores (
χ2(2,N=86) = 5.9434,n.s.
) or retention-test vs.
post-test scores (χ2(2,N=86) = 2.4132,n.s.).
VR Students Score Higher in Final Exams & Class Grades
Finally, we compared students’ exam scores and final grades,
by condition. First, comparing their final exam scores (see Fig-
ure 5) revealed that there is a significant difference in final exam
scores between conditions (
χ2(2,N=83) = 8.3005,p< .05
).
Post-hoc analysis with Bonferroni correction revealed the VR
vs. control condition (
W=11.80556,Z=2.568715,p< .05/3
)
pair was significantly different (with the VR group scoring
higher), from comparisons of the other pairs. Next, comparing
the students’ final grades (see Figure 6) revealed that there is a
significant difference in chemistry attitudes between conditions
(
χ2(2,N=85) = 15.313,p< .05
). Post-hoc analysis revealed
the VR vs. control condition (
W=8.7094,Z=1.9696,p< .05
)
pair was significantly different (with the VR group scoring
higher), from comparisons of the other pairs.
Fig. 6. Boxplot of students’ class grades, by condition.
DISCUSSION
Attitude Assessment Interpretation & Observations
Our analysis of chemistry attitudes revealed that the VR
group participants reported significantly higher attitudes than
their control group counterparts in the pre-assessment. This is
likely due to the fact that the chemistry teacher erroneously
informed subjects which technology they would be using prior
to the study beginning. It is interesting to report that students
(especially those in the VR group) were so excited to try out
new technologies that it had an impact on their perception of
chemistry. However, As we saw in our study results, it may
be difficult to maintain this novelty effect, especially when
the content might not live up to users’ expectations. For our
study, the content was closely tied to the medium (i.e., a game
for MB, or an application for VR), so we are unable to say
conclusively that either the medium or the content, individually,
was responsible for our results.
Based on informal student feedback, VR group students
exhibited initial excitement with the new technology which
decreased over time as their expectations adjusted to the existing
content. It seems there was audience misalignment in the VR
software, as students mentioned that they could not connect
or relate with the British narrator, who starkly contrasted the
learner demographics. As with all software, the design of
educational technology cannot serve all user needs perfectly.
We observed advantages and disadvantages to each educational
technology platform in the study and how its content was
designed. For example, we overheard the MB group students
saying that the game was difficult to play at first, but once
they got used to the interface, it was fun for them to race
other students to complete the levels. Students in the VR group
talked about it being interesting to zoom down to the size of
atoms, but that the application lacked the level of interactively
they were accustomed to with video games.
We observed a mixed reaction toward educational technolo-
gies from the teachers during conversations in the teacher break
room. Most teachers had a reluctant optimism as they have
experienced instances of technology improving an aspect of
classroom management, but also poor implementations increas-
ing their workload. Before starting this study, the chemistry
teacher had reservations about using VR in the classroom
since it is was a largely untested educational technology.
He openly talked about his hesitance with new educational
technologies since previous technology implementations from
his administration has resulted in extra work for him. He
also referenced the usefulness of the math teacher’s class
management software and wished that a similar software
product existed for chemistry education. Over the course
of the study, his attitude become more positive towards
educational technology and he excitedly discussed ideas about
implementing virtual labs to mitigate physical costs, and
including MB chemistry gaming time in his regular class.
The experience of this study showed that an effective way
to incorporate educational technology into the classroom is to
let the teacher experiment with the technology and find their
own use cases as the class progresses through the learning
material. Once the teacher became more experienced with the
technology later in the study, we found that he was much
better able to integrate the supplemental learning time with his
lectures. As he discussed the coming week’s agenda with his
teaching aid, he would pause in his lesson description and used
the educational technologies to visualize the content to the
teaching aid. The teacher commented that every class will be
different so effective technology will need to be highly flexible
to teachers’ needs. Even in the four chemistry classes with
the same teacher, we observed differences in progress through
curriculum, class management issues, teaching techniques, and
student attitudes.
Learning Assessment Interpretation & Observations
Our analysis revealed no significant differences when com-
paring students’ learning assessment sums and differences.
The low scores on assessments and teacher feedback indicated
issues with voluntary non-graded quizzes. Future studies will
integrate the assessments as graded class assignments so
students will be incentivized to take them seriously. Learning
metrics will be improved by integrating assessments directly
into the educational technology. Future work will explore how
to best integrate assessments into VR, as research in MB games
have shown conflicting results [27], [36].
Both grade items sent from the teacher—the final exam
and final class grades—showed the VR group participants did
score significantly higher than their control group counterparts.
The final exam included a significant number of questions
covering the topics taught in the study conditions. Unlike the
voluntary knowledge assessments, where all students did poorly,
many students performed well in the final exam (see Figure 5),
suggesting that students took this exam more seriously. This
also suggests that those in the VR condition retained the
information they learned during their VR sessions better than
those of the other groups.
Our discussions with students indicated that VR improved
their chemistry contextualization issues. VR students reported
that they were able to understand the chemistry concepts better
by visualizing the microscopic chemical interactions. VR and
MB students indicated an effective activity in their respective
applications was the sandbox mode of freely being able to
build atoms and molecules after a lecture to understand the
lesson material better. Another contextualization issue came
up in conversations with students, teachers, and administrators,
that while not connected to this research, still warrants a brief
note. There is a consistent misinterpretation from students of
what scientists and engineers are and what they do for society
since the students do not have to opportunity to learn about
STEM careers from members of their community. This gives
the impression to the students that this knowledge does not have
worth to them and further compounds the contextualization
issues. Administrators and teachers point toward using video
recordings and in-person visits from STEM professionals to
correct these assumptions.
Finally, the final grades—where VR group participants did
significantly better than those in the control group—are likely
reflective of the final exam grades, as final exam scores make up
a significant part of the class grade. Although our experimental
design cannot reveal the specific reason why VR group students
did better than their other classmates, we will explore this in
future work.
Study Limitations & Future Work
As the first major use of education technology in this
classroom, it was a success for the researchers, teacher, and
students. The data that was collected from this research is viable
to report on, yet confounding variables restrict explanation
reporting of the data. Educational technology research operates
within the classroom environment and is impacted by the
variables of that operating condition. Despite this limitation,
data trends in the study show a positive academic impact with
the educational technologies and call for further classroom
experiments to validate these claims.
Future research will seek to evaluate and isolate some of
the confounding variables. The observer effect, which is well
known in educational technology research, cannot be verified
in the research even though there is anecdotal evidence to
support its existence. Future studies will be conducted without
researchers in the classroom to remove the observer effect on
the subjects. The participating teacher will be trained to use
the technologies effectively and integrating the training of the
students into their curriculum. The study will separate the four
classes completely into experimental and control groups to
verify the educational technologies influence on the students’
attitudes and comprehension of the class materials.
It is important to recognize that the time limitations on
students within a classroom complicate data collection with
established methods such as interviews and focus groups. In
the future, we plan to develop metrics to analyze student
perspectives in a time-efficient manner. These will be necessary
to develop a detailed understanding of the student needs
on which effective educational technologies can be built.
Observations of the different platforms and content deliveries
of the educational technologies used in this study confirmed
the need for further user analysis to increase the effectiveness
of integrating the technology into the teacher’s curriculum.
While we did observe an effect on the technologies, we cannot
distinguish the impact of the platform separate from the content.
If we were to run the study again, we would need to define
custom-built or modify existing technologies that could leverage
the same content in all experimental technology platforms to
isolate variables relating to platform and content.
CONCLUSION
This research compares MB and VR educational technologies
as learning environments, specifically comprehension and re-
tention of chemistry topics, to traditional educational mediums
of textbook, lecture, and physical lab work. We specifically
focused on chemistry education, as the subject matter is well-
suited for visually enhanced explanations to support the learning
of abstract concepts. It is vital to understand the scope of these
research questions to enable further clarification of the problem
space within the classroom.
Ultimately, this research project serves as a foundation to
determine whether educational technologies have the potential
to engage high school students in their STEM classes. This
high-level view of technology in the classroom enables us to
see if there is viability to continue this stream of research. Even
with this level of confounding variables in the classroom, we
were able to see changes to students’ academic performances.
It is worthwhile to spend effort in isolating factors in future
classroom experiments with the end result of confirming data
which is vital for education research.
ACKNOW LE DG ME NT
We thank the Elizabeth School System and Oculus Education
for their support of this research. Any opinions, findings,
conclusions, or recommendations are those of the authors and
may not reflect the views of either of these parties.
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