Effects of an Augmented Reality-Based Chemistry Experiential Application on Student Knowledge Gains, Learning Motivation, and Technology Perception

Abstract

The microscopic composition of substances is a piece of essential but abstract knowledge in chemistry. Junior high school students may experience difficulty in mental representation when learning micro concepts, which leads to problems such as unsatisfactory academic performance and low learning motivation. Augmented reality (AR) is an optimal choice for presenting abstract concepts and invisible phenomena. Consequently, this study developed an AR application including three-layer experiential learning activities and integrating multiple external representations (text, pictures, 3D models, operations, etc.). To assess the effect of the AR application on students’ knowledge gains, learning motivation, and technology perception, an experiment was conducted with 95 ninth-grade students aged 13–15 years who were randomly assigned to two groups (AR and non-AR). The results show that the AR application helped increase students’ knowledge gains. Although there was no significant difference in the retention test between the two groups, scores on the transfer test were significantly higher for the AR group than for the non-AR group. Moreover, the AR application significantly improved students’ motivation to learn. Finally, students had a positive perception of AR technology.

Full text

Vol.:(0123456789)
1 3
Journal of Science Education and Technology (2023) 32:153–167
https://doi.org/10.1007/s10956-022-10014-z
Effects ofanAugmented Reality‑Based Chemistry Experiential
Application onStudent Knowledge Gains, Learning Motivation,
andTechnology Perception
QingtangLiu1,2· JingjingMa1,2 · ShufanYu1,2· QiyunWang3· SuxiaoXu4
Accepted: 25 November 2022 / Published online: 23 December 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022
Abstract
The microscopic composition of substances is a piece of essential but abstract knowledge in chemistry. Junior high school
students may experience difficulty in mental representation when learning micro concepts, which leads to problems such as
unsatisfactory academic performance and low learning motivation. Augmented reality (AR) is an optimal choice for present-
ing abstract concepts and invisible phenomena. Consequently, this study developed an AR application including three-layer
experiential learning activities and integrating multiple external representations (text, pictures, 3D models, operations, etc.).
To assess the effect of the AR application on students’ knowledge gains, learning motivation, and technology perception, an
experiment was conducted with 95 ninth-grade students aged 13–15years who were randomly assigned to two groups (AR
and non-AR). The results show that the AR application helped increase students’ knowledge gains. Although there was no
significant difference in the retention test between the two groups, scores on the transfer test were significantly higher for the
AR group than for the non-AR group. Moreover, the AR application significantly improved students’ motivation to learn.
Finally, students had a positive perception of AR technology.
Keywords Augmented reality· Microscopic composition of substances· Learning motivation· Technology perception·
Chemistry education
Introduction
Chemistry is a vital science discipline involving the study of
the composition, structure, properties, and chemical reactions
of substances (Srisawasdi & Panjaburee, 2019). Chemical con-
cepts are often utilized to explain phenomena involved in daily
life, and such concepts are also closely related to other science
concepts (Özmen, 2011). However, learning chemical concepts
is not straightforward (Chen & Liu, 2020; Özmen, 2004), as
students need to establish the relationships and distinctions
between three levels of chemical representations: macroscopic,
submicroscopic, and symbolic (Johnstone, 1993). Understand-
ing submicroscopic and symbolic representations might be a
challenge for new chemistry learners since they are invisible
and abstract (Gilbert, 2009; Stieff & Wilensky, 2003; Wu etal.,
2021). In particular, when students observe a chemical equa-
tion, they may have difficulty visualizing and understanding the
particulate nature of the substances the symbols represent and
the dynamic chemical reaction phenomena involved (Treagust
etal., 2003). These challenges may lead to students’ unsatisfac-
tory academic performance and low learning motivation (Ewais
& Troyer, 2019; Fidan & Tuncel, 2019). Consequently, it is
necessary to improve the learning methods and tools used in
chemistry teaching (Cai etal., 2014; Srisawasdi & Panjaburee,
2019).
In chemistry education, external representations have
been used to promote meaningful learning and enhance
conceptual change (Özmen, 2011). Studies have indicated
* Jingjing Ma
majingjing@mails.ccnu.edu.cn
1 School ofEducational Information Technology,
Central China Normal University, Wuhan,
People’sRepublicofChina
2 Hubei Research Center forEducational Informationization,
Central China Normal University, Wuhan,
People’sRepublicofChina
3 Learning Sciences andTechnologies Academic Group,
National Institute ofEducation, Nanyang Technological
University, Singapore, Singapore
4 Hangzhou Jialvyuan Primary School, Hangzhou,
People’sRepublicofChina

154 Journal of Science Education and Technology (2023) 32:153–167
1 3
that pictures and ball-and-stick models can help students
develop accurate mental models of various chemical phe-
nomena (Levy, 2013; Plass etal., 2012). However, the
dynamic motion of an atom’s 3D structure is difficult to
capture using static representations (Bernholt etal., 2019;
McElhaney etal., 2015). Additionally, animations or videos
can show how chemical reactions change over time (Berg
etal., 2019), which may support students in connecting the
macroscopic and submicroscopic levels (Barak & Hussein-
Farraj, 2013). However, animations have the drawback in
that they cannot provide students with the opportunity to
manipulate concrete models. Recently, the development of
augmented reality (AR) technology has made interactive
simulation experiments a more promising method for chem-
istry learning (Habig, 2020; Nechypurenko etal., 2018).
Although numerous studies have examined the effective-
ness of AR on students’ learning performance (Cai etal.,
2014; Chen & Liu, 2020; Ewais & Troyer, 2019; Habig,
2020; Lee & Kellogg, 2020), studies in which AR is com-
bined with learning theory are limited. In particular, research
on using AR to help students establish connections between
different levels of chemical representations still needs to
integrate technology and learning theory more effectively
(Ainsworth, 2008; Chiu & Linn, 2014). In this study, an AR-
based experiential application—ArAtom—was developed to
teach students the microscopic composition of substances.
In addition, Kolb’s (1984) experiential learning model was
incorporated into the application and the learning activities
to enable a smooth integration of AR within the learning
process. This study aimed to investigate the effectiveness of
ArAtom on student knowledge gains, learning motivation,
and perception of technology.
Literature Review
Representation inChemistry Learning
Representation, the way knowledge is stored and presented,
can be divided into internal and external representation.
Internal representations consist of building blocks involv-
ing mental models, which constitute students’ content
knowledge of a particular topic or domain (Rau, 2017).
Johnstone (1993) proposed three levels of representation
in chemistry: macroscopic, submicroscopic, and symbolic.
Specifically, the macroscopic level involves observable
chemical phenomena such as color changes and precipi-
tate generation (Berg etal., 2019). The submicroscopic
level refers to the arrangement and motion of molecules
and atoms. Chemistry at the symbolic level is represented
by symbols, numbers, formulas, and equations (Wu etal.,
2001). The complexity of chemistry learning is attributed
to the need to understand the relationships among these
three levels of representing chemical phenomena. However,
learners have difficulties transferring knowledge from one
level to another (Bain etal., 2018). For example, some stu-
dents view H2O as a representation of one particle without
the conception of atoms or a collective entity since they
do not recognize that water is formed by the aggregation
of many water molecules (Wu & Shan, 2004). Chemistry
education researchers have noted that the causes of these
chemical misconceptions include the abstract nature of
chemical concepts, the separation between life experience
and chemical knowledge, and the lack of relevant pedagog-
ical practice skills (Sirhan, 2007; Srisawasdi & Panjaburee,
2019; Yakmaci-Guzel, 2013). Therefore, an ability to sci-
entifically represent and explain the submicroscopic-level
dynamics of a chemical system is necessary for students to
comprehend the macroscopic-level behavior of such sys-
tems and to connect the critical components of multi-level
chemistry knowledge effectively (Levy, 2013).
In past decades, researchers and science educators have
explored effective methods and pedagogies to address
students’ learning difficulties in understanding chemical
concepts. The multiple external representation (MERs)
method, in which pictures, models, animation, and vari-
ous representations are combined to illustrate chemi-
cal concepts (Gilbert, 2009; Kozma etal., 2000; Wu &
Puntambekar, 2012), is widely used in chemical teaching
and learning (Adadan, 2013; Pikoli, 2020). Gilbert etal.
(1998) proposed four modes of representations that can
be used to support the visualization of concepts: concrete
(sometimes referred to as material or physical), verbal,
symbolic, visual, and gestural representations. Accord-
ing to Ainsworth’s (1999) functional taxonomy of mul-
tiple representations, MERs may facilitate learning by
complementing information, constraining interpretations,
and constructing deeper understanding. For example, Rau
(2015), in a study involving 158 undergraduate students
in a general chemistry introductory course, found that
using virtual simulations (with multiple graphical repre-
sentations) significantly improved the students’ conceptual
understanding of the atomic structure and chemical bond-
ing concepts. In Sunyono etal. (2015)’s work, learning
with multiple representations, rather than conventional
learning, was found to be more effective in constructing
students’ mental models about understanding the concept
of atomic structure. As shown by these and other studies
(Baptista etal., 2019; Berg etal., 2019), MERs promote
learners’ comprehension of the acquired information and
their ability to transfer such knowledge effectively. From
the perspective of Mayer’s cognitive theory of multimedia
learning (Mayer, 2005), MERs provide students with both
visual and auditory stimuli, which then trigger students to
use both cognitive channels (images and language) to form
a coherent mental model of chemical concepts.

155Journal of Science Education and Technology (2023) 32:153–167
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Although MERs have the potential to support the learn-
ing process, it is likely that, without a practical design and
in unsuitable combinations, MERs may negatively affect
the learning process (De Jong etal., 1998). In this con-
text, scholars have suggested that technology support can
facilitate the learning-promoting effect of MERs (Horz &
Schnotz, 2010) by helping reduce irrelevant components of
the cognitive load when learning with MERs. In this study,
we utilized the taxonomy of MERs proposed by Lemke
(1998) and Tsui (2003), in which verbal-textual, symbolic-
mathematical, visual-graphical, and actional-operational
components are distinguished. This taxonomy contains dif-
ferent symbol systems and captures the multidimensionality
of external representations used in science that are also suit-
able for chemistry learning. Moreover, to address the issue
whereby traditional multimedia technology cannot readily
provide learners with an actional-operational representation
at the microscopic level, we considered AR with these four
components of MERs.
Augmented Reality Application inChemistry
Education
AR is a technology in which virtual elements generated
by computers, such as videos, graphics, animations, texts, or
audios, are superimposed onto real-world backdrops in real-
time (Azuma, 1997; Dunleavy etal., 2009; Wu etal., 2013).
With the increasing research on AR in education, several
meta-analysis studies on AR have been published, reporting
effect values ranging from 0.36 to 0.72 (Garzón & Acevedo,
2019; Ozdemir etal., 2018; Santos etal., 2014; Tekedere &
Göker, 2016), which indicate that AR has a positive impact
on education.
There have been several studies on the application of AR
in chemistry, especially those showing substances’ micro-
structure. For example, Zheng and Waller (2017) developed
an AR application called ChemPreview, which can manipu-
late bio-molecular structures at an atomic level. It can also
be used to interact with a protein in an intuitive way using
natural hand gestures. Likewise, Lee and Kellogg (2020)
introduced an open-source AR application called Palantir,
which visualizes the protein molecular structure and allows
the 3D model to be controlled by zooming and rotating ges-
tures on mobile device screens. Additionally, two applica-
tion prototypes were developed for university courses. Ewais
and Troyer (2019) developed an AR application that enables
students to explore different reactions with several atoms and
molecules. They mainly investigated female students’ atti-
tudes toward AR applications, but the learning effectiveness
was not examined. In addition, some studies have introduced
AR applications and applied them to practical teaching. For
instance, Chen and Liu (2020) investigated the effects of AR
combined with different approaches. The results showed that
the hands-on AR group performed significantly better on
a chemical reaction concept test and interest questionnaire
than the demonstration AR group. It was also found that AR
had a long-term retention effect on knowledge mastery. Cai
etal. (2014) developed an inquiry-based AR learning tool
for “the composition of substances”, which could promote
students’ cognitive performance and learning attitudes, as
indicated by their study findings. However, this AR tool run
on a desktop computer and did not take full advantage of
the convenience of mobile AR technology.
Overall, the findings of previous studies provide concrete
evidence for the usability of AR in chemistry subjects. AR
used in chemistry learning has two main benefits. First,
AR can help visualize atoms and molecules in the micro-
scopic world by displaying virtual elements alongside natu-
ral objects (Wu etal., 2013). Second, AR can provide an
interactive operation experience at the micro-level since AR
allows users to interact with virtual objects naturally and
obtain real-time feedback (Akçayir & Akçayir, 2017). These
benefits realize the integration of multiple representations to
present learning content from the technical level and help
students understand micro concepts.
However, some researchers have drawn attention to lim-
itations associated with AR in education. Squire and Jan
(2007) contended that without a well-designed interface
and guidance for students, AR could be too complicated
for them to use. In addition, due to some problems, such as
unresponsive touch features and inaccurate recognition in
location-based AR applications (Akçayır & Akçayır, 2017;
Cheng & Tsai, 2013; Dunleavy etal., 2009), students may
require excessive additional lecture time. Assessing its user
acceptance to apply new technology into a specific domain
is crucial to improve its quality for future use. Therefore,
one of the research objectives of this study was to evaluate
students’ perception of AR application using the technol-
ogy acceptance model developed by Davis (1985), which is
widely used to measure students’ technology perception of
the learning media (Liu etal., 2020).
Motivation inChemistry Education
Motivation is an internal condition that initiates, guides, and
sustains a goal-oriented action in pupils (Koballa & Glynn,
2013). In chemistry education, learning motivation has been
viewed as an essential factor determining the success of
chemistry learning (Barak etal., 2011; Vaino etal., 2012).
However, previous research has found that some students
experience difficulty in forming mental representations when
learning microscopic concepts, which leads to low learning
motivation (Ahmad etal., 2021; Ewais & Troyer, 2019). This
problem can be explained by the expectancy-value theory,
which considers that a student’s learning motivation is pre-
dominately determined by the expectancy of success and

156 Journal of Science Education and Technology (2023) 32:153–167
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subjective task values (Wigfield, 1994). Chemistry is often
regarded as a challenging and complex subject for the for-
mer. The invisible and abstract nature of chemical concepts
sometimes leads to a lack of confidence among students in
their ability to complete relevant learning tasks. For the lat-
ter, atoms and molecules are chemical concepts that require
understanding at a microscopic level, which has a less direct
connection with students’ life experiences. Therefore, stu-
dents may underestimate the value of chemistry learning.
Given that learning motivation is significant for chemis-
try learning, it is essential to design learning materials that
can arouse students’ interests and motivate them (Srisawasdi
& Panjaburee, 2019). As a novel technology, AR has been
employed in classroom teaching and effectively enhances
students’ learning motivation (Chang etal., 2019; Yu etal.,
2022). Moreover, literature has shown that the instructional
material motivation survey (IMMS) based on Keller’s ARCS
model (2010) was an effective instrument that can assess
students’ motivation in the simulation-based learning envi-
ronment by looking at four dimensions: attention, relevance,
confidence, and satisfaction. For example, Yu etal. (2022)
designed an AR learning tool named “MagAR” to assist
students’ magnetism learning by visualizing the magnetic
induction line. IMMS was applied to investigate how AR
may affect students’ learning motivation with different lev-
els of learning anxiety. The results indicated that AR could
significantly motivate students with high anxiety. Likewise,
in Chang etal. (2019)’s work, AR was utilized to promote
motor skills learning. Students reported significantly greater
attention, relevance, and confidence when compared to those
assigned video materials. In this regard, we also aimed to
evaluate the effect of AR on students’ motivation through
the lens of the ARCS model.
The Aim oftheStudy
In this study, an AR-based experiential learning application
for the microscopic composition of substances was devel-
oped, and an experiment was conducted to verify its educa-
tional efficacy. First, we considered whether this application
could improve students’ knowledge gains (RQ1). Second,
given that students have low motivation in chemistry learn-
ing, especially in microstructure learning, we considered
whether this application could improve students’ learning
motivation (RQ2). Finally, as many studies imply (Akçayır
& Akçayır, 2017; Cheng & Tsai, 2013; Dunleavy etal.,
2009), technical usability is one of the limitations of AR
applications. We considered students’ perceptions of the AR
application (RQ3). This study aimed to answer the following
questions.
RQ1. How does the AR experience influence students’
understanding of chemical knowledge?
RQ2. How does the AR experience influence students’
learning motivation in chemistry?
RQ3. What are the students’ perceptions of the AR-based
experiential learning tool?
Methods
This study aims to take unique advantages of AR to address
the learning challenges of microscopic representations in
secondary school chemistry, and to examine the effects
of AR learning application on students’ knowledge gains,
learningmotivation, and technology perception through
experimental research. First, considering that previous
studies have highlighted the significance of integrating AR
with learning theory, this study took the experiential learn-
ing model as the theoretical basis, which is proven to be
an effective framework for understanding student contex-
tual learning processes. Second, we combed the knowledge
points about “the microscopic composition of substances”
from the Chinese ninth-grade chemistry textbook to form
the instructional content framework in this study. Third,
based on the experiential learning model and learning con-
tent framework, we developed an AR learning application
that includes three layers of experiential learning activities.
Finally, we conducted an experimental study in a junior high
school in southwestern China. The specific research process
is described below.
Experiential Learning Theory
Experiential learning regards learning as the process of
experience transformation and knowledge creation (Jarmon
etal., 2009). In Kolb’s (1984) experiential learning model,
individuals acquire learning experience in two ways: con-
crete experience (the specific perception of learning content
and learning environment) and abstract conceptualization
(learners’ internal explanation of concepts or description of
symbols). Furthermore, two processing methods are used in
experience transformation: reflective observation and active
application. Reflection includes the learner’s recall, atten-
tion, and evaluation of the experience and transforms this
experience into the learning process. The application tests
the concept in a new context. Experiential learning is a con-
tinuous cycle and spiral process, which is consistent with the
cognitive spiral model (Ebert, 1994).
Instructional Content Design
Figure1 shows the structure of instructional content. The
design of instructional content should establish a close con-
nection among the three levels of representations in chem-
istry: macro, micro, and symbolic. In the Chinese junior

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high school ninth-grade chemistry textbook, the chapter
on molecules and atoms includes two distinctive concepts:
“Substances are composed of microscopic particles” and
“Molecules can be divided into atoms.” The former uses
macro phenomena to initiate thinking and establish a con-
nection from the macro to the micro level. The latter focuses
on understanding substance changes from the perspective of
microscopic particles, taking specific reactions as examples
to help students initially understand the nature of chemical
reactions. Furthermore, the gain and loss of electrons inside
the atoms help students understand the changing laws of
atoms. The three parts of instructional content were closely
interlinked—accordingly, the following AR application
designs three-layer experiential learning activities based on
this instructional content structure.
AR‑Based Experiential Learning Application Design
Unity 3D was used as the development platform, and Vufo-
ria SDK was imported to realize AR functions. Versions
for Android and iOS were released, which can be installed
and run on mobile devices such as mobile phones or tab-
lets. From the content design, AR is combined with the four
stages of the experiential learning model: concrete experi-
ence, reflective observation, abstract conceptualization, and
active application. As shown in Fig.2, these four stages are
a continuous cycle forward process, including three layers
of experiential learning activities.
The First Layer: Visualizing theThree Levels ofSubstance
Composition (Macro‑Sub‑Micro‑Micro) byOperating
theSlider
Concrete Experience Seven common substances were
selected as instructional cases, including water (H2O), oxy-
gen (O2), hydrogen (H2), dry ice (CO2), alcohol (C2H5OH,
take 75% alcohol for disinfection as an example), diamond
(C), and iron (Fe). Each substance is visualized in three
states (i.e., macroscopic, sub-microscopic, and microscopic).
Taking water as an example (see Fig.3), students select the
AR marker representing liquid water and scan it with the
camera, and the macro state of liquid water will be superim-
posed on the AR marker through feature matching and three-
dimensional registration. In addition, students can manipu-
late the AR marker (e.g., rotate, move) to trigger changes
of the virtual model in real time. Next, students manipulate
the slider to switch to the microscopic composition of liquid
water. Many disordered water molecules will appear on the
screen. Subsequently, students again operate the slider to
switch to a water molecule. They will find that a water mol-
ecule is composed of two hydrogen atoms and one oxygen
atom. Meanwhile, critical information about experimental
content is presented on the screen, which helps students gain
a better understanding of the learning content.
Reflective Observation and Abstract Conceptualisation The
entire process follows the learning order from macro to
micro—known to unknown—to build the relationship
between macro substances and micro particles. Students
develop an intuitive perception of the microscopic compo-
sition of substances through concrete interactive experiences
such as manipulating the AR marker and dragging the slider.
In addition, by operating and observing seven common sub-
stances, students can conclude that substances are composed
of atoms and molecules and establish the chemical concepts
of microstructure.
Substances are composed of particles
The substance made up of molecules
The substance made up of atoms
Molecules can be divided into atoms
The microscopic essence of physical/chemical reactions
The transformation of molecules and atoms
The gains and losses of electrons in atoms
The structure of the atom
Thetransfer of electrons in atoms
Fig. 1 The structure of instructional content
RO
AC
CE
AA
Second layer
CE
RO First layer
AC
AA
CE
Thirdlayer
RO
AC Summarize
Fig. 2 The three-layer experiential learning activities (CE concrete
experience, RO reflective observation, AC abstract conceptualization,
AA active application)

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Active Application In the first layer of experiential learn-
ing, students understand that molecules and atoms are the
fundamental particles of substances. Understanding the rela-
tionship between molecules and atoms requires students to
apply the concepts acquired in the first stage to new prob-
lems through active experiments.
The Second Layer: Visualizing theRelationship
Between Molecules andAtoms andtheMicroscopic
Nature ofChemical/Physical Reactions
Concrete Experience Four kinds of reactions (i.e., water
evaporation, water electrolysis, hydrogen and oxygen igni-
tion, and carbon and oxygen ignition) were selected as
instructional cases. The purpose of this module is to enable
students to understand the essential differences between
chemical and physical reactions at a microscopic level. Tak-
ing the evaporation and electrolysis of water as an exam-
ple (see Fig.4), when students select the “heat” button, the
movement of water molecules on the screen will accelerate,
and the intervals will become larger. When students select
the “electrify” button, each water molecule decomposes
into one oxygen atom and two hydrogen atoms. Meanwhile,
oxygen and hydrogen atoms recombine to form oxygen and
hydrogen molecules.
Reflective Observation and Abstract Conceptualisa‑
tion Through the concrete experience and reflective
observation of the four experiments in the second layer,
students can summarize the essential differences between
physical and chemical reactions. More importantly, students
form a further understanding of the interconversion between
atoms and molecules.
Active Application In the second layer of experiential learn-
ing, students understand that molecules are composed of
atoms. However, answering questions such as “What is the
structure of the atom?” or “How do atoms form molecules?”
requires students to apply the knowledge acquired in the
second stage to new problems through active experiments.
The Third Layer: Visualizing theAtomic Structure
andtheElectronic Gain andLoss ofAtoms
inChemical Reactions
Concrete Experience Bohr’s atomic structure model of the
layered arrangement of electrons is displayed on the screen
(see Fig.5). Students can scale and rotate the model to
observe and calculate the number of electrons and orbits
outside the atomic nucleus. When students bring two atomic
models close to each other, they will observe the electron
transfer of the chemical reaction.
Relflective Observation and Abstract Conceptualiza‑
tion Observing the atomic structure and electron transfer
in the process of chemical reaction, the rules for the gain
Fig. 3 The first layer of experiential learning
Fig. 4 The second layer of
experiential learning

159Journal of Science Education and Technology (2023) 32:153–167
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and loss of electrons in the process of forming molecules
are summarized.
Moreover, the AR tool integrates the four types of external
representations proposed by Tsui (2003). The details are
listed in Table1.
Participants
The study was conducted in a junior high school in
southwest China. A total of 103 student volunteers aged
13–15years were randomly divided into two groups: the
AR group (n = 47, including 22 male and 25 female stu-
dents) in the AR-based experiential learning environment
and the non-AR group (n = 56, including 30 males and
26 females) in the conventional situated learning environ-
ment. After the experiment, each student received one sta-
tionery (e.g., a notebook or a pen) reward. The two groups
were taught by the same teacher, who had been teaching
chemistry for more than 5years. In addition, the students
in each group were divided into subgroups of three or
four members. In the data processing, it was found that
two students did not complete the pre-test, and six did not
complete the post-test. Therefore, the final sample of this
study consisted of 95 participants (AR group, 46; non-AR
group, 49).
Procedure
The entire experimental procedure, which lasted 3weeks, is
shown in Fig.6. In the first week, before the class started, all
students were asked to complete a pre-test measuring their
knowledge of “The microscopic composition of substance”
(15min). It is worth noting that to avoid cognitive load caused
by the unfamiliar use of AR tools (Dunleavy etal., 2009), we
conducted a pre-experience session to familiarize students in
the experimental group with the operation process of AR tools
(15min). Then, experimental and control groups studied the
first part of the content (i.e., substances are composed of parti-
cles) in different environments. In the second and third weeks,
the two groups learned the second (molecules can be divided
into atoms) and third topics (the gain and loss of electrons in
atoms), respectively. The learning process lasted 45min for
each of the three parts. Figure7 shows the learning situations
of the two groups. In order to ensure the validity of this experi-
ment, we tried to eliminate the influence of irrelevant variables
as much as possible; that was, the instructor, learning progress,
and learning content of the two groups were the same. The only
difference was that the learning materials of the two groups
were presented in different ways, which ensures that the dif-
ference in the post-test between the two groups is caused by
whether ArAtom is used or not. Specifically, ArAtom includes
a combination of various external representations, such as texts,
animations, pictures, and 3D models. More importantly, students
can manipulate AR markers and click on experimental condi-
tions to trigger chemical or physical reactions, thus enabling
students to participate in inquiry activities. In contrast, in the
non-AR group, students learned chemical concepts mainly
through text and pictures in the textbook and on slides. Finally, a
post-test was conducted to examine students’ mastery of knowl-
edge, learning motivation, and perception of the AR tool after
completing the learning task in the third week (30min).
Measuring Tools
Testing Student Knowledge of“The Microscopic
Composition ofSubstance”
The questions in the knowledge tests were designed by two
middle school chemistry teachers with more than 10years of
Fig. 5 Bohr’s atomic structure model of oxygen atom
Table 1 The multiple external
representations designed in this
study
External representations Realization form in this research
Verbal-textual AR markers present the name of particles, relative atomic/molecular
mass, and other textual information
Symbolic-mathematical 3D models display the structure of particles
Visual-graphical Virtual animations display the process of water electrolysis and heating
Actional-operational Slider controls the state of the particle
AR marker can be manipulated to transform molecules and atoms

160 Journal of Science Education and Technology (2023) 32:153–167
1 3
teaching experience. There were ten questions in the pre-test
for a maximum possible score of 10 points to examine the
difference in prior knowledge between the two groups. The
purpose of the post-test was to assess students’ mastery of
knowledge after studying in different conditions. It included
three types of questions, which involved multiple-choice (11
items, 1 point each), fill-in-the-blank (11 blanks, 1 point
each), and short answers (2 items, 2 points each), with a
maximum possible score of 26 points. The post-test was
divided into retention (16 points) and transfer (10 points)
performance. Following Mayer (2005), this study defined
retention as demonstrating a recollection of taught infor-
mation. The transfer was defined as the ability to under-
stand taught information and apply it in new settings. For
instance, one retention test item is: “Water is composed of
_____and diamond is composed of _____?” while an exam-
ple of a transfer question is “Why are wet clothes easier
to dry in summer than winter?” The specific questions are
shown in the Appendix. The KR20 (Kuder & Richardson,
1937) values of the pre-and post-tests were 0.79 and 0.83,
Fig. 6 Experimental procedure Pre-test (‘Moleculesand Atoms’ knowledgetest)
Theexperimental group
(N=47)
Thecontrol group
(N=56)
AR based
experiential learrning
theconventional
situated learning
Module1:Substances arecomposedofparticles
Module2:Molecules can be dividedintoatoms
Module3:The gainsand losses of electronsinatoms
Post-test: theknowledgetestof'Moleculesand Atoms'
Questionnaireonlearningmotivation
Questionnaireonstudents' perception of AR technology
Week 1
Week 2
Week 3
Fig. 7 The learning situations
of the two groups (a students
in the non-AR group learned
through the traditional teaching
method; b students in the AR
group learned with the ArAtom;
c the learning materials used
in the non-AR group; d the
learning materials used in the
AR group)

161Journal of Science Education and Technology (2023) 32:153–167
1 3
respectively, indicating high reliability of the knowledge
tests.
Learning Motivation
The learning motivation questionnaire was adapted from
Keller’s (1983) IMMS (Instructional Material Motivation
Scale) scale based on the ARCS (attention, relevance, con-
fidence, satisfaction) model to evaluate students’ learning
motivation after the learning activities. There are 20 items
across four dimensions: attention, relevance, confidence,
and satisfaction. Each dimension contains five questions.
The questionnaire uses a five-point Likert scale (1 = strongly
disagree; 5 = strongly ag ree).
Technology Perception Questionnaire
The technology perception questionnaire was adapted from
Davis (1985). In this study, the questionnaire was used to
examine students’ perception of AR technology in the exper-
imental group along three dimensions: perceived usefulness
(PU), perceived ease of use (PEOU), and use intention (UI),
with a total of 10 questions. The questionnaire uses a five-
point Likert scale (1 = strongly disagree; 5 = strongly agree).
Data Analysis
Since the sample size of this study is small, the Shapiro–Wilk
test was performed and showed that all of the data sets had a
normal distribution (pre-test p > 0.05; post-test p > 0.05; lear n-
ing motivation p > 0.05). Consequently, the independent samples
t-tests were conducted to investigate the effect of AR on knowl-
edge gains and learning motivation.
Results
Analysis ofLearning Outcomes
The descriptive statistics of the pre-test are shown in Table2.
The results revealed that there was no significant difference
in chemistry knowledge between the two groups before the
experiment (t = 1.271, p > 0.05).
As presented in Table3, the post-test results were
divided into three parts: total grades (TG), retention per-
formance (RP), and transfer performance (TP). The means
of the AR group (TG 20.67; MP 13.54; RP 7.13) for each
dimension were higher than those of the non-AR group (TG
17.9; MP 12.88; RP 5). Furthermore, independent sample
t-tests were conducted to examine the differences between
the two groups. Specifically, the TG (t = − 3.65, p < 0.05,
d = 0.75) and TP (t = − 4.56, p < 0.05, d = 0.30) scores of the
experimental group were significantly higher than those of
the control group, whereas the difference in terms of reten-
tion performance was not statistically significant at the 5%
level (t = − 1.44, p = 0.152, d = 0.94).
In summary, the chemistry knowledge levels of the two
groups improved after the experimental intervention. Stu-
dents in the AR group had significantly higher total grades
and transfer performance than those in the non-AR group.
However, retention performance was not significantly differ-
ent between the two groups, indicating that the AR experi-
mental intervention did not have a distinct effect on retention
performance compared with the traditional experimental
intervention.
Analysis ofLearning Motivation
As presented in Table4, Cronbach’s α value for the entire
questionnaire was 0.965, and that for each dimension was
above 0.800, indicating that the questionnaire was reliable.
To further examine the impact of the AR-based experien-
tial learning environment on students’ learning motivation,
an independent sample t-test was conducted (Table5). The
learning motivation questionnaire had four dimensions:
attention, relevance, confidence, and satisfaction.
In terms of attention, the AR group showed a signifi-
cant difference with the non-AR group (mean AR = 4.39,
non-AR = 3.11, t = − 12.28, p < 0.05, d = 2.56). The results
demonstrate that AR technology used in class can arouse
students’ interest and attract students’ attention better than
the traditional learning method. Comparing the relevance
results, the AR group again had significantly higher scores
than the non-AR group (mean AR = 4.20, non-AR = 3.59,
t = − 5.65, p < 0.05, d = 1.16). This result indicates that
using AR to present learning materials could help stu-
dents establish connections between knowledge points.
Regarding confidence, there was a significant difference
between the two groups (mean AR = 4.24, non-AR = 3.37,
t = − 7.31, p < 0.05, d = 1.51), which indicates that AR
can help students build up their confidence in learning,
especially for abstract micro concepts. For satisfaction, a
significant difference was found between the two groups
(mean AR = 4.26, non-AR = 3.41, t = − 7.5, p < 0.05,
d = 1.54), which reveals that the students in the AR envi-
ronment were more satisfied than those in the traditional
learning environment.
Table 2 Students’ pre-test scores and independent samples t-test
results
Group NMean SD t p
Control group 49 7.11 1.45 1.27 0.140
Experimental group 46 7.09 1.24

162 Journal of Science Education and Technology (2023) 32:153–167
1 3
In summary, AR technology was conducive to improving
students’ learning motivation. All four dimensions showed
statistically significant differences between the AR and the
non-AR group.
Analysis ofTechnology Perception
As shown in Table6, Cronbach’s α value for the entire
questionnaire was 0.967, and the three dimensions were
0.927, 0.959, and 0.890, respectively; this suggests that
the questionnaire was reliable. The technology percep-
tion questionnaire was used to measure the perceptions
of the AR group students toward the AR application.
The results are listed in Table7. The scores of PEOU
(mean = 4.58, SD = 0.50), PU (mean = 4.53, SD = 0.53),
and UI (mean = 4.54, SD = 0.56) were higher than 4 (the
maximum possible score was 5), implying that students
had a high level of acceptance of the AR application.
Discussion
In this study, an AR-based experiential learning applica-
tion covering the topic “The microscopic composition of
substance” was developed to facilitate students’ experien-
tial learning in a chemistry course. To assess the effects of
the implemented approach on learning, we experimented
in a junior high school.
RQ1. How does the AR experience influence students’
understanding of chemical knowledge?
First, the students in the AR group demonstrated sig-
nificantly better knowledge gains than those in the non-
AR group. This finding is consistent with previous studies
(Akçayır & Akçayır, 2017; Cheng & Tsai, 2013), which con-
sistently conclude that AR applications increase knowledge
gains when compared to traditional approaches. In this study,
the AR application integrates multiple representations (e.g.,
chemical symbols and relative atomic mass are presented
in AR markers; 3D models display their composition and
structure, and animations reflect the state in the reaction),
which provide complementary information for students to
develop an in-depth understanding by integrating them into
a coherent mental model of the content (Ainsworth, 2014).
Interestingly, we also found that students could gradually
build the spatial imagination of the micro-world through
three layers of experiential learning activities. For exam-
ple, when the teacher asked students how to explain that a
shrunken ping-pong ball would expand again when it was in
hot water, students in the AR group replied that they could
imagine the movement of air molecules, and the interval
between them increased. This finding shows that the AR
application can help students build spatial imagination and
break through the representation dilemma of the micro-
world (Rau & Matthews, 2017).
Additionally, there was no significant difference in the
retention performance between the two groups, contrary to
some previous studies (Estapa & Nadolny, 2015; Lai etal.,
2019). This result indicates that both AR and traditional
paper-based learning materials are effective in facilitating
students’ memory of conceptual knowledge. According to
the multimedia learning theory, the best way to help students
remember concepts is to have learning materials presented
integrating words and pictures (Mayer, 2005). In our study,
the ArAtom and the traditional paper-based learning mate-
rials are similar in their function to present text and image
information simultaneously, which is the possible reason for
the no significant differences in the retention performance.
A similar conclusion was also found in Weng etal. (2019)’s
study; that is, AR technology did not have a significant effect
on students’ remembering and understanding levels, which
are basic cognitive levels in Bloom’s taxonomy. In addition,
students in the AR group did not achieve better retention
performance than students in the traditional paper-based
group as expected, possibly because students in the AR
group were overly focused on the AR technology itself and
ignored the key conceptual knowledge in the AR system.
Table 3 Students’ post-test
scores and independent sample
t-test results
Dimension Group NMean SD t p Cohen’s d
Total grades Control group 49 17.90 4.29 − 3.65 0.000 0.75
Experimental group 46 20.67 3.02
Retention performance Control group 49 12.88 2.43 − 1.44 0.152 0.30
Experimental group 46 13.54 2.01
Transfer performance Control group 49 5.00 2.47 − 4.56 0.000 0.94
Experimental group 46 7.13 2.04
Table 4 The learning motivation questionnaire
Dimension Items Cronbach’s α
Attention Q1, Q6, Q11, Q15, Q20 0.888
Relevance Q3, Q7, Q13, Q16, Q18 0.841
Confidence Q2, Q4, Q9, Q12, Q19 0.886
Satisfaction Q5, Q8, Q10, Q14, Q17 0.869
ARCS 20 0.965

163Journal of Science Education and Technology (2023) 32:153–167
1 3
This issue has also been mentioned in the Erbas and Demirer
(2019)’s study.
Moreover, there was a significant difference in the trans-
fer performance between the two groups, which revealed
that the AR application could help students understand and
transfer knowledge. In terms of the technical advantages
of AR, the AR application developed in this study simu-
lates real-world substances through virtual objects, which
enhances students’ ability to relate the acquired knowledge
to the real-world environment. Another important reason for
the realization of knowledge transfer is the use of this study’s
three-layer experiential learning circle. In each layer of expe-
riential learning, learners have experienced the process of
learning and applying new knowledge so that students can
realize the application and transfer of knowledge step by
step. Previous studies such as those of Manolis etal. (2013)
and Lai etal. (2007) also show that experiential learning
offers students the opportunity to utilize knowledge in new
situations.
RQ2. How does the AR experience influence students’
learning motivation in chemistry?
The results demonstrated that the AR group had higher
learning motivation than the non-AR group. This finding
is in line with the reviews of Radu (2012) and Akçayir and
Akçayir (2017), which indicate that AR can enhance learn-
ing motivation and positive attitudes. Specifically, the AR
application improved students’ learning motivation along
the four dimensions of attention, relevance, confidence, and
satisfaction. First, students changed from passive recipi-
ents to active knowledge explorers in the AR experiential
learning environment, where they increased their engage-
ment and attention to knowledge. This result implies that AR
can promote interaction between students and the learning
material, thus facilitating “learning by doing” (Hsiao etal.,
2012). Second, familiar substances from daily life were
selected (e.g., water and alcohol), and their molecules and
atoms were presented. Therefore, the relationship between
the micro and macro worlds was established to enhance the
relevance of the learning content for the students. This find-
ing confirms Lin etal. (2013) argument that AR is a sup-
portive instrument for constructing students’ knowledge in a
way that clarifies the relations among theoretical concepts or
principles. The non-AR group presents the learning content
in static text and pictures. For novices new to microscopic
phenomena, it is challenging to construct mental represen-
tations of molecular and atomic motions, which reduces
their confidence in learning this point. While AR technol-
ogy makes abstract content more intuitive and accessible
for students to understand, thus enhancing their confidence
and satisfaction. This finding is consistent with the findings
of previous studies that AR is ideal for explaining micro
phenomena that cannot be observed (Ibáñez etal., 2016;
Lin etal., 2013).
RQ3. What is the students’ perception of the AR-based
experiential learning tool?
This study measured three aspects of students’ percep-
tion of AR technology experience: perceived usefulness,
perceived ease of use, and future use intentions. Results
revealed that the scores of the three dimensions were all
greater than 4 (the maximum possible score was 5), and the
scores of the three items indicated that the AR application
provided students with positive experiences. This finding
corresponds with Liou etal. (2017) and Martin-Gonzalez
Table 5 Students’ learning
motivation scores and
independent samples t-test
results
Dimension Group NMean SD t p Cohen’s d
Attention Control group 49 3.11 0.47 − 12.28 0.000 2.56
Experimental group 46 4.39 0.53
Relevance Control group 49 3.59 0.50 − 5.65 0.000 1.16
Experimental group 46 4.20 0.55
Confidence Control group 49 3.37 0.59 − 7.31 0.000 1.51
Experimental group 46 4.24 0.56
Satisfaction Control group 49 3.41 0.52 − 7.50 0.000 1.54
Experimental group 46 4.26 0.58
Table 6 The technology perception questionnaire
PEOU Perceived Ease of Use, PU Perceived Usefulness, UI Use
Intention
Dimensions Items Cronbach’s α
PEOU Q1–4 0.927
PU Q5–8 0.959
UI Q9–10 0.890
Total Q1–10 0.967
Table 7 Descriptive statistics of
students’ technology perception
scores
Dimension NMean SD
PEOU 46 4.58 0.50
PU 46 4.53 0.53
UI 46 4.54 0.56

164 Journal of Science Education and Technology (2023) 32:153–167
1 3
etal. (2016). In general, no students experienced learning
difficulties due to technical usability issues, indicating that
students had a positive perception of using this AR applica-
tion. This finding implies that the AR application can be
promoted to a more extensive range of use.
Although, from the data, students have a positive per-
ception of the AR application, according to the teacher,
some students in the AR group often asked the teacher to
help solve the problems in operation, which is due to the
student’s lack of proficiency in the use of the AR applica-
tion. This finding corresponds with the research of Liu etal.
(2020), which interviewed students using AR technology.
The result shows that more time could be allocated for stu-
dents to become familiar with AR technology before the
lesson.
Conclusion andLimitations
The microscopic composition of a substance is an abstract
component of chemical knowledge. Students face difficul-
ties constructing mental representations, leading to unsat-
isfactory academic performance and low learning motiva-
tion. Therefore, this study used AR technology to integrate
multiple external representations, such as texts, images, 3D
models, and operations, to provide hands-on experience at
the micro-level. In addition, the existing studies rarely men-
tion the combination of AR with knowledge structure and
students’ cognitive development processes. Hence, this study
constructed a three-layer experiential learning model that
combines AR technology and the learning process to help
students build knowledge step by step through three-layer
learning activities.
This study has both theoretical and practical implica-
tions. The main theoretical innovation of our AR appli-
cation is the three-layer experiential learning circle con-
structed based on Kolb’s experiential learning model,
which provides a new perspective for researchers. Experi-
ential learning is a spiral process consistent with Piaget’s
view of the process of students’ cognitive development. As
the practical contribution, this study enriches the research
on the application of AR in chemistry, especially in micro-
structure teaching. The AR application developed in this
study was shown to be effective in improving students’
knowledge gains and learning motivation. In addition,
this study also found that AR has a positive impact on
the development of students’ spatial imagination, which is
crucial for chemistry learning. Moreover, AR tools can be
used in various applications, especially in underdeveloped
areas. Since AR has low requirements for equipment, only
one mobile device is needed to provide AR resources to
students in underdeveloped areas, thereby achieving a bal-
anced distribution of educational resources.
This study has some limitations and suggests new directions
for future research. First, the three-layer experience learning
model constructed in this study is based on the knowledge of the
microscopic composition of a substance. Future studies can test
whether three-layer or multi-layer experiential learning applies
to other disciplines or other types of knowledge. Second, this
study found that AR had no significant effect on retention perfor-
mance; a delay test can be conducted to re-examine the impact
of AR on student retention and transfer performance. Finally,
this study only uses quantitative data to analyze the pre- and
post-test data to test the effectiveness and usability of AR tools.
However, it is not clear how students develop chemical con-
cepts during this process, which is the limitation of this study.
We plan to explore how AR can facilitate students’ conceptual
development process based on the knowledge integration (KI)
framework (Linn, 2006) in the future.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s10956- 022- 10014-z.
Funding This work was supported by Wuhan Science and Technol-
ogy Bureau (grant number: 2020010601012190), Annual General
Project of Henan Provincial Philosophy and Social Science Planning
(2022BYJ016), Ministry of Culture and Tourism of China (grant num-
ber: 20201194075), and Fundamental Research Funds for the Central
China Normal University (grant number: 2020YBZZ037).
Data Availability Data can be accessed by sending a request e-mail to
the corresponding author.
Declarations
Ethics Approval All procedures performed in studies involving human
participants were in accordance with the ethical standards of the
Research Ethics Review Committee of Faculty of Education, Central
China Normal University.
Consent to Participate The participants were protected by hiding their
personal information during the research process.
All participants took part in the experiment voluntarily and they could
withdraw from the study at any time.
Consent for Publication Not applicable.
Conflict of Interest The authors declare no competing interests.
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