A preview of this full-text is provided by IGI Global Scientific Publishing.
Content available from International Journal of Adult Education and Technology
This content is subject to copyright. Terms and conditions apply.
DOI: 10.4018/IJAET.314607
Volume 13 • Issue 1
Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.
*Corresponding Author
1
Zhonggen Yu, Faculty of Foreign Studies, Beijing Language and Culture University, Beijing, China*
https://orcid.org/0000-0002-3873-980X
Paisan Sukjairungwattana, Faculty of Liberal Arts, Mahidol University, Nakhon Pathom, Thailand*
https://orcid.org/0000-0002-7568-9784
Wei Xu, Faculty of Humanities and Social Sciences, City University of Macau, Taipa, Macau*
https://orcid.org/0000-0002-7224-1116
Serious games have been deemed as an effective tool to engage students with various needs and
expectations. Although serious games have emerged as an important assistant in education, sparse
studies have systematically reviewed the related studies in the fields of bibliographic knowledge
structure and their effects. Through CiteSpace, VOSviewer, and content analysis, this study concludes
that: (1) Many studies have demonstrated that serious games could improve student engagement,
motivation, learning strategies, and cognition; (2) The prominent advantages of serious games may
be the enjoyment, followed by enhanced happiness, satisfaction, and positive attitude; (3) The study
also discussed recently developed serious games and their effectiveness in education. It is suggested
that future research could focus on the assessment of serious games to select the effective serious
games and improve their design.
Cognition, Engagement, Enjoyment, Learning Outcome, Motivation, Serious Game
An educational or serious game was defined as “an instructional method requiring the learner to
participate in a competitive activity with preset rules” (Akl et al., 2010). Examples are “exergaming”
game, Yourself!Fitness, and the biofeedback game (Michael & Chen, 2006). There have been
many studies committed to the exploration of student engagement, motivation, learning strategies,
cognition, and enjoyment of serious games. Most of them reported positive educational outcomes.
Volume 13 • Issue 1
2
For example, serious games have been deemed as an effective tool to engage students with various
needs and expectations (Yu, 2019). Compared with traditional pedagogy, serious game-assisted
teaching could cause higher engagement in and positive attitude toward learning (Varvara, Michail,
& Konstantinos, 2016). The level of engagement in a serious game could predict learning outcomes
(Volejnikova-Wenger et al., 2021).
Numerous studies have examined the effects of serious games in various fields. Some of them
have reported that serious games were beneficial to medical education in terms of student satisfaction,
knowledge acquisition, skill training, attitude, and learning behaviors (Akl, Pretorius, Sackett, &
Erdley, et al., 2010). Serious games could enhance the effectiveness of instruction in business institutes
and construction education (Tagliabue et al., 2021). Other studies have been committed to influencing
factors of the acceptance of serious games although the influence of the factors on student performance
has not been thoroughly explored (e.g. Giannakos, 2013; Yu & Yi, 2020).
However, it is also reported that the use of serious games has been proven either effective or
ineffective in the medical field. Little is known about the mixed effects of serious games on different
variables in various fields. More comprehensive review studies are still needed to test the effectiveness
of serious games in medical education and other fields (Akl et al., 2010). Given the contradictory
findings, it is meaningful and important to systematically review the effect of serious games in various
fields to provide a solid reference for future researchers and practitioners. This study aims to answer
several research questions as follows:
(1) What are major clusters, citation counts, burst detection, centrality, sigma, and co-occurrence
in serious game-assisted education research? Burst detection aims to identify the frequency of
words or phrases used in cited documents, and the citation frequency of the cited documents.
It may indicate the development direction of the theme. The results obtained by the sudden
increasing algorithm can identify the changing trend of the theme in time. Centrality describes
that a node establishes a bridge between two unrelated nodes, and the high centrality highlights the
importance of nodes in the structure. Sigma value is a composite of two indicators, i.e. centrality
and burst values. It is used to identify innovative literature. This indicator can be used to identify
innovative topics. Co-occurrence is a quantitative analysis method in various information carriers.
It can reveal the content correlation of information and the implied co-occurrence relationship
of featured items.
(2) How can serious games improve student engagement, motivation, learning strategies, and
cognition?
(3) How can serious games enhance enjoyment, happiness, intention to use, positive attitude, and
satisfaction of students?
(4) How can serious games improve learning outcomes, e.g. health education effectiveness,
understanding, gain and sharing of knowledge and information, decision-making, flow experience,
mental effort, and higher-order abilities?
(5) What are recently developed serious games and how about their effects?
We obtained 293 results by searching Web of Science Core Collection, including Social Sciences
Citation Index, Science Citation Index Expanded, Arts & Humanities Citation Index, Conference
Proceedings Citation Index-Science, Conference Proceedings Citation Index-Social Science &
Humanitiesand Emerging Sources Citation Index, spanning from 2016 to 2020. Boolean searches
were conducted on the term (“educat* gam*” OR “serious gam*”) AND (motivat* OR “learning
outcome*” OR satisfaction* OR performance* OR satisfaction* OR achievement* OR “cognitive
load*” OR engag* OR participat* OR accept* OR collaborat* OR enjoy*) as titles. After removing
Volume 13 • Issue 1
3
irrelevant results and those of lower quality, we finally used 279 results for further analysis through
CiteSpace 5.6.R4.
To answer the first research question, the analysis was conducted based on the proper settings
of CiteSpace. Time slicing in CiteSpace ranged from 2004 to 2020. Term sources included title,
abstract, author keywords, and keywords plus. Node types included author, source, and reference.
The link strength is analyzed via Cosine within slices. We also used VOSviewer to conduct the co-
occurrence analysis.
To answer the other research questions, we used a content analysis method, one of the methods
frequently applied to social sciences and humanities. Through content analysis, we can understand
and judge the focus, tendency, attitude, position of some issues, and the changing law of the content
of serious game-assisted research in a certain period. In the process of content analysis, we excluded
individual bias and pursue common values from the existing literature. We analyzed the literature
both quantitatively by mathematical statistics and qualitatively by logical reasoning and philosophical
thinking. The content analysis went through three stages,i.e. selection, classification, and statistics.
The following three methods were adopted: (1) recording or observing the content in a certain
period; (2) analyzing and comparing the content reported by the same literature in different periods;
(3) simultaneous interpreting and comparing the contents, methods of the same event or the same
subject reported by different authors during the same period.
This section attempts to answer research questions including major clusters, citation counts, burst
detection, centrality, sigma, and co-occurrence in serious game-assisted education research, the
effect of serious games on student engagement, motivation, learning strategies, cognition, enjoyment,
happiness, intention to use, attitude, satisfaction, and learning outcomes. We also introduced several
serious games recently developed and applied to various fields.
Cluster analysis refers to the analytical process of grouping a set of physical or abstract objects into
multiple classes composed of similar components, based on the similarity of the analyzed objects
(Yu, 2020). There are many different algorithms for clustering analysis. CiteSpace provides three
algorithms. The names of the three algorithms are Term Frequency-Inverse Document Frequency
(TF-IDF), log maximum the likelihood ratio (LLR), and mutual information (MI). For different data,
the three algorithms have the same performance and can be used in practice.
TF-IDF. Term frequency (TF) refers to the number of times a given word appears in the file.
This number is usually normalized (usually word frequency divided by the total number of words in
the article) to prevent it from leaning towards long files. The same word may have a higher frequency
in a long document than in a short document, regardless of its importance or not.
The main idea of inverse document frequency (IDF) is that if fewer documents contain a certain
entry and the IDF is larger, it means that the entry has a good capacity to distinguish categories. The
IDF of a specific word can be obtained by dividing the total number of files by the number of files
containing the word, and then taking the quotient logarithm.
LLR. LLR is a kind of index revealing authenticity, which belongs to a composite index reflecting
both sensitivity and specificity. This index fully reflects the diagnostic value of screening tests and
is very stable. The calculation of the likelihood ratio only involves sensitivity and specificity, and is
not affected by prevalence. Due to the positive and negative test results, the likelihood ratio can be
divided into the positive likelihood ratio (+LLR) and the negative likelihood ratio (-LLR).
MI. In probability theory and Information theory, Mutual Information (MI) or trans-information
of two random variables is a measure of the interdependence between variables. Unlike correlation
coefficients, mutual information is not limited to real-value random variables. It is more general and
Volume 13 • Issue 1
4
determines the degree of similarity between the joint distribution p (X, Y) and the product p (X) p
(Y) of the decomposed edge distribution. Mutual Information is a measure of mutual dependence
between two sets of events.
The network is divided into 10 co-citation clusters. These clusters are labeled by category
terms from their citers. The largest 4 clusters are summarized based on TF-IDF, LLR, and MI. The
largest cluster (#0) has 62 members and a silhouette value of 0.647. It is labeled as user experience
by LLR, serious games by TF-IDF, and other members by MI. The most active citer to the cluster
is the article authored by Wang, Rajan, Sankar, & Raju (2017), who found that game players would
experience higher concentration and enjoyment if they predicted obvious goals, ease of use of games,
and usefulness of games. This provided a reference for game designers and practitioners.
The second largest cluster (#1) has 47 members and a silhouette value of 0.881. It is labeled as
using mobile serious game by LLR, students by TF-IDF, and many other members by MI. The most
active citer to the cluster is the research conducted by Habgood, & Ainsworth (2011), who revealed
that the intrinsic elements in a serious game could strongly attract children’s attention within a limited
seven-day period. This suggests that game designers and teachers could manage to integrate intrinsic
motivation into serious games so that the effectiveness of serious games could be improved.
The third largest cluster (#2) has 40 members and a silhouette value of 0.859. It is labeled as
motivational effect by LLR, serious games by TF-IDF, and L2 vocabulary acquisition (0.4); and
learning gain (0.4), etc. by MI. The most active citer to the cluster is the study conducted by Wouters,
van Nimwegen, van Oostendorp, & van der Spek (2013), who assumed that serious games could
influence learning effectiveness by moderating cognition and motivation. Compared with learners
without the aid of serious games, those with them could learn more knowledge in groups more
effectively (Wouters et al., 2013).
The fourth largest cluster (#3) has 32 members and a silhouette value of 0.954. It is labeled as
literature review by LLR, serious games by TF-IDF, and transboundary watershed management (0.19)
and social learning, etc. by MI. The most active citer to the cluster is the article titled “On evaluating
social learning outcomes of serious games to collaboratively address sustainability problems: a
literature review” authored by Haan, & Mascha (2018), who argued that serious games could be
deemed as collaborative tools to improve social learning. The study focuses on the evaluation of
serious game-assisted social learning, e.g. methods and procedures, which could provide constructive
suggestions for game designers, knowledge transmitters, and learners.
The top-ranked item by citation counts is Connolly, Boyle, MacArthur, Hainey, & Boyle (2012) in
Cluster #6, with citation counts of 23. The second one is Deterding, Dixon, Khaled, & Nacke (2011)
in Cluster #7, with citation counts of 10. The third is Boyle, Hainey, Connolly et al., 2016) in Cluster
#2, with citation counts of 9. The 4th is Breuer, & Bente (2010) in Cluster #8, with citation counts of
7. The 5th is Hamari, Shernoff, Rowe, Coller, & Edwards (2016) in Cluster #2, with citation counts
of 7. The 6th is Girard, Ecalle, & Magnan (2013) in Cluster #5, with citation counts of 7. The 7th is
Kebritchi, Hirumi, & Bai (2010) in Cluster #4, with citation counts of 7. The 8th is Annetta, Minogue,
Holmes, & Cheng (2009) in Cluster #6, with citation counts of 7. The 9th is Guillén-Nieto, & Aleson
(2012) in Cluster #5, with citation counts of 7. The 10th is Graafland, Schraagen, & Schijven (2012)
in Cluster #6, with citation counts of 7 (See Table 1).
CiteSpace provides the function of burst detection to detect significant changes in the number of
references in a certain period, and to find out the decline or rise of a subject word or keyword. The
top-ranked item by bursts is the work authored by Michael & Chen (2006) in Cluster #5, with bursts of
2.94, which was frequently cited by researchers and practitioners. It is a book excerpt focusing on the
educational benefits of video games among healthcare professionals. It analyzed both advantages and
Volume 13 • Issue 1
5
disadvantages of various serious games, e.g. “exergaming” game, Yourself!Fitness, and the biofeedback
game. The authors also introduced important contributions made by Dr. Mark Wiederhold, who used
video games for therapeutic interventions and to distract patients during painful medical procedures,
used simulations to improve rehabilitation, and used virtual reality to improve motor skills. Japanese
game companies facilitated the acceptance of serious games, especially in healthcare. Serious games
have also been widely used in treatment and recovery, self-management, health education and physical
fitness, and distraction therapy.
The top-ranked item by centrality is Connolly, Boyle, MacArthur, Hainey, & Boyle (2012) in Cluster
#6, with the centrality of 38. The second one is Annetta, Minogue, Holmes, & Cheng (2009) in
Cluster #6, with the centrality of 32. The 3rd is Kebritchi, Hirumi, & Bai (2010) in Cluster #4, with
the centrality of 23. The 4th is Hamari, Shernoff, Rowe, Coller, & Edwards (2016) in Cluster #2,
with the centrality of 22. The 5th is Wouters, van Nimwegen, van Oostendorp, & van der Spek (2013)
in Cluster #4, with the centrality of 22. The 6th is Michael, & Chen (2006) in Cluster #5, with the
centrality of 21. The 7th is Douven, Mul, Son, Bakker, Radosevich, & Hendriks (2014) in Cluster #4,
with the centrality of 21. The 8th is Deterding, Dixon, Khaled, & Nacke (2011) in Cluster #7, with
the centrality of 20. The 9th is Bourgonjon, De Grove, De Smet, Looy, Soetaert, & Valcke (2013) in
Cluster #7, with the centrality of 20 (See Table 2).
The top-ranked item by sigma is Connolly, Boyle, MacArthur, Hainey, & Boyle (2012) in Cluster #6,
with the sigma of 0.24. The second one is Annetta, Minogue, Holmes, & Cheng (2009) in Cluster
#6, with the sigma of 0.15. The third is Deterding, Dixon, Khaled, & Nacke (2011) in Cluster #7,
with the sigma of 0.12. The 4th is Michael, & Chen (2006) in Cluster #5, with the sigma of 0.11.
The 5th is Barab, Scott, & Siyahhan et al. (2009) in Cluster #4, with the sigma of 0.07. The 6th is
Kebritchi, Hirumi, & Bai (2010) in Cluster #4, with the sigma of 0.06. The 7th is Zyda (2005) in
Cluster #8, with the sigma of 0.06. The 8th is Gee (2003) in Cluster #8, with the sigma of 0.06. The
9th is (De Jans, Van Geit, Cauberghe, Cauberghe, & Hudders (2017) in Cluster #2, with the sigma
of 0.06 (See Table 3).
Table 1. Citation counts
Citation counts References Cluster
#
23 Connolly et al., 2012 6
10 Deterding et al., 2011 7
9 Boyle et al., 2016 2
7 Breuer & Bente, 2010 8
7 Hamari et al., 2016 2
7 Girard et al., 2013 5
7 Kebritchi et al., 2010 4
7 Annetta et al., 2009 6
7 Guillén-Nieto & Aleson, 2012 5
7 Graafland et al., 2012 6
Volume 13 • Issue 1
6
Before the co-occurrence analysis, it is necessary to understand word frequency analysis. Word
frequency refers to the number of occurrences of words in the document being analyzed. Word
frequency analysis is a method to extract the high-frequency distribution of keywords and subject
words that can express the core content of the literature to study the development trend and research
theme of this field. Based on word frequency analysis, the higher level analysis of word frequency
network is called the co-occurrence analysis. The basic principle of the co-occurrence analysis is to
count the number of times that a group of words can appear in the same group of literature in pairs,
and to measure their affinity by the number of co-occurrences.
Through the VOSviewer, we created a map (See Figure 1) based on the obtained bibliographic
data from Web of Science, the type of analysis is co-occurrence with full counting as the counting
method. The minimum number of occurrences of a keyword was 11, which led to 21 meeting the
threshold of the 1084 keywords. For each of the 21 keywords, the total strength of the co-occurrence
links with other keywords was calculated. The keywords with the greatest total link strength were
selected. The keywords were grouped into three clusters. In the first cluster, there were 10 items,
e.g. children, collaborative learning, design, education, serious games, framework, game design, and
Table 2. Centrality
Centrality References Cluster
#
38 Connolly et al., 2012 6
32 Annetta et al., 2009 6
23 Kebritchi et al., 2010 4
22 Hamari et al., 2016 2
22 Wouters et al., 2013 4
21 Michael & Chen, 2006 5
21 Douven et al., 2014 4
20 Deterding et al., 2011 7
20 Bourgonjon et al., 2013 7
Table 3. Sigma
Sigma References Cluster
#
0.24 Connolly et al., 2012 6
0.15 Annetta et al., 2009 6
0.12 Deterding et al., 2011 7
0.11 Michael & Chen, 2006 5
0.07 Barab et al., 2009 4
0.06 Kebritchi et al., 2010 4
0.06 Zyda, 2005 8
0.06 Gee, 2003 8
0.06 De Jans et al., 2017 2
Volume 13 • Issue 1
7
students, etc. The second cluster included 8 items, e.g. engagement, intrinsic motivation, motivation
and serious game, etc. The third cluster included 3 items, i.e. computer games, performance, and video
games. The top variables included engagement, motivation, learning strategies, cognition, enjoyment,
happiness, intention to use, attitude, satisfaction, learning outcomes, and recently developed serious
games. Researchers, therefore, focused on these items for in-depth exploration.
Serious games could enhance student engagement in learning, strengthen both intrinsic and extrinsic
motivations, and improve their learning strategies and cognitive abilities. Video games could enhance
the engagement of patients, and provide suggestions for developing an effective stroke rehabilitation
system. Game design principles were established for upper limb stroke rehabilitation and presented
several games (Burke, McNeill, & Charles, 2009). Constructing a serious game could improve student
motivation and deep learning strategies more than playing an existing one (Vos, Meijden, & Denessen,
2011). Intrinsic motivation played an important role in serious game-assisted learning and children
learned more from the intrinsic motivation-based games than the extrinsic motivation (Habgood, &
Ainsworth, 2011). However, external rewards could neither exert any negative influence on student
motivation, nor improve student engagement (Filsecker, & Hickey, 2014).
Serious games were directly and strongly correlated with the intention to use mobile banking
services, which could make banking business more exciting, interesting, and enjoyable, coupled
with higher levels of customers’ acceptance, engagement, and satisfaction (Baptista & Oliveira,
2017). Learners assisted with serious games could learn more than those without serious games due
Figure 1. Clustering of keywords via VOSviewer
Volume 13 • Issue 1
8
to improved cognition and motivation of learners (Wouters, van Nimwegen, van Oostendorp, & van
der Spek, 2013).
Serious games could enjoy both students and teachers, enhance the level of their happiness and
satisfaction, improve their attitudes toward learning and strengthen their intention to use serious
games in learning. Enjoyment, happiness, and intention to use could exert a great influence on
student performance by increasing knowledge gain of students, and enhancing student enjoyment in
the serious game-based context. Nevertheless, students’ intention to use serious games and happiness
were not correlated with their performance (Giannakos, 2013). Serious games could improve complex
learning outcomes and enhance student satisfaction by including scripted collaboration (Hummel,
van Houcke, & Nadolski, et al., 2010).
Serious games could fill the gap between concrete practice and abstract theory by enhancing
student motivation and pedagogical skills in a basic automatic control course (Munz, Schumm,
Wiesebrock, & Allgower, 2007). Augmented reality (AR) enables learners to associate their social
practice with their acquired knowledge. It is important to apply AR technology to their actual
observance in the world. AR-based gamified approach could improve students’ attitudes and
performance in learning activities (Hwang, Wu, Chen, & Tu, 2015).
In many fields, serious games could result in positive learning outcomes in terms of health education
effectiveness, understanding, gain and sharing of knowledge and information, decision-making, flow
experience, mental effort, and higher-order abilities. Flow experience refers to the experience of
continuous communication in learning or social interactions (Zenk et al., 2021). Mental effort aimed
to measure the degree to which cognitive control abilities influenced the learning procedures to make
sure that the procedures could fulfill certain tasks (Székely & Michael, 2021). Higher-order abilities
referred to the skills involving scientific thinking and reasoning, e.g. critical thinking, analytical
skills, problem-solution, and decision-making (Janouskova et al., 2021).
Serious games could be properly used in health education and support medical decisions,
especially for older patients. Serious games were effective as a decision assistant in localized prostate
cancer (Reichlin et al., 2011). Serious games could also virtually bring together different stakeholders
for them to equally access negotiation, to share knowledge and information, and to evaluate decision-
making outcomes (Medema et al., 2016).
Serious game-assisted learning could facilitate learners’ flow experience, whereby elementary
school students could acquire anti-phishing knowledge via learning analytics (Sun, Kuo, Hou, & Lin,
2017). Serious games could enhance public understanding and knowledge of climate change and its
adaptation by improving social learning and encouraging beneficial behaviors (Flood et al., 2018).
Serious game-assisted learning methods could increase students’ mental effort and improve their
learning outcomes. Learning via games could enhance learning effectiveness (Hawlitschek, & Joeckel,
2017). Serious games could provide students with academic contents and skills, which stimulated
enduring learning behaviors. Students could gain a larger vocabulary range in serious game-assisted
learning than traditional learning. However, many controversial issues still exist regarding the effect of
serious games on learning, especially when no pedagogical support was available (Calvo-Ferre, 2015).
The size of game grids played an important role in learning, which could be analyzed through
both task-based and tile-based action sequence coding approaches (Loh, Sheng, & Li, 2015). A serious
game could facilitate interactions between professionals and amateurs, and engage low-secure service
users with serious mental diseases in the design and refurbishment of their environment (Fitzgerald,
Kirk, & Bristow, 2011). Mobile serious games could improve higher-order abilities such as perception
of collaborative skills and problem-solving strategies (Sanchez, & Olivares, 2011).
Volume 13 • Issue 1
9
A number of serious games have recently been designed to facilitate the effectiveness of education.
With playful components such as storytelling, walking and moving, sketching, drawing, and games,
the serious game, referred to as NextCampus, could encourage public engagement in urban planning
(Poplin, 2012).
The Serious Games-Engaging Training Solutions (SG-ETS) successfully brought together
game designers and pedagogical experts in leading universities in the UK, such as the Universities
of Birmingham, London, Sheffield, and several game companies, such as Trusim, VEGA Group,
and PLC. SG-ETS, aimed to design serious games and publish research achievements in the games
(Freitas, & Jarvis, 2007).
Several types of games such as Serious Educational Games (SEG), Educational Simulations
(ES), and Serious Games (SG) could be powerful aids in science teaching and learning. SEG, SG,
and ES could greatly improve students’ cognitive gains and produce positive effects (Lamb, Annetta,
& Firestone, et al., 2018). A computer-aided serious game (eMedOffice) could improve learning
effectiveness at the RWTH Aachen University Medical School. Internet-based serious games could
thus be considered effective tools to facilitate medication learning and teaching (Hannig, Kuth, &
Oezman, et al., 2012).
A serious game named Reach Out Central (ROC) could improve the mental health and well-being
of young people by attracting, engaging, and educating them. It could also reduce young women’s
psychological stress and enhance their life satisfaction, problem-solving, and help-seeking abilities.
However, ROC could not engage young people for long. The future design could make every effort
to engage them for a longer time (Burns, Webb, Durkin, & Hickie, 2010).
Another serious game, referred to as Aqua Republica, could facilitate boundary crossing,
collaboration, and knowledge co-creation in a watershed governance context (Jean, Medema,
Adamowski, Chew, Delaney, & Wals, 2018). Mingoville, a Serious Game, could motivate Chinese
primary students to engage in an EFL classroom, which was greatly influenced by teachers’ and parents’
attitudes toward the game (Anyaegbu, Ting, & Li, 2012). The influencing factors of a serious game,
i.e. DEBORAH Game, included perceived usefulness, and interaction of students with colleagues,
but effort expectancy did not exert a significant influence in the course of accounting (Malaquias,
Fernanda, & Hwang, 2018).
By LLR, TF-IDF, and MI, the top three clusters included user experience, serious games, mobile
serious game, students, educational game, and motivational effect. Researchers tend to focus on
serious game users’ experience and their motivational factors. Designers and teachers should pay
much attention to how to improve their experience and motivation in the gameplay process or in
serious game-assisted education. As the most active citer to the first cluster, Wang, Rajan, Sankar,
& Raju (2017) argued that game players could experience higher concentration and enjoyment in the
gameplay periods. The most active citer to the second cluster (Jacob Habgood, & Ainsworth, 2011)
reported that the intrinsic motivation could draw children’s attention in gameplay. Motivation and
cognition could greatly influence the effect of serious games on education (Wouters, van Nimwegen,
van Oostendorp, & van der Spek, 2013).
Confronted with challenging, boring, and uninteresting learning materials, students tend to be distracted
by more interesting things such as games, TV programs, and stories. Serious games, by integrating
features such as interactivity, enjoyment, and motivation, engage players in learning activities by
Volume 13 • Issue 1
10
enhancing their motivation and improving their learning strategies. The frequent interaction process is
a learning period. Possibly problem solvers, serious games motivate players or learners to frequently
interact with peers and teachers, encouraging them to participate in the learning process.
Many studies have demonstrated that serious games could improve student engagement,
motivation, learning strategies, and cognition (e.g. Burke, McNeill, & Charles, 2009; Vos, Meijden,
& Denessen, 2011; Habgood, & Ainsworth, 2011). The attributes of enjoyment and entertainment
may have encouraged students to participate in education in a relaxing mood. With rewards and
coupons in the serious game, students were motivated to continue the game, by which they also felt
successful if they acquired knowledge and achieved success in education. They could also modulate
their learning strategies and enhance their cognitive abilities during gameplay. Meanwhile, they learn
and progress as the gameplay facilitates the acquisition of knowledge.
The prominent advantages of a serious game may be enjoyment, followed by enhanced happiness,
satisfaction, and positive attitude. Recently, numerous researchers have echoed this phenomenon
(e.g. Giannakos, 2013; Hummel et al., 2010; Munz, Schumm, Wiesebrock, & Allgower, 2007). Many
learners engaged in serious game-assisted learning due to their curiosity, which prompted them to
play rather than learn or acquire knowledge painfully. Well-designed serious games could enhance
their intention to use them by integrating enjoyment into gameplay. After some time of gameplay
and learning, they would feel satisfied if they could make progress in learning. Therefore, designers
need to make players interested in and thus retain the gameplay.
It is generally accepted that serious games could greatly improve learning outcomes in health
education, knowledge gain, information sharing, decision-making, flow experience, mental effort,
and higher-order abilities (e.g. Reichlin et al., 2011; Medema et al., 2016; Sun et al., 2017; Flood et
al., 2018). To improve higher-order abilities, designers should carefully adapt serious games to the
cultivation of higher-order thinking and reasoning. Besides enjoyment, designs aiming to spur in-
depth critical thinking abilities may be needed. In the medical education field, experimental training
assisted with serious games may be carefully designed and developed so that medical students could
be effectively trained.
There have been numerous serious games in various fields. However, few of them could be assessed
with unbiased methods. Assessment of serious games has emerged as an important method to improve
the effectiveness and design of them. Practitioners tend to assume that serious games could effectively
facilitate learning so it becomes difficult to evaluate the serious game-based environment. The
Maximum Similarity Index, as a metric for serious games analytics, was able to identify differences
between novices and experts (Loh, & Sheng, 2014). Cognition changes during gameplay could
greatly influence the effectiveness of serious games. A device such as Emotiv EEG could identify
the differences between cognitive process and various stimulus modalities (McMahan, Parberry, &
Parsons, 2015).
This study identified major clusters, citation counts, burst detection, centrality, sigma, and co-
occurrence, student engagement, motivation, learning strategies, cognition, enjoyment, happiness,
Volume 13 • Issue 1
11
intention to use, positive attitude, and satisfaction of students in serious game-assisted education
research.
There are still some limitations in this study. On one hand, this study could not retrieve publications
from all the databases in the world. Those written in languages other than English were not included.
On the other hand, the content analysis method may be limited in itself to a lack of enough statistical
support.
There are several important directions for future research into serious games. Future research could
focus on the assessment of serious games to select the effective serious games and improve their
design. Future research could highlight how to develop serious game-integrated platforms such as
Classcraft because they could improve learning achievements, enhance learning motivation, and enrich
learning experiences (Zhang et al., 2021). Serious games integrated into mobile learning technologies
and social media tools could improve learning outcomes and enhance students’ learning motivation
(Yu et al., 2022). Games studies have also caught the attention of researchers in the field of media
and communication studies, where games have emerged as important components in media studies.
Future studies could focus on how to improve learning outcomes by combining media with serious
games (Chess & Consalvo, 2022).
This work is supported by 2019 MOOC of Beijing Language and Culture University (MOOC201902)
(Important) “Introduction to Linguistics”; “Introduction to Linguistics” of online and offline
mixed courses in Beijing Language and Culture University in 2020; Special fund of Beijing Co-
construction Project-Research and reform of the “Undergraduate Teaching Reform and Innovation
Project” of Beijing higher education in 2020-innovative “multilingual +” excellent talent training
system (202010032003); The research project of Graduate Students of Beijing Language and Culture
University “Xi Jinping: The Governance of China” (SJTS202108).
Volume 13 • Issue 1
12
Akl, E. A., Pretorius, R. W., Sackett, K., Erdley, W. S., Bhoopathi, P. S., Alfarah, Z., & Schünemann, H. J. (2010).
The effect of educational games on medical students’ learning outcomes: a systematic review: BEME Guide No
14. Medical Teacher, 32(1), 16–27. doi:10.3109/01421590903473969 PMID:20095770
Annetta, L., Minogue, J., Holmes, S., & Cheng, M. (2009). Investigating the impact of video games on high
school students engagement and learning about genetics. Computers & Education, 53(1), 74–85. doi:10.1016/j.
compedu.2008.12.020
Anyaegbu, R., Ting, W., & Li, Y. (2012). Serious game motivation in an EFL classroom in Chinese primary
school. The Turkish Online Journal of Educational Technology, 11(1), 154–164. doi:10.1017/S0958344011000310
Baptista, G., & Oliveira, T. (2017). Why so serious? Gamification impact in the acceptance of mobile banking
services. Internet Research, 27(1), 118–139. doi:10.1108/IntR-10-2015-0295
Boyle, E. A., Hainey, T., Connolly, T. M., Gray, G., Earp, J., Ott, M., Limd, T., Ninause, M., Ribeiroe, C., & Pereira,
J. (2016). An update to the systematic literature review of empirical evidence of the impacts and outcomes of
computer games and serious games. Computers & Education, 94, 178–192. doi:10.1016/j.compedu.2015.11.003
Breuer, J. S., & Bente, G. (2010). Why So Serious? On the Relation of Serious Games and Learning. Eludamos
(Göttingen), 4(1), 7–24. doi:10.7557/23.6111
Burke, J. W., McNeill, M. D. J., Charles, D. K., Morrow, P. J., Crosbie, J. H., & McDonough, S. M. (2009).
Optimising engagement for stroke rehabilitation using serious games. The Visual Computer, 25(12), 1085–1099.
doi:10.1007/s00371-009-0387-4
Burns, J. M., Webb, M., Durkin, L. A., & Hickie, I. B. (2010). Reach Out Central: A serious game designed to
engage young men to improve mental health and well-being. The Medical Journal of Australia, 192(11, Suppl),
S27–S30. doi:10.5694/j.1326-5377.2010.tb03689.x PMID:20528704
Calvo-Ferre, J. R. (2015). Educational games as stand-alone learning tools and their motivational effect on L2
vocabulary acquisition and perceived learning gains. British Journal of Educational Technology, 48(2), 264–278.
doi:10.1111/bjet.12387
Chess, S., & Consalvo, M. (2022). The future of media studies is game studies. Critical Studies in Media
Communication, 39(3), 159–164. doi:10.1080/15295036.2022.2075025
Connolly, T. M., Boyle, E. A., MacArthur, E., Hainey, T., & Boyle, J. M. (2012). A systematic literature
review of empirical evidence on computer games and serious games. Computers & Education, 59(2), 661–686.
doi:10.1016/j.compedu.2012.03.004
Den Haan, R. J., & Mascha, V. D. V. (2018). On evaluating social learning outcomes of serious games to
collaboratively address sustainability problems: A literature review. Sustainability, 10(12), 4529. doi:10.3390/
su10124529
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011) From Game Design Elements to Gamefulness: Defining
Gamification. Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media
Environments, ACM, New York, 9-15. doi:10.1145/2181037.2181040
Filsecker, M., & Hickey, D. T. (2014). A multilevel analysis of the effects of external rewards on elementary
students’ motivation, engagement and learning in an educational game. Computers & Education, 75, 136–148.
doi:10.1016/j.compedu.2014.02.008
Fitzgerald, M. M., Kirk, G. D., & Bristow, C. A. (2011). Description and evaluation of a serious game
intervention to engage low secure service users with serious mental illness in the design and refurbishment of
their environment. Journal of Psychiatric and Mental Health Nursing, 18(4), 316–322. doi:10.1111/j.1365-
2850.2010.01668.x PMID:21418431
Flood, S., Cradock-Henry, N. A., Blackett, P., & Edwards, P. (2018). Adaptive and interactive climate futures:
Systematic review of ‘serious games’ for engagement and decision-making. Environmental Research Letters,
13(6), 063005. doi:10.1088/1748-9326/aac1c6
Volume 13 • Issue 1
13
Freitas, S. D., & Jarvis, S. (2007). Serious games engaging training solutions: A research and development project
for supporting training needs. British Journal of Educational Technology, 38(3), 523–525. doi:10.1111/j.1467-
8535.2007.00716.x
Giannakos, M. N. (2013). Enjoy and learn with educational games: Examining factors affecting learning
performance. Computers & Education, 68, 429–439. doi:10.1016/j.compedu.2013.06.005
Girard, C., Ecalle, J., & Magnan, A. (2013). Serious games as new educational tools: How effective are they? A
meta-analysis of recent studies. Journal of Computer Assisted Learning, 29(207), 207–219. doi:10.1111/j.1365-
2729.2012.00489.x
Graafland, M., Schraagen, J. M., & Schijven, M. P. (2012). Systematic review of serious games for medical
education and surgical skills training. British Journal of Surgery, 99(10), 1322–1330. doi:10.1002/bjs.8819
PMID:22961509
Guillén-Nieto, V., & Aleson, M. (2012). Serious games and learning effectiveness: The case of It’s a Deal!
Computers & Education, 58(1), 435–448. doi:10.1016/j.compedu.2011.07.015
Habgood, M. P. J., & Ainsworth, S. E. (2011). Motivating children to learn effectively: Exploring the value of
intrinsic integration in educational games. Journal of the Learning Sciences, 20(2), 169–206. doi:10.1080/10
508406.2010.508029
Hamari, J., Shernoff, D. J., Rowe, E., Coller, B., & Edwards, T. (2016). Challenging games help students learn:
An empirical study on engagement, flow and immersion in game-based learning. Computers in Human Behavior,
54, 170–179. doi:10.1016/j.chb.2015.07.045
Hannig, A., Kuth, N., & Oezman, M. (2012). eMedOffice: A web-based collaborative serious game for teaching
optimal design of a medical practice. BMC Medical Education, 12(104), 104. doi:10.1186/1472-6920-12-104
PMID:23110606
Hawlitschek, A., & Joeckel, S. (2017). Increasing the effectiveness of digital educational games: The effects of
a learning instruction on students’ learning, motivation and cognitive load. Computers in Human Behavior, 72,
79–86. doi:10.1016/j.chb.2017.01.040
Hummel, H. G. K., van Houcke, J., Nadolski, R. J., van der Hiele, T., Kurvers, H., & Löhr, A. (2010). Scripted
collaboration in serious gaming for complex learning: Effects of multiple perspectives when acquiring water
management skills. British Journal of Educational Technology, 42(6), 1029–1041. doi:10.1111/j.1467-
8535.2010.01122.x
Hwang, G. J., Wu, P. H., Chen, C. C., & Tu, N. T. (2015). Effects of an augmented reality-based educational game
on students’ learning achievements and attitudes in real-world observations. Interactive Learning Environments,
24(8), 1895–1906. doi:10.1080/10494820.2015.1057747
Jacob Habgood, M. P., & Ainsworth, S. E. (2011). Motivating children to learn effectively: Exploring the value
of intrinsic integration in educational games. Journal of the Learning Sciences, 20(2), 169–206. doi:10.1080/
10508406.2010.508029
Janouskova, S., Rathouska, L. P., Zak, V., & Urvalkova, E. S. (2021). The scientific thinking and reasoning
framework and its applicability to manufacturing and services firms in natural sciences. Research in Science &
Technological Education, 1–22. doi:10.1080/02635143.2021.1928048
Jean, S., Medema, W., Adamowski, J., Chew, C., Delaney, P., & Wals, A. (2018). Serious games as a catalyst
for boundary crossing, collaboration and knowledge co-creation in a watershed governance context. Journal
of Environmental Management, 223(10), 1010–1022. doi:10.1016/j.jenvman.2018.05.021 PMID:30096742
Kebritchi, M., Hirumi, A., & Bai, H. (2010). The effects of modern mathematics computer games on mathematics
achievement and class motivation. Computers & Education, 55(2), 427–443. doi:10.1016/j.compedu.2010.02.007
Lamb, R. L., Annetta, L., Firestone, J., & Etopio, E. (2018). A meta-analysis with examination of moderators
of student cognition, affect, and learning outcomes while using serious educational games, serious games, and
simulations. Computers in Human Behavior, 80, 158–167. doi:10.1016/j.chb.2017.10.040
Loh, C. S., & Sheng, Y. (2014). Maximum similarity index (MSI): A metric to differentiate the performance of
novices vs. multiple-experts in serious games. Computers in Human Behavior, 39(10), 322–330. doi:10.1016/j.
chb.2014.07.022
Volume 13 • Issue 1
14
Loh, C. S., Sheng, Y., & Li, I. H. (2015). Predicting expert-novice performance as serious games analytics
with objective-oriented and navigational action sequences. Computers in Human Behavior, 49(8), 147–155.
doi:10.1016/j.chb.2015.02.053
Malaquias, R. F., Fernanda, F. O. M., & Hwang, Y. (2018). Understanding Technology Acceptance Features in
Learning through a Serious Game. Computers in Human Behavior, 87, 395–402. doi:10.1016/j.chb.2018.06.008
McMahan, T., Parberry, I., & Parsons, T. D. (2015). Modality specific assessment of video game player’s
experience using the Emotiv. Entertainment Computing, 7, 1–6. doi:10.1016/j.entcom.2015.03.001
Medema, W., Furber, A., Adamowski, J., Zhou, Q., & Mayer, I. (2016). Exploring the Potential Impact of Serious
Games on Social Learning and Stakeholder Collaborations for Transboundary Watershed Management of the
St. Lawrence River Basin. Water (Basel), 8(5), 175. doi:10.3390/w8050175
Michael, D. R., & Chen, S. L. (2006). Serious Games: Games That Educate, Train, and Inform. Thomson
Course Technology.
Munz, U., Schumm, P., Wiesebrock, A., & Allgower, F. (2007). Motivation and learning progress through
educational games. IEEE Transactions on Industrial Electronics, 54(6), 3141–3144. doi:10.1109/TIE.2007.907030
Poplin, A. (2012). Playful public participation in urban planning: A case study for online serious games.
Computers, Environment and Urban Systems, 36(3), 195–206. doi:10.1016/j.compenvurbsys.2011.10.003
Reichlin, L., Mani, N., McArthur, K., Harris, A. M., Rajan, N., & Dacso, C. C. (2011). Assessing the acceptability
and usability of an interactive serious game in aiding treatment decisions for patients with localized prostate
cancer. Journal of Medical Internet Research, 13(1), 188–201. doi:10.2196/jmir.1519 PMID:21239374
Sanchez, J., & Olivares, R. (2011). Problem-solving and collaboration using mobile serious games. Computers
& Education, 57(3), 1943–1952. doi:10.1016/j.compedu.2011.04.012
Sun, J. C. Y., Kuo, C. Y., Hou, H. T., & Lin, Y. Y. (2017). Exploring learners\’ sequential behavioral patterns,
flow experience, and learning performance in an anti-phishing educational game. Journal of Educational
Technology & Society, 20(1), 45–60.
Székely, M., & Michael, J. (2021). The Sense of Effort: A Cost-Benefit Theory of the Phenomenology of Mental
Effort. Review of Philosophy and Psychology, 12(4), 889–904. doi:10.1007/s13164-020-00512-7
Tagliabue, L. C., Mastrolembo Ventura, S., Teizer, J., & Ciribini, A. L. C. (2021). A Serious Game for Lean
Construction Education Enabled by Internet of Things. In Ó. Mealha, M. Rehm, & T. Rebedea (Eds.), Ludic,
Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education. Smart Innovation, Systems
and Technologies, (Vol. 197). Springer. doi:10.1007/978-981-15-7383-5_19
Varvara, G., Michail, G., & Konstantinos, C. (2016). Serious games as a malleable learning medium: The effects
of narrative, gameplay, and making on students’ performance and attitudes. British Journal of Educational
Technology, 48(3), 842–859. doi:10.1111/bjet.12455
Volejnikova, S., Andersen, P., & Clarke, K. A. (2021). Student nurses’ experience using a serious game to
learn environmental hazard and safety assessment. Nurse Education Today, 98(4), 104739. doi:10.1016/j.
nedt.2020.104739 PMID:33418087
Vos, N., Meijden, H. V. D., & Denessen, E. (2011). Effects of constructing versus playing an educational game
on student motivation and deep learning strategy use. Computers & Education, 56(1), 127–137. doi:10.1016/j.
compedu.2010.08.013
Wang, Y., Rajan, P., Sankar, C., & Raju, P. K. (2017). Let them play: The impact of mechanics and dynamics of
a serious game on student perceptions of learning engagement. IEEE Transactions on Learning Technologies,
10(4), 514–525. doi:10.1109/TLT.2016.2639019
Wouters, P., van Nimwegen, C., van Oostendorp, H., & van der Spek, E. D. (2013). A meta-analysis of the
cognitive and motivational effects of serious games. Journal of Educational Psychology, 105(2), 249–265.
doi:10.1037/a0031311
Yu, Z. (2019). A meta-analysis of use of serious games in education over a decade. International Journal of
Computer Games Technology, 1, 1–8. doi:10.1155/2019/4797032
Volume 13 • Issue 1
15
Zhonggen Yu, First Corresponding Author, Professor (distinguished) and Ph.D. Supervisor in Department of
English Studies, Faculty of Foreign Studies, Beijing Language and Culture University, has already published over
110 academic papers in distinguished journals based on rich teaching and research experiences. He is Editor in
Chief of International Journal of Technology-Enhanced Education and Academic Editor of Education Research
International. His research interest includes educational technologies, language attrition, and language acquisition.
Email: 401373742@qq.com; yuzhonggen@blcu.edu.cn
Paisan Sukjairungwattana Second Corresponding Author, Assistant Professor in the Faculty of Liberal Arts, Mahidol
University, Ph.D. in Linguistics. With over 15 years of experience Chinese language teaching, his research interests
include teaching and learning Chinese as a foreign language, second language acquisition and higher education.
Email: paisan.suk@mahidol.edu
Wei Xu Third Corresponding Author, Assistant Professor in the Faculty of Humanities and Social Sciences, City
University of Macau, Ph.D. in Linguistics. His research interests include language education, second language
acquisition, semantics and multilingual studies. He has published a number of SSCI/SCI indexed papers and has
been invited to serve as a reviewer for SSCI indexed journals. Email: weixu@cityu.mo
Yu, Z. (2020). Visualizing Co-citations of Technology Acceptance Models in Education. Journal of Information
Technology Research, 13(1), 77–95. doi:10.4018/JITR.2020010106
Yu, Z., & Yi, H. (2020). Acceptance and effectiveness of Rain Classroom in linguistics classes. International
Journal of Mobile and Blended Learning, 12(2), 77–90. doi:10.4018/IJMBL.2020040105
Yu, Z., Yu, L. H., Xu, Q. Y., Xu, W., & Wu, P. (2022). Effects of mobile learning technologies and social media
tools on student engagement and learning outcomes of English learning. Technology, Pedagogy and Education,
31(3), 381–398. doi:10.1080/1475939X.2022.2045215
Zenk, L., Primus, D. J., & Sonnenburg, S. (2021). Alone but together: Flow experience and its impact on creative
output in LEGO (R) SERIOUS PLAY (R). European Journal of Innovation Management, 25(6), 340–364.
doi:10.1108/EJIM-09-2020-0362
Zhang, Q., Yu, L. H., & Yu, Z. G. (2021). A Content Analysis and Meta-Analysis on the Effects of Classcraft
on Gamification Learning Experiences in terms of Learning Achievement and Motivation. Education Research
International, 9429112, 1–21. doi:10.1155/2021/9429112
