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Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
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Original scientific paper
Received: February 11, 2025.
Revised: March 25, 2025.
Accepted: Aprili 02, 2025.
UDC:
378.147
795:004.96
10.23947/2334-8496-2025-13-1-127-140
© 2025 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: radoslav@diplomacy.bg.ac.rs
Abstract: Digital game-based learning (DGBL) has redefined education in recent years. Instead of replacing conventional
methods of instruction, the aim is to make learning more complex and adapted to how students really engage with the modern
world. This study aims to collect data that will assist educators, students, legislators, and creators of digital games in recognizing
the value of the DGBL approach to education. Together, they may enhance and modify these approaches to better suit students’
requirements and enhance their academic performance. The study’s conclusions may significantly affect future applications of
digital educational games in educational settings. Since they could offer a deeper comprehension that would enable students’
benefit from personalized instruction through artificial intelligence (AI), while at the same time using immersive technologies would
increase students’ involvement, interest, and motivation for learning in a virtual environment. A questionnaire was emailed to 328
students at all three study levels, as well as faculty and administrative personnel from Megatrend University in Belgrade, who
took part in the study. According to the findings, participants believe that a) if digital games and educational content are combined
in learning, students are more likely to increase their learning efficiency in this way; b) if a digital game-based learning (DGBL)
approach provides a dynamic and engaging learning environment, it is more likely to increase student motivation and participation
in the learning process; c) if digital game-based learning (DGBL) includes rewards, feedback, and competition, there is greater
potential to significantly improve student learning outcomes; d) if digital game-based learning (DGBL) is supported by artificial
intelligence (AI), which enables personalization, the learning is more likely to dynamically adapt to each student’s performance.
Keywords: Digital Game-Based Learning (DGBL), digital games, students, Artificial Intelligence (AI), personalized
learning, immersive technologies.
Radoslav Baltezarević
1*
, Ivana Baltezarević
2
1
Institute of International Politics and Economics, Belgrade, Serbia, e-mail: radoslav@diplomacy.bg.ac.rs
2
Megatrend University, Faculty of Law, Belgrade, Serbia, e-mail: ivana.baltezarevic@gmail.com
Digital Game-Based Learning’s (DGBL) Effect on Students’
Academic Performance
Introduction
The most common computer activity at home for children and adolescents is playing computer
games (Harris, 2002). Digital games have a significant impact on young people’s lives by generating a
strong sense of excitement and connection (Kirriemuir and McFarlane, 2004). A vast selection of games
and instructional apps are continuously being updated on the mobile market. As a result, youth encounter
a multitude of educational programs that provide various approaches to comprehension, instruction, and
the integration of information, science, math, and artistic creativity (Liao et al., 2019). Statistics on the
global game-based learning industry income show that this market has expanded from 3.5 billion USD in
2018 to 24 billion USD in 2024 (Clement, 2021). This market is anticipated to develop at an exponential
rate during the next years. At a compound annual growth rate (CAGR) of more than 27%, it will reach
nearly $55 billion in 2029 (Thebusinessresearchcompany, 2025).
Students’ social conduct and academic performance have been found to be impacted by digital
gaming (Rahayu, 2021). According to the National School Boards Association (NSBA), students who join
esports programs have better attendance (+10%) and grades than their peers who do not participate in es-
ports (Intenta.digital, 2021). From a theoretical standpoint, there are a number of benefits to using games
for education. First of all, games can offer problem-based, active, and multisensory learning. Gamers
may engage with wider communities in game worlds and receive responses instantly. Last but not least,
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games can offer score-based self-evaluation (Chen et al., 2018). Research indicates that using games in
the classroom has a good impact on learning and can effectively raise achievement (Chen et al., 2018).
Playing games improves visual-spatial abilities, which are helpful in engineering and science (Din and
Calao, 2001). Educational digital games are more than just teaching tools. Modern games offer students a
dynamic learning platform by simulating real-world scenarios, problems, and settings (Sheehy et al., 2014).
By engaging in learning-by-doing activities, students may combine their new knowledge with their prior
knowledge and experiences. This integrity helps them learn more effectively (Pitarch, 2018).
Digital educational games are computer programs that mimic real-world situations to provide an
engaging learning environment (Kapp, 2014). The “stimulus-response” theory, another name for behav-
iorist theory, is used in digital educational games. These games gradually provide clues from simple to
complicated, offering students a variety of learning opportunities at varying levels of difficulty. Scores that
serve as feedback encourage students to meet the instructor’s expected response by acting as a stimulant
(Fokides, 2018). Digital games are advantageous due to their multimodal nature, variety in terms of the au-
thentic environments and experiences they can replicate, autonomy, feedback and reward systems, scal-
ing of difficulty and progression, chances for experimentation, and alignment with constructivist learning
theories (Yu et al., 2021). Digital games and educational content are combined in digital game-based learn-
ing (DGBL) to pique students’ attention and give them the chance to improve the efficacy of their learning.
Students consequently have a favorable lifelong perspective on education and information (Cheng et al.,
2013). Digital games can assist in molding students’ emotions and behaviors by positively impacting their
perceptions of control and benefit, as well as their affective and behavioral components. Actually, the goal
is to entertain the students and encourage both behavioral and emotional engagement in the activities
(Sarıgöz et al., 2018). Furthermore, games may improve students’ capacity for problem-solving and analy-
sis as well as their social abilities (Kirikkaya et al., 2010).
Although different scholars may have different definitions of DGBL, most of them concur that it of-
fers a motivating learning environment to improve student performance by utilizing the benefits of digital
games (Byun and Joung, 2018). Numerous studies have demonstrated the beneficial effects of DGBL on
a wide range of student learning outcomes, including information acquisition, cognitive and perceptual
abilities, affective and motivational outcomes, and behavior modification outcomes (Hussein et al., 2022).
According to a study by Li et al., students who played educational digital games showed greater motivation
to learn. Therefore, these games can be viewed as “stimuli” that have the potential to enhance students’
motivation for learning (Li et al., 2024). A major move toward more effective, individualized, and interesting
learning experiences is represented by the incorporation of 3D games, virtual worlds, and cutting-edge AI
into the classroom. Teachers can construct immersive learning environments that catch students’ attention
and promote a deeper comprehension of difficult subjects by strategically organizing instructional content
around potent gaming dynamics and utilizing the most recent technological breakthroughs (Axon Park,
2024). With technology playing a key part in changing students’ learning, the future of DGBL is becoming
increasingly fascinating. AI-driven personalization is one of the most exciting developments. In this ap-
proach, learning is dynamically tailored to each student’s performance and learning preferences, making
learning more interesting and enhancing retention as it goes (Paradisosolutions, 2025).
The consistent and repetitive use of the Internet to play games with other players on a regular basis
is known as digital game addiction, and it can have detrimental effects on many facets of life. Since gam-
ing is now easily accessible on a wide range of devices thanks to recent technology advancements, digital
game addiction has grown in frequency and severity as a major public health concern (Mohammad et al.,
2023). Mental tension, poor academic performance, insomnia, suicidal thoughts, a decline in sociability
and self-efficacy, and a drop in life satisfaction are all consequences of gaming. The negative effects of ex-
cessive gaming extend to a person’s emotional, mental, and physical health (Mardian and Hastono, 2019).
Due to students’ neglect of healthy food and sleeping patterns, digital gaming addiction has had a negative
impact on their health. When students spend too much time playing online games, they become distracted
and may engage in harmful behaviors like gambling, stealing, threatening others, or even considering
suicide (Hanafie and Makassar, 2022). The usage of digital education-based games in communities and
schools has grown in popularity in recent years; however, this has also caused families to worry about the
creation of uncertainties over the detrimental effects of these games on youth (Konok et al., 2021). Notwith-
standing all of the benefits, some experts think that educational games may worsen motor skills, develop
addiction, and raise hostility. To better understand how and what factors influence children’s use of digital
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Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 127-140.
games, the majority of researchers have called for additional research (Lazarinis et al., 2020).
Literature review
The range of genres and topic areas in which game-based learning is used can make its definition dif-
ficult to define. Nevertheless, game-based learning (GBL) can be thought of as a way to improve students’
learning experiences by integrating games with course objectives (Roodt and Ryklief, 2019). Using digital
games as teaching tools to accomplish learning objectives is known as digital game-based learning (DGBL)
(Prensky, 2001). By introducing competitive processes, accomplishment systems, and reward mechanisms,
digital game-based learning (DGBL) offers students demanding, dynamic, and engaging learning environ-
ments that greatly increase students’ motivation and participation in the learning process (Chen et al., 2018).
According to research, DGBL can be more successful than traditional approaches in a number of
subject areas, such as math instruction, foreign language learning, science study, and healthcare (Gen-
try et al., 2019). Virtual characters, challenges, quests, awards, avatars, and other well-designed game
features are what make DGBL so appealing, engaging, and inspiring (Abdul Jabbar and Felicia, 2015).
According to research in the literature, students’ motivation, engagement, attitude, and focus can all be im-
proved by effective game design and planning for digital game-based learning (Cai et al., 2022). Because
digital games offer more depth in terms of gameplay and storyline, it is crucial to remember that they are
more sophisticated than simple drill/practice games (Ertmer et al., 2012). Simulations that aim to capture
the intricacy of real-life circumstances can be found in this kind of game. As an alternative, they can pre-
sent fictitious or even fantastical situations to encourage involvement and immersion with gripping stories.
They have been created to reflect real-life circumstances and to fulfill instructional objectives and key end
purposes (Kapp, 2014).
Research indicates that when game-based learning was used, students spent 93% of class time
on task, compared to 72% when it wasn’t (Nisbet, 2024). As an indicator of engagement, 81% of students
reported having fun while performing game-based activities during the summer semester, according to a
study done a few years ago by de Carvalho et al. (de Carvalho et al., 2016). By providing students with
instant feedback, games are supposed to help young people enjoy learning, increase their self-esteem,
creativity, and imagination, and guide them to the right knowledge (Gurpinar, 2017). Educational games
are a learner-centered approach that motivates students, makes instruction more efficient and pleasur-
able, and allows them to have fun (Boghian et al., 2019). Predetermined rules and objectives, quick feed-
back on students’ activities, and a gradually increasing degree of difficulty are all important components of
DGBL environments (Mayer and Johnson, 2010). The results of numerous researches show that adding
rewards, feedback, and competition to DGBL can greatly enhance learning (Yang et al., 2022). Learners’
critical thinking skills are enhanced via game-based learning, as well. When playing a game with others,
students must cooperate and exchange ideas. Because of this, students must listen to and consider the
opinions of other students before choosing their next move (Mao et al., 2022). Processes that let students
take control of their own education are included in DGBL. When learners receive immediate feedback
in the game regarding their knowledge gaps, they are immediately assigned game activities to help fill
up these areas that are thought to need more practice (Harding, 2023). In these games, students must
complete challenging activities in a set amount of time to receive points. More points are awarded for
quicker completion times, and points can be redeemed for exclusive incentives, encouraging students to
take charge of their education. Additionally, the digital gaming system shows the names of students who
perform well, fostering a sense of accomplishment that encourages good behavior like paying attention in
class (Fokides, 2018). According to O’Donovan et al., DGBL leaderboards would encourage competition
and a sense of community among like-minded groups (O’Donovan et al., 2013). When their points are
displayed on the scoreboard, learners feel motivated to improve (Alhebshi and Halabi, 2020). Students
will continue to replay the game in order to enhance their performance, which will improve their learning
performance (Behnamnia et al., 2020). Additionally, when students see that their peers have won specific
game aspects or have attained a high ranking on the leaderboard, they could try harder to do better in
games (Huang and Hew, 2018).
The following are just a few of the many examples of game-based educational platforms that aim to
boost student engagement and productivity by integrating gaming components into the training approach.
Incorporating game-based learning into educational environments was pioneered by platforms “Blooket”
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and “Gimkit.” To increase player involvement, they provide a range of game modes. Educators can use
Blooket’s variety of gaming possibilities to make learning engaging and dynamic. However, Gimkit has
a special feature called an in-game economy that allows students to earn points and spend them to buy
improvements, creating a fun and competitive learning environment (Miller, 2024). A game-based learn-
ing platform called “SC Training” incorporates various engagement components to help students improve
their course completion rates while guaranteeing a productive learning environment. This application
has an integrated writing tool with interactive templates such as true or false, letter jumble, image/word
match, and many more (Bariuad, 2022). Students who successfully complete drill-and-kill grammar and
vocabulary activities on the app “Duolingo” earn experience points that may be used to advance to more
challenging exercises, gamifying language learning. Students can make their own avatars and inhabit a
3D environment in “Second Life,” a virtual reality that can facilitate text communication and lessen speech
anxiety (Uwaterloo, 2024). A version of Minecraft called “Minecraft Education” was created specifically for
classroom instruction. This edition is educational for students of all ages. Teachers can use the materi-
als they have in-game to make their own lesson plans. Furthermore, a number of lesson plans covering
a wide range of topics, including language arts, physics, history and culture, computer science, art and
design, and math, are now available (Minecraft, 2025). In order to engage and inspire students to learn
science, “Alien Rescue” combines gaming aspects, play, and authenticity to create a lighthearted experi-
ence with a purposefully problem-based narrative. Students are asked to participate in an urgent United
Nations rescue expedition to save the distraught aliens in the open-ended game scenario, which puts
them in the role of young scientists. Through a 3D immersive, sensory-rich method, a playful fantasy
experience is combined with this genuine scientific investigation process (Lee and Liu, 2017). Designing
gamified content for microlearning is made possible by “Centrical,” a powerful gamification training tool.
To assist players in adopting the proper behaviors, practicing skills in a risk-free virtual environment,
and improving the general knowledge and abilities they require, the platform lets users create narrative
mission-based games, set up prize tournaments, or tailor learning challenges (Bariuad, 2022).
A 2023 study that involved 69 students learning English as a second language tested vocabulary
acquisition abilities using “Quizziz,” a DGBL tool. Students were divided into two groups for this study: the
experimental group practiced vocabulary using Quizziz, while the control group practiced vocabulary in
their mother tongue. The experimental group did noticeably better than the control group, according to the
data, proving the value of DGBL (Nisbet, 2024). Anderson et al. conducted a study to examine the effect
of failure in learning by examining the gaming habits and discussions of 88 middle school students who
were playing the educational video game “Virulent.” They discovered that players learned more effectively
when they worked together, with more accomplished players sharing their techniques with less successful
peers (Anderson et al., 2018). According to Khan et al., students are more engaged when instructional
design is oriented toward DGBL (Khan et al., 2017). A study by Yurdaarmagan et al. demonstrated that,
in contrast to the conventional method, students’ academic success is increased when using a DGBL
approach. Students (a total of 152) were split up into two groups for their research. While the first group
received a standard teacher’s lecture, the second group engaged in game-based learning in a labora-
tory setting. The results of the study demonstrated that the two groups’ test scores differed significantly,
with the group that engaged in game-based learning in a lab obtaining, on average, 10% higher scores
(Yurdaarmagan et al., 2015). Chen et al. investigated the efficacy of digital game-based vocabulary learn-
ing in a meta-analysis study. To examine the data from a few chosen studies, they employed Comprehen-
sive Meta-Analysis Version 3. The data gave them an approximation of how well language learning was
impacted by DGBL, and it revealed that study participants’ vocabulary acquisition had increased (Chen
et al., 2018). US students who participated in DGBL and the effect this approach had on their academic
performance were examined in a study by Liu et al. The digital game “Alien Rescue,” was utilized with the
220 students who took part in the study for three weeks during their regular science class. Throughout the
study, a science knowledge test was used to evaluate the learning performance of the students. Partici-
pants in the study took the test both before and after playing the digital game. The test results revealed a
notable rise in the percentage of right answers (over 80%) in comparison to the results obtained prior to
the use of the digital game (around 50%). In this specific study, the results showed that participants who
used DGBL significantly increased their scientific knowledge (Liu et al., 2011).
However, there are drawbacks to DGBL as well. These are mainly reflected in the fact that these
games require time to learn and play correctly, occasionally require additional, costly materials, and some-
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times call for pedagogical and technical support, which naturally calls for more resources (Discoverdigital,
2020). DGBL can be very entertaining and motivating, but it can also be distracting. A clear expectation
for learning time must be established with students because some may find the desire to continue playing
the game too strong (Harding, 2023). Time, money, and technological resources might not be easily ac-
cessible in some educational settings. Furthermore, game distractions can occasionally occur if they are
not properly monitored and controlled. Lastly, game-based learning is not a good way to teach every sub-
ject or skill. Using games to communicate abstract or difficult concepts could be tough (Callahan, 2024).
Another problem that arises with DGBL, but also with playing all digital games in general, is the threat to
players’ cybersecurity. The following are some of the most frequent cybersecurity threats that students
encounter when playing digital games: a) Phishing attacks, in which hackers obtain user credentials. By
doing this, an attacker can obtain valuable in-game elements that they can either keep for themselves or
sell on the black market; b) A data breach as an attack on the gaming corporation whose systems may
include a variety of sensitive data. Personally identifiable information (PII), such as the player’s name,
address, and credit card information, is often owned by a gaming company. The sites might be targeted
by cybercriminals who want to steal data to resell on the dark web or utilize in future assaults; c) To obtain
access to user accounts, another popular cyberattack is credential stuffing. The attacker in this instance
is making use of weak passwords, which is a frequent issue (Behnke, 2023).
When it comes to learning, artificial intelligence-enabled game-based learning establishes a new
paradigm in which the younger generation uses digital technology, including mobile or ubiquitous gadg-
ets (Chen et al., 2022). The role of AI in digital games in education was conceptualized by McLaren and
Nguyen in two ways: as games that use AI to function and interact with players and as games that have
been created and/or expanded using AI techniques (McLaren and Nguyen, 2023). Scholars have been
attracted to the argument that AI applications can enhance adaptability in game-based learning. Person-
alization, game difficulty balancing, assessment, player analytics, competence modeling, social gamifica-
tion, language technologies, and emotional computing are just a few of the AI-based features covered by
the game design elements that enable learning (Westera et al., 2020). Motivation and engagement, two
essential components of effective gamification, can be greatly increased by this personalized approach
(Parody et al., 2022). Researchers have discovered that AI-powered chatbots can improve learners’ emo-
tional, behavioral, and metacognitive awareness in virtual reality gaming learning environments (Liang
et al., 2024). Also, DGBLs are important data sources for AI training, which aims to improve individual-
ized experiences and get a deeper knowledge of learners. The majority of current efforts focus on using
digital games to train AI algorithms by taking advantage of the organized progression that is a feature
of game design (Silver et al., 2017). In AI-driven DGBL, feedback can be considered an affordance that
extends beyond conventional evaluations. Both educators and students can get current and pertinent
information through this dynamic and ongoing process. By highlighting not only the informative aspect
of identifying potential student misunderstandings but, more importantly, by generating feedback that is
practical and encourages a cycle of continuous development, this particular affordance may enhance the
quality of feedback given (Romero et al., 2024). To guarantee that AI-driven adaptations continuously
serve educational purposes, it is imperative to make sure that game mechanics are in line with learn-
ing objectives. This alignment becomes more complicated with AI and calls for careful design (Kingsley
and Grabner-Hagen, 2015). The use of AI in education does, however, present some serious difficulties.
Careful thought must be given to data privacy and AI bias concerns (Wang et al., 2024). Despite the
undeniable benefits that DGLB’s application of AI provides to the field of education, extra care must be
taken in the near future to safeguard students’ cybersecurity. In any event, more precise legislation must
be passed at the state level, and those who engage in such illicit cyber activity must be suitably identified
and prosecuted (Baltezarević and Baltezarević, 2021). However, the dilemma comes from the potential
that resolving security issues could jeopardize privacy protection (Baltezarević and Baltezarević, 2015).
Game-based learning’s remarkable effect on students is largely due to its seamless integration with
cutting-edge technology like augmented reality (AR) and virtual reality (VR). This tasteful combination
improves the educational process overall and opens up new avenues for student participation and interac-
tion (Sharma, 2023). The virtual reality (VR) and generative artificial intelligence (AI) technologies were
used to build the immersive game-based learning platform known as “LearningverseVR.” Using a shared
computer and webcam, this platform offers an immersive learning environment where students may take
on the role of avatars to interact with virtual items and other avatars. They can also utilize unique collabo-
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Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 127-140.
ration tools to build activities (Song et al., 2024). Research is still being conducted, but a number of case
studies have shown how effective these tools are in classrooms. In science classes, for instance, virtual
labs let students perform experiments in a secure environment, and historical reconstruction games let
players immerse themselves in past cultures. In addition to enhancing students’ comprehension, these
experiences pique their interest and cultivate a passion for learning (Axon Park, 2024).
Objective of the research
T
his study examines how students’ academic performance is impacted by digital game-based
learning (DGBL).
One main and three supporting hypotheses were developed in order to fulfill the research’s objective:
H
0
: If digital games and educational content are combined in learning, students are more likely to in-
crease their learning efficiency in this way.
H
1
: If a digital game-based learning (DGBL) approach provides a dynamic and engaging learning en-
vironment, it is more likely to increase student motivation and participation in the learning process.
H
2
: If digital game-based learning (DGBL) includes rewards, feedback, and competition, there is great-
er potential to significantly improve student learning outcomes.
H
3
: If digital game-based learning (DGBL) is supported by artificial intelligence (AI), which enables
personalization, the learning is more likely to dynamically adapt to each student’s performance.
Materials and Methods
Pattern and procedure
With a disclaimer that the study is being carried out solely for scientific purposes, the questionnaire
used to assess participant attitudes was sent to 350 email addresses belonging to students at all three
study levels, as well as to professors and administrative staff of a Megatrend University in the Republic
of Serbia. A total of 328 accurately and completely filled questionnaires comprise the sample that served
as the basis for the research, namely: 186 (56.7%) male and 142 (43.3%) female (M=1.43, SD=.496), of
which 8 (2.4%) with elementary school, 98 (29.9%) with secondary school, 136 (41.5%) completed high
school/college, 67 (20.4%) completed master’s degree, and 19 (5.8%) completed doctorate (M=2.97,
SD=0.913). The age-related structure of the respondents demonstrates that 114 (34.8%) were aged 26-
35, 76 (23.2%) were aged 18-25, 68 (20.7%) were aged 46-55, 55 (16.8%) were aged 36-45, and 15
(4.6%) were aged 56-65 (M=2.49, SD=1.186) participated in the research.
In order to ascertain the respondents’ socio-demographic characteristics, the questionnaire was
designed with three questions: gender, age, and professional qualifications. After that, 15 statements
were created to investigate the responses from participants regarding the application of digital game-
based learning (DGBL) in educational settings. A software program for data processing and analysis (IBM
SPSS Statistics) was used to process the data. The analysis of the gathered data was done using descrip-
tive statistics (average value, or M, and standard deviation, or SD) and statistical inference. To assess
the obtained values of Spearman’s rank correlation coefficient rho and Pearson’s correlation r, we used a
value scale that states that a correlation is weak when r ≥ 0.1, moderately strong when r ≥ 0.3, and strong
when r ≥ 0.5 (Field, 2009, p. 100).
Instruments
For further research on the specific research assignment described in this paper, eight variables out of a
total of fifteen were selected, and a subscale was created from them. The reliability of the scale was measured
by Cronbach’s alpha coefficient, which showed that α = .913. The mean values of the subscale range from 2.72
to 3.44., which shows a high value of the internal consistency of the scale (Briggs and Cheek, 1986, p.115).
The analysis employed correlation analysis, scale reliability analysis, and descriptive statistics.
A Likert-type scale with five points was used to analyze the format of the responses to the state-
ments (from 1 = I do not agree at all to 5 = I completely agree).
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Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
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Results
Using correlation analysis, we examined the responses on the following statements to verify the
validity of H
0
: (T1) Digital gaming should be part of the learning content (M=3.32, SD=1.091) and (T2)
digital games in education contribute to the effectiveness of learning (M=3.44, SD=1.335).
Table 1. Presentation of correlation data and the coefficient of determination for H
0
Symmetric Measures
Value Asymp. Std. Error
a
Approx. T
b
Approx. Sig.
Ordinal by Ordinal
Gamma .602 .046 11.194 .000
Spearman Correlation .563 .046 12.289 .000
c
Interval by Interval Pearson’s R .571 .046 12.566 .000
c
N of Valid Cases 328
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
According to Table 1’s results analysis, the Chi square test of independence demonstrates the
statistical significance of the impact on the result χ2(16,1) 483.414
a
=, p < 0.01. Significance (p ≤ .05)
indicates how certain one can be that the relationship. p < 0.01 shows that the intersection of variables is
statistically significant. Spearman’s rank correlation coefficient rho = 0.563 and Pearson’s linear correla-
tion r = 0.571 indicate how strong the relationship is and in what direction, and in this case reflect a strong
positive correlation and a direct connection between digital gaming as a part of the learning content and
the effectiveness of learning. Association measure Gamma indicates the extent to which the variation in
the changing variable (T1) is explained by the changing variable (T2). Gamma coefficient 0.602 means
that knowing the level of acceptance of the first statement improves the prediction of acceptance of the
second statement by 60.2%. Given the substantial correlation found between these two variables and the
strong correlation found between the statements provided, H
0
was confirmed.
Using correlation analysis, we examined the responses on the following statements to verify the
validity of H
1
: (T3) A digital game-based learning (DGBL) approach provides a dynamic and engaging
learning environment (M=2.72, SD=1.309), and (T4) learning based on digital games has a positive effect
on students’ motivation (M=3.22, SD=1.214).
Table 2. Presentation of correlation data and the coefficient of determination for H
1
Symmetric Measures
Value Asymp. Std. Error
a
Approx. T
b
Approx. Sig.
Ordinal by Ordinal
Gamma
.640 .044 12.167 .000
Spearman Correlation .600 .043 13.540 .000
c
Interval by Interval Pearson’s R .624 .035 14.403 .000
c
N of Valid Cases 328
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
According to Table 2’s results analysis, the Chi-square test of independence demonstrates the
statistical significance of the impact on the result χ2(16,1) =371.543
a
, p<0.01. Significance (p ≤ .05)
indicates how certain one can be that the relationship. p<0.01 shows that the intersection of variables is
statistically significant. Spearman’s rank correlation coefficient rho=0.600 and Pearson’s linear correlation
r = 0.624 indicate how strong the relationship is and in what direction, and in this case reflect a strong
positive correlation and a direct connection between a digital game-based learning (DGBL) approach
that provides a dynamic and engaging learning environment and learning based on digital games that
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134
Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 127-140.
has a positive effect on students’ motivation. Association measure Gamma indicates the extent to which
the variation in the changing variable (T3) is explained by the changing variable (T4). Gamma coefficient
0.640 means that knowing the level of acceptance of the first statement improves the prediction of ac-
ceptance of the second statement by 64.0%. Given the substantial correlation found between these two
variables and the strong correlation found between the statements provided, H
1
was confirmed.
Using correlation analysis, we examined the responses on the following statements to verify the va-
lidity of H
2
: (T5) Digital game-based learning should include rewards, feedback, and competition (M=3.08,
SD=1.305) and (T6) good information, competition, and rewarding students have a positive effect on
learning outcomes (M=3.31, SD=1.070).
Table 3. Presentation of correlation data and the coefficient of determination for H
2
Symmetric Measures
Value Asymp. Std. Error
a
Approx. T
b
Approx. Sig.
Ordinal by Ordinal
Gamma .510 .053 8.705 .000
Spearman Correlation .450 .049 9.087 .000
c
Interval by Interval Pearson’s R .445 .049 8.973 .000
c
N of Valid Cases 328
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
According to Table 3’s results analysis, the Chi-square test of independence demonstrates the
statistical significance of the impact on the result χ2(16,1) =153.727
a
, p<0.01. Significance (p ≤ .05)
indicates how certain one can be that the relationship. p<0.01 shows that the intersection of variables is
statistically significant. Spearman’s rank correlation coefficient rho=0.450 and Pearson’s linear correla-
tion r=0.445 indicate how strong the relationship is and in what direction, and in this case reflect a strong
positive correlation and a direct connection between the digital game-based learning (DGBL) that includes
rewards, feedback, and competition and its great potential to significantly improve student learning out-
comes. Association measure Gamma indicates the extent to which the variation in the changing variable
(T5) is explained by the changing variable (T6). A gamma coefficient of 0.510 means that knowing the
level of acceptance of the first statement improves the prediction of acceptance of the second statement
by 51,0%. Given the substantial correlation found between these two variables and the strong correlation
found between the statements provided, H
2
was confirmed.
Using correlation analysis, we examined the responses on the following statements to verify the
validity of H
3
: (T7) Digital game-based learning (DGBL) supported by artificial intelligence (AI) enables
personalization (M=3.30, SD=1.117), and (T8) personalization of learning contributes to dynamically
adapting learning to the performance of each student (M=3.21, SD=1.008).
Table 4. Presentation of correlation data and the coefficient of determination for H
3
Symmetric Measures
Value Asymp. Std.
Error
a
Approx. T
b
Approx. Sig.
Ordinal by Ordinal
Gamma
.669 .048 12.092 .000
Spearman Correlation .577 .045 12.747 .000
c
Interval by Interval Pearson’s R .582 .044 12.936 .000
c
N of Valid Cases 328
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
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135
Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 127-140.
According to Table 4’s results analysis, the Chi-square test of independence demonstrates the
statistical significance of the impact on the result χ2(16,1) =216.022
a
, p<0.01. Significance (p ≤ .05) shows
how certain one can be that the relationship. p<0.01 indicates that the intersection of variables is statistical-
ly significant. Spearman’s rank correlation coefficient rho=0.577 and Pearson’s linear correlation r =0.582
indicate how strong the relationship is and in what direction, and in this case reflect a strong positive cor-
relation and a direct connection between digital game-based learning (DGBL) supported by artificial intel-
ligence (AI) that enables personalization and personalization of learning, which contributes to dynamically
adapting learning to the performance of each student. Association measure Gamma indicates the extent to
which the variation in the changing variable (T7) is explained by the changing variable (T8). Gamma coef-
ficient 0.669 means that knowing the level of acceptance of the first statement improves the prediction of
acceptance of the second statement by 66.9%. Given the substantial correlation found between these two
variables and the strong correlation found between the statements provided, H
3
was confirmed.
Discussions
In recent years, digital learning games have emerged as an established practice in the field of
education. With the greater understanding of gaming that young people today possess, educators can
design engaging learning environments to increase student interest. Drawing students’ attention is one
of the main benefits of DGBL in the classroom. Students are thus encouraged to actively engage in their
education, in contrast to textbooks and conventional classroom instruction, which are occasionally insuf-
ficiently compelling to grab and hold students’ attention. DGBL methods provide instant feedback on
the choices students make while playing games, in contrast to traditional ways of testing and verifying
students’ knowledge for evaluation by their instructors. In this way, students are able to improve their
problem-solving abilities almost immediately by learning from their failures in real time. Additionally, DGBL
promotes cooperation among participants since social contact rises when students collaborate to solve
game difficulties or when they compete against other student groups. Communication, cooperation, team-
work, and social interaction can all contribute to deeper learning, which in turn can improve grades and
test scores. DGBL can be used for purposes other than schooling, it speeds up the effects of healthcare,
civic participation, and staff training (Sharma, 2023). Thanks to DGBL, students from many countries are
working together on projects and learning about each other’s cultural perspectives, which is increasing
the appeal of global collaboration. This creates an inclusive, multicultural classroom that broadens stu-
dents’ horizons and increases their awareness of the world (Paradisosolutions, 2025).
However, depending on the individual learning style of each student or the educator’s approach to
teaching, there may be drawbacks to learning through digital games. These negative aspects include the
fact that students spend excessive amounts of time in front of computers. Playing games can disrupt their
other daily activities, and digital games aren’t always in line with the objectives of their studies. Finally,
students may be exposed to possible threats to their cybersecurity through DGBL. To obtain unauthor-
ized access, cybercriminals frequently employ techniques like password cracking and security vulnerability
exploitation. Proper student engagement and learning outcomes are obviously impacted by this. Phishing
schemes in-game frequently take the shape of emails that appear authentic and deceive gamers into divulg-
ing personal information or login credentials. Additionally, social engineering in games refers to coercing
users into disclosing private information or taking activities that jeopardize their security (Cooper, 2024).
The ongoing integration of cutting-edge digital technology (such as artificial intelligence and immer-
sive technologies) is what DGBL’s future holds. AI-enhanced virtual worlds and more complex 3D games
create new opportunities for individualized, interesting, and successful education. With technology greatly
influencing how students learn, the future of DGBL is becoming very interesting. AI-driven personalization
is one of the most exciting developments. It allows learning to adjust in real time to each student’s per-
formance and preferred method of learning, generating dynamic learning routes that suit each student’s
needs, increasing learning engagement, and enhancing retention as it goes (Paradisosolutions, 2025).
The current study suggests that when learning is personalized using AI, students are more likely to iden-
tify their own abilities and inventiveness (Baltezarević and Baltezarević, 2024).
Participants provided their attitudes, for the study’s purposes, about the effects of digital game-
based learning (DGBL) on students’ academic performance. According to the results, students are more
likely to improve their learning efficiency when they combine educational content with digital games.
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136
Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 127-140.
This is in line with earlier research showing that digital gaming affects young people’s social behavior
and academic achievement (Rahayu, 2021). Additionally, DGBL combines instructional information with
digital games to attract students’ interest and provide them with opportunities to enhance their learning
effectiveness. As a result, students have a positive outlook on learning and information for the rest of their
lives (Cheng et al., 2013). The study also shows that if a DGBL strategy provides a dynamic and engag-
ing learning environment, it is more likely to boost student motivation and involvement in the learning
environment. This outcome is consistent with research by Li et al. which demonstrated that students who
played educational digital games were more motivated to learn. Consequently, it is possible to consider
these games as “stimuli” that could increase students’ learning motivation (Li et al., 2024). The next find-
ing of this study, that DGBL, which includes rewards, feedback, and competition, has greater potential
to significantly improve student learning outcomes, finds its basis in the finding of Chen et al. They claim
that DGBL provides students with challenging, dynamic, and captivating learning environments that sig-
nificantly boost students’ motivation and involvement in the learning process by implementing competitive
processes, accomplishment systems, and reward mechanisms (Chen et al., 2018). Regarding the result,
according to whom artificial intelligence (AI) that facilitates personalization through digital game-based
learning (DGBL) increases the likelihood that the learning will dynamically adjust to each student’s per-
formance. This supports a previous study by Westera et al. that discovered AI applications can improve
game-based learning’s adaptability. Game design aspects that facilitate learning encompass a variety of
AI-based features, including social gamification, language technologies, emotional computing, compe-
tence modeling, evaluation, player analytics, personalization, and game difficulty balancing (Westera et
al., 2020). There is a connection between this remark and the assertion made by Parody et al. that this
personalized approach may significantly increase motivation and engagement, two crucial elements of
successful gamification (Parody et al., 2022).
Conclusions
The aim of this study was to investigate participants’ perspectives on the effects of digital game-
based learning (DGBL) on students’ academic performance for the purpose of gathering information that
will assist educators, lawmakers, game designers, and artificial intelligence developers in improving the
educational role of digital games in order to enhance student achievement. According to this study, partici-
pants believe that DGBL can increase student learning efficiency and that such a dynamic and engaging
learning environment can increase their motivation and participation. If DGBL includes rewards, feedback,
and competition, it can further improve student learning outcomes. Finally, if DGBL is supported by AI,
personalization can be enabled, and in this way, learning may be dynamically adapted to each student’s
performance. The DGBL approach is currently in its initial phases of application; therefore, more time is
required for its continued development and adaptation. Moreover, all current obstacles must be rectified
before this approach may realize its full potential. Additional studies would also more thoroughly analyze
how DGBL affects students and instructors as well as the educational sector as a whole, providing a bet-
ter grasp of all the benefits and drawbacks of these methods of instruction. With a solid basis for further
research and real-world applications, this study contributes notably to the fields of educational technology,
artificial intelligence (AI), and immersive technologies (which are known as augmented reality (AR) and
virtual reality (VR)). Additional research involving other respondent demographics, implementation and
evaluation techniques could broaden the conclusions of this empirical study.
Based on our results, we suggest that future studies thoroughly examine the function of immersive
technologies in DGBL, paying special attention to the potential applications of the latest technology that
can arouse students’ senses in an augmented or virtual reality setting (e.g., haptic gloves and suits). This
would increase students’ involvement, interest, and motivation for learning while reducing the sense that
they are not a part of the virtual environment. Under these virtual circumstances, students can visit muse-
ums or well-known historical locations, work on group projects, or carry out difficult tasks (like practicing
virtual surgery in authentic settings). In any event, this virtual experience makes learning more memora-
ble, efficient, and enjoyable.
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137
Baltezarević R. & Baltezarević I., (2025). Digital Game-Based Learning’s (DGBL) Effect on Students’ Academic Performance,
International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 13(1), 127-140.
Acknowledgements
The paper presents findings of a study developed as a part of the research project “Serbia and
challenges in international relations in 2025”, financed by the Ministry of Science, Technological Develop-
ment and Innovation of the Republic of Serbia, and conducted by Institute of International Politics and
Economics, Belgrade during year 2025.
Conflict of interests
The authors declare no conflict of interest.
Author Contributions
Conceptualization, B.R., and B.I.; methodology, B.R.; software, B.R.; formal analysis, B.R.; writ-
ing—original draft preparation, B.R. and B.I.; writing—review and editing, B.R. and B.I. All authors have
read and agreed to the published version of the manuscript.
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