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Article Not peer-reviewed version
An In-depth Evaluation of Educational
Burst Games - in Relation to Learner
Proficiency
Ashish Amresh , Vipin Verma * , Michelle Zandieh
Posted Date: 30 July 2024
doi: 10.20944/preprints202407.2425.v1
Keywords: game-based learning; assessment; design; proficiency
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Article
An In-Depth Evaluation of Educational Burst Games -
in Relation to Learner Proficiency
Ashish Amresh , Vipin Verma * and Michelle Zandieh
Decision Theater, Arizona State University, Orchid House at the Brickyard, 21 E 6th St #126a, Tempe, AZ 85281;
amresh@asu.edu (A.A.); michelle.zandieh@asu.edu (M.Z.)
*Correspondence: vverma9@asu.edu
Abstract: Game-based learning assessments lean on educational data mining approaches such as stealth assess-
ments and quasi mixed-methods that help gather student learning proficiency. Rarely do we see approaches
where student proficiency in learning is woven into the game’s design. Educational Burst Games (EBGs) is a new
approach to improving learning proficiency by designing fast-paced, short, repetitive, and skill-based games.
They have the potential to be effective learning interventions both during instruction in the classroom and during
after-school activities such as assignments and homework. Over five years we have developed two EBGs aimed
at improving linear algebra concepts of undergraduate students. In this study, we provide results of an in-depth
evaluation of the two EBGs developed with 45 participants that represent our target population. We discuss the
role of EBGs and their design constructs such as pace and repetition, the effect of the format (2D vs. 3D), the
complexity of the levels and the influence of prior knowledge on the learning outcomes.
Keywords: game-based learning; assessment; design; proficiency
1. Introduction
Educational technology has transformed how knowledge is acquired, making learning more
interactive, engaging, and accessible. Among these innovations, educational burst games (EBGs) have
emerged as a promising tool, leveraging the appeal of gaming to enhance learning outcomes [
1
]. Skill
and drill games have had a long history of promoting proficiency in a particular subject area [
2
]. Math
Bingo [
3
] replaces numbers with math problems in its bingo cards. Sentence Scramble [
4
] provides
students with sentences that are scrambled and they have to arrange the words to be grammatically
accurate. Science Memory Match [
5
] creates science cards with terms and definitions that students
have to correctly match and Vocabulary Dominoes [
6
] uses words and their definitions as a matching
strategy to connect the tiles. The common theme in the skill and drill games is the application of a
learning strategy to an existing game mechanic (Bingo, Dominoes, etc.). EBGs include some skill and
drill activity in the design of the game mechanic. However, what separates them is the inclusion of
repetition and leveling-up similar to popular games such as Angry Birds and Peggle. The educational
elements are then woven into EBGs to map the skill and the appropriate level of difficulty with
proficiency or mastery of the subject matter. This study explores the effectiveness of EBGs in promoting
learning proficiency, considering factors such as game complexity, format, and the learners’ prior
knowledge.
2. Background
The integration of digital games in education has been extensively studied, showing varying
impacts on motivation, engagement, and learning outcomes [
7
]. EBGs, characterized by their short
duration and focus on repetitive learning tasks, offer unique advantages for educational settings. They
align with cognitive theories suggesting repetition and engagement is critical for knowledge retention
and skill acquisition [8].
Cognitive Load Theory [
9
] posits that learning is more effective when cognitive load is optimized.
EBGs, with their brief and focused gameplay sessions, minimize extraneous cognitive load, allowing
learners to concentrate on core educational content. Additionally, the theory of spaced repetition [
10
]
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supports the idea that information is more effectively retained when learning is distributed over time,
a principle that EBGs naturally incorporate through their repetitive gameplay mechanics.
Research has shown that game-based learning (GBL) can significantly enhance student engage-
ment and motivation [
11
,
12
]. Studies by Papastergiou [
13
] and Ke [
14
] have demonstrated the positive
effects of digital games on learning outcomes in various subjects, including mathematics and language
learning. However, the impact of game complexity, format, and prior knowledge on learning efficiency
remains underexplored. This gap in research motivates the current study, aiming to provide insights
into designing more effective EBGs for educational purposes. In prior work [
15
] we explored the
relationship between combining inquiry-oriented learning (IOL) and game-based learning (GBL)
where linear algebra concepts were discussed in classroom activities by the design of two EBGs. The
study aimed to weave EBGs into an existing curricular framework for teaching linear algebra [
16
].
We then studied the effects of learning strategies that students employed due to the addition of the
EBGs [
17
–
20
]. Our findings indicate that students can be directed to explore multiple strategies and
critically evaluate the benefits when situated within the context of an EBG. For example, it was evident
that strategies such as "Guess and Check" or "Mental Math" are nuanced and could have multiple
meanings in their usage with the students. While the strategies are nuanced, EBGs help reinforce the
proficiency levels of students through repetition and discussion of these strategies. However, questions
remained in terms of the effect that EBGs have on increased proficiency in the subject matter and this
study aims to help explore how effective EBGs can be in improving proficiency.
Studies on repetitive practice, such as those by Ericsson et al. [
21
] indicate that deliberate practice
is key to achieving high levels of proficiency. Repetitive practice involves engaging in activities that
require focused, repetitive efforts to improve performance. Ericsson’s research emphasizes that expert
performance is largely the result of prolonged efforts to improve performance while negotiating
motivational and external constraints. By repeatedly engaging with EBGs, learners can internalize and
master the educational content more effectively. EBGs provide a structured yet flexible environment
where learners can repeatedly practice and refine their skills, leading to improved proficiency over
time.
Vygotsky’s [
22
] concept of the Zone of Proximal Development (ZPD) suggests that tasks should
be within the learner’s capacity to be challenging yet achievable. The ZPD represents the difference
between what a learner can do without help and what they can achieve with guidance and encour-
agement from a skilled partner. Investigating how varying levels of game complexity impact learner
proficiency can provide insights into optimizing game design for educational purposes. EBGs can be
tailored to progressively increase in difficulty, ensuring that learners are constantly challenged within
their ZPD, which can enhance learning outcomes.
Research by Mayer and Moreno [
23
] on multimedia learning indicates that simpler visual presen-
tations can facilitate better cognitive processing. Their Cognitive Theory of Multimedia Learning posits
that learning is more effective when information is presented in a way that aligns with how human
cognitive systems process information. Comparing 2D and 3D game formats can reveal whether
simpler designs enhance learning efficiency by reducing cognitive load. EBGs designed in 2D formats
may be easier for learners to navigate and understand, potentially leading to better learning outcomes
compared to more complex 3D formats.
Theories of prior knowledge activation [
24
] emphasize that learners’ existing knowledge sig-
nificantly impacts new learning. Prior knowledge provides a framework for understanding and
assimilating new information. Assessing how prior knowledge influences proficiency in EBGs can
help tailor these games to individual learner needs. For instance, EBGs can be designed with adaptive
learning paths that account for the learner’s prior knowledge, ensuring that each learner receives
personalized challenges that match their skill level.
Bandura’s [
25
] theory of self-efficacy highlights the importance of learners’ beliefs in their capa-
bilities. Self-efficacy refers to an individual’s belief in their ability to succeed in specific situations or
accomplish a task. Understanding how EBGs affect self-efficacy can provide valuable insights into their
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motivational and psychological benefits, potentially leading to more confident and capable learners.
EBGs can enhance self-efficacy by providing immediate feedback, achievable goals, and opportunities
for learners to experience success, thereby boosting their confidence and motivation to learn.
This study aims to fill the research gaps by systematically evaluating the impact of EBGs on
learning proficiency, considering factors such as game complexity, format, and prior knowledge.
Through this investigation, we hope to provide actionable insights for educators and game designers,
ultimately enhancing the effectiveness of EBGs in educational settings. This study will address the
following research questions.
1. Does quick repetitive gameplay (in burst games) help improve learner proficiency?
2. Does the complexity of the levels affect learner proficiency in burst games?
3. Does game format (2D vs 3D) affect learner proficiency in burst games?
4. Does prior domain knowledge influence learner proficiency (self-efficacy) or perceived ability?
5. How do the games impact self-efficacy or perceived ability?
3. Methods
The study was approved by the ASU IRB MOD00021448. A participatory design approach
between computer science students in capstone course sequences working with math education and
computer science faculty was used to develop the games. The games were iteratively developed by
several capstone teams. As a result, the research team was able to iterate and build the games with
very little cost. Two games titled Vector Unknown: 2D referred to as the “Bunny Game" and Vector
Unknown: 3D Echelon Seas referred to as the “Pirate Game" were developed over the duration of five
years. These two are hereby referred to in this article as game format, as being 2D or 3D.
3.1. Game Mechanics
VectorUnknown’s primary objective is to get the player’s character to a specified goal position. In
the “Bunny Game", the player character is a bunny while in the “Pirate Game" the player character is a
pirate. The objective is achieved by dragging and dropping two vectors into appropriate slots and
then adjusting the vector’s scalars to create a linear combination that can get the player to the goal.
The player is given a visual perspective of the game scene depending on the game format. There are a
total of 3 levels in both the games. In level 1 and level 2, players are shown a line as a guide (shown in
Figures 1and 2) to indicate the current output of their selected linear combination. In level 3, this line
is removed as an added challenge for the players. A typical play-through involves the player starting
the level, noting the goal position, trying out combinations of vectors and scalars until a solution is
found, and then pressing the appropriate button to begin the movement. Thus a single play-though
will take only a few minutes, conforming to the burst game paradigm. If the player reaches the goal
position as a result of this movement, then they win the level. The scalars that manipulate the chosen
vectors are handled via the “+” and “-” operators in the bunny game interface (Figure 1) or using
sliders in the pirate game interface (Figure 2). The game tracks every operation of the player including
the selection of vectors and the manipulation of scalars until they press the appropriate button (“GO”
button in bunny game and “Submit” button in pirate game) and push that data for analysis.
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Figure 1. The red line (guided path) showing the player trajectory in the bunny game
Figure 2. The guided path showing the player trajectory in the pirate game
There are a total of 3 levels in both the games with an increasing difficulty. Each level consists
of a mathematical puzzle in which a player must move their avatar to a target location. To solve
the puzzle, a player needs to drag and drop two vectors into appropriate slots and then adjust the
vector’s factors (scalars) to create a linear combination that can get the player to the goal position.
There are four available choices from which player can pick two. These choices consist of two pairs
of linearly dependent vectors, i.e. vectors which are multiple of each other. For example, in Figure 1,
the available choices are
<−
1, 0
>
,
<−
2, 0
>
,
<−
1, 1
>
,
<
6,
−
6
>
, with
<
6,
−
6
>
and
<−
2, 0
>
being selected by the player to reach the target position and their corresponding scalar coefficients
being -1 and 3 respectively. In Figure 1the choices
<−
1, 0
>
and
<−
2, 0
>
are linearly dependent
as
<−
2, 0
>
can be obtained by multiplying
<−
1, 0
>
with a scalar 2. Similarly,
<
6,
−
6
>
can be
obtained by multiplying
<−
1, 1
>
with -6. This is done intentionally to have students make linearly
independent vector choices when trying to solve the puzzles. The results of the player choices is the
vector
<−
12, 6
>
which is different from the goal of
<−
8, 6
>
shown in Figure 1. In the first level, at
least one of the vector pairs has x or y coordinate as zero, making it easier to reach the target position.
For example, in the Figure 1the pair
<−
1, 0
>
,
<−
2, 0
>
has y-coordinate as zero. In the second
level, there are no zeros in either of the pair choices. However, in both the first and second levels, there
is a guided path showing the trajectory that the player will follow on submitting the solution. In the
third level, this guided path is absent and there are not zeros in either of the vector choices. This would
push the player to use math instead of just guessing and checking the correct combination of scalars
and vector. All the three levels are repeated 4 times with different vector choices and target goal in
each repetition. These repetitions were termed as stages within levels. Thus, there were 3 levels with
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4 stages i neach level. These are listed in Appendix
??
for the bunny game and Appendix
??
for the
pirate game.
3.1.1. Bunny Game
Figure 1shows the user interface in the bunny game that features four main sections of heads up
display (HUD). A mini-map is located in the upper left corner of the interface, providing a top-down
view of the game scene. It displays the X-Y axis in the 2D format that students typically associate with
linear algebra. Next to the mini-map is the formula tab located on the right. Players can drag and
drop the vector choices in the two blank slots located in this tab. Each slot has a scalar coefficient that
players can adjust by using the plus and minus sign located below it. The results of player choices is
computed and shown to the right of the equals sign. On pressing the “GO” button, the bunny initiates
movement based on the result. Located below the formula tab is the game view-port which can be
rotated using the arrow keys to get a better view of the axis. The view-port consists of a 2D grid
with player and target locations mapped on it. The player character is depicted using a polygonal
bunny model and the target location using a basket of eggs. In level 1 and 2, the trajectory that the
bunny will follow based on the result is depicted using a red line. The path that has already been
traversed in previous steps (in case goal was not reached) is shown in green. The upper right corner of
the view-port displays the timer, showing the real time spent on the current level. To the left of the
view-port lies the vector choices tab that shows four vector choice tiles. These choices can be dragged
to the blank slots in the formula tab. It also displays the player ’s current position which is origin by
default (
<
0, 0
>
). The goal position (coordinates of egg basket) are also displayed here. The bottom of
this tab is used to track all the player choices
The game starts with a tutorial level (Figure 3) which is a brief walk-through of the game play
mechanics and user interface used in the game. On completing the walk-through, player begins the
level 1, followed by level 2 and level 3. Players can not skip a level and must complete them to advance
to the next level.
Figure 3. The tutorial level in the bunny game
3.1.2. Pirate Game
The 2D game evolved in a 3D format and followed similar design strategy, albeit adding an extra
dimension into the mix. A narrative was also added to the game where the player is a pirate who is
marooned on an island. By completing the levels the pirate can get the missing parts for his ship and
sail away from the island.
The game starts with a tutorial level as well which is a brief narrative walk-through with a parrot
named zeta (Figure 4a) who tells them that the pirates need to scale various chasms in order to collect
ship parts for rebuilding their ship. However, the tutorial level used only 1D vectors as opposed to 2D
vectors (Figure 4b). Zeta tells the players that they need to use combination of vectors and scalars in
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order to aim their grapple gun correctly to an anchored location. On completing the tutorial, player
begins the level 1, followed by level 2 and level 3. Players can not skip a level and must complete them
to advance to the next level. Once they cross all the chasms, they can obtain the sail needed for their
ship.
(a) Conversation with zeta (b) Tutorial game play
Figure 4. Tutorial level in the pirate game
3.2. Participants
The current study was conducted online with the help of zoom and qualtrics. The experiment
protocol was approved by the ASU IRB STUDY00018334 and the experiment was conducted in
accordance with the guidelines and regulations. A total of 45 participants completed the study.
Participants were invited to a shared zoom session led by the researcher who walked them through
the informed consent embedded in the qualtrics survey. Upon consenting, they filled the pre-survey
which asked them about their previous experience and comfort level with linear vector algebra. It
also asked them about their perceived skill (on a scale of 1-5) in how vectors and scalars work. Once
pre-survey is completed, participants were taken to the next page which had details and the link to the
game they will be playing. The order of play was randomized, therefore, some participants played the
bunny game followed by the pirate game while others played in the reverse order. On finishing both
the games, they were asked to fill a post-survey which asked them about their perceived skill after the
game play and feedback, if any.
Figure 5. The flowchart showing the participant workflow
Participants used their own computer for playing the game. Therefore, the game crashed some-
times for some players during the game play. Upon crash, their progress was not saved and they had
to replay the game from the start. Therefore, in such a case, only the data for their first play-through
was analysed. For example, if the game crashed while they were playing level 2.1, then the game play
data until the level 2.1 was considered for analysis. The second attempt play-through data until level
2.1 was discarded. Therefore their game play data consisted of first attempts data until 2.1 and second
attempt data from 2.1 to 3.4. There were a total of 16 participants whose 2D game data was corrupted
in this manner, 5 whose 3D game got corrupted and 2 for whom both got corrupted.
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3.3. Analytics to Support Learning Proficiency
A player can take multiple different approaches to reach the given target on a given level. Their
approach may lead them towards or away from the target location. The steps that take them towards
the target were classified as ideal steps and the steps that take them away from the target were classified
as non-ideal steps. We used an algorithm that uses player data to classify a particular step as ideal
or non-ideal. We provided four vectors as possible options. Of these four, two vectors were linearly
dependent on the other two. Therefore, we presented two pairs of linearly dependent vectors. Let’s
say the two vectors were
A=<n
,
m>
and
B=<p
,
q>
. Then their linearly dependent counterparts
were
C=kA
and
D=jB
, Where
n
,
m
,
p
,
q
,
k
,
j
are scalars. The target position was generated so that
it could be obtained by operating on the two linearly independent vectors. The goal position can be
represented mathematically as
xC +yD
, where
x
and
c
are scalars. Depending on the vectors and
scalars chosen by the student, their step could be ideal or non-ideal. The step that they took could be
non-ideal for the following reasons:
• If the step involved two linearly dependent vectors, i.e. choosing both A,Cor B,D.
–Aand Care chosen as vectors
–Band Dare chosen as vectors
•
If the scalar is chosen such that it takes them away from the goal position. For example, if they
choose
A
as an option and its corresponding scalar is adjusted in the direction opposite to where
it should go. If the sign of the scalar is opposite to the sign of xk
–sign of vector A’s scalar is different from sign of xk
–sign of vector B’s scalar is different from sign of x
–sign of vector C’s scalar is different from sign of yj
–sign of vector D’s scalar is different from sign of y
This algorithm helps instructors determine whether students are guessing their solutions or
attempting to cognitively use math while completing the levels. While Level 1 and 2 provides guided
lines, the expectation is that students would easily correct their paths as soon as they see the direction
they are headed. Level 3 would limit their guess and check abilities and they would have to rely more
on their mental math skills.
4. Analysis
The raw data collected from the game consisted of the choices of scalars and vector at the every
step. This raw data was used to determine if a particular step is ideal or not. The processed data
consisted of total number of ideal and non-ideal steps used on a given level, which was used to
compute the percentage of ideal steps (PIS) on that level. The learning proficiency was operationalized
using this measure. Thus a higher percentage of ideal steps on a given level would indicate better
learning proficiency as compared to a lower percentage.
4.1. Burst Game Effectiveness
The burst game effectiveness was evaluated for each level in both the games. In order to do so,
the PIS was compared across the four stages within each level. A higher PIS in later stages would
indicate the effectiveness of the burst game paradigm. Thus a total of 6 ANOVAs were carried out, 3
for each game, 1 for each level within a game.
4.2. Effect of Game Complexity
The levels became more complex as players progressed through the game. Level 1 was the least
complex while level 3 was the most complex. PIS was averaged across the four stages of a given level
to compute the average PIS for that level. The average PIS was then compared across the three levels
in the game to evaluate the effect of complexity on the player proficiency. A total of two ANOVAs
were conducted, 1 for each game format.
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4.3. Effect of Game Format
PIS was averaged across all the three levels and four stages of each level to compute the average
PIS for the overall game. The games were played in random order, where 29 participants played the
2D game followed by the 3D game while 16 participants played the 3D game followed by the 2D game.
The order was considered another factor in this analysis and the comparison was conducted using
the repeated-measures ANCOVA with order as between-subject and average PIS (2d vs 3d) as the
within-subject factor.
4.4. Effect of Prior Knowledge
The pre-survey asked the participants to rate their perceived skill in the knowledge of vectors
and scalars on a scale of 1 to 5. The average PIS calculated in the previous step is compared across
these five groups to evaluate the effect of prior knowledge on the learning effectiveness.
5. Results
5.1. Burst Game Effectiveness
The mean PIS (
¯
X
) and standard deviation (
sx
) of individual stages within levels was computed
and indicated in Table 1. Table 2lists the results of the 6 ANOVAs carried out, one for each level of the
bunny and pirate games. These comparisons indicated that repetition caused significant performance
different between the stages of level 2 in the bunny game and the level 1 & 3 of the pirate game.
Figure 6shows the box plots of different levels within these games. Box plot for the bunny game’s
level 2 suggest that the median PIS was least in the stage 1 of this game and it improved in stage 2
while it was similar between stage 2 and 3. Stage 4 saw the maximum median PIS among the 4 stages
of bunny game’s level 2.
Table 1. Mean and standard deviation of levels and stages in the games
2D 3D
Level Stage ¯
X sx¯
X sx
1
1 75.73 23.52 68.15 25.97
2 78.03 21.14 71.12 21.78
3 84.45 17.01 76.15 23.60
4 80.18 19.90 78.36 21.00
2
1 72.43 20.80 72.44 24.23
2 80.41 18.91 76.57 21.89
3 81.15 21.98 70.14 23.07
4 85.83 21.44 67.74 24.12
3
1 77.83 18.09 67.19 23.78
2 76.24 18.56 72.27 22.86
3 73.83 22.65 79.96 22.83
4 83.00 20.18 81.44 21.30
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Figure 6. Box plots for bunny and pirate game’s individual levels
Table 2. ANOVA results for comparing percent ideal steps within different stages of various game
levels
Game format Level F(1, 44)pη2
p
2D
1 1.88 .177 0.04
2 10.28 .003** 0.19
3 1.45 .235 0.03
3D
1 6.65 .013* 0.13
2 2.35 .132 0.05
3 13.02 .001*** 0.23
The pirate game’s level 1 also demonstrated similar results where the median PIS was least in
stage 1 and the most in stage 4, with stage 3 and 4 demonstrating similar PIS. The plots for level 3 of
the pirate game also demonstrate similar results. However, there was no significant PIS difference
within the rest of the bunny and pirate game levels which is also reflected in their box plots.
5.2. Effect of Game Complexity
No significant difference was observed in the mean PIS for the 2D game,
F(
2, 88
) =
.5632,
p=
.572,
η2
p=
.01. There was no significant difference in PIS for the 3D game either,
F(
2, 88
) =
1.5602,
p=
.216,
η2
p=
.03. Figure 7shows the box plots for bunny and pirate game levels which supports these findings.
These results suggest that the level complexity did not affect the learner performance ad there was no
significant difference in PIS across different game levels.
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Figure 7. Averaged box plots for bunny and pirate game levels
5.3. Effect of Game Format
There was no significant interaction between the order and the game format,
F(
1, 43
) =
.82,
p=
.371,
η2
p=
.019. However, a significant difference due to the game format,
F(
1, 43
) =
8.57,
p=
.005,
η2
p=
.166. Subsequent post-hoc tests reveled no significant difference in the PIS due to order,
F(1, 43) = 0.23, p=.633, η2
p=.005. This suggests that it did not matter if the participants played the
2D game followed by the 3D game or in the reverse order. From the box plots in Figure 8it appears
that the median PIS was higher in the 2D game as compared to the 3D game.
Figure 8. Averaged box plots for games based on game format and game-play order
5.4. Effect of Prior Knowledge
There was no significant difference observed in the 2D,
F(
4, 40
) =
2.419,
p=
.064,
η2=
.20, and
3D game,
F(
4, 40
) =
2.116,
p=
.097,
η2=
.17 performance due to prior score. Figure 9shows the box
plots for average PIS across the 5 groups of participants who indicated their prior knowledge ranging
from 1 to 5. However, the distribution of the groups was uneven across the 5 groups as indicated in
the histogram in Figure 10.
Figure 9. Averaged box plots for bunny and pirate game based on prior knowledge
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Figure 10. Histogram for bunny and pirate game based on prior knowledge
6. Discussion and Limitations
This study investigated the impact of educational burst games (EBGs) on learning proficiency,
examining factors such as game complexity, game format, and prior knowledge. Our findings revealed
mixed support for the hypothesis that quick, repetitive gameplay enhances learner proficiency, aligning
with the first principle of learning as stated by Bruner [
26
]. Some levels of the bunny and pirate game
support this hypotheses while others do not. Of the 3 levels in bunny game, 1 support it while 2 out of
3 pirate game levels support it. Specifically, the increased proficiency observed in certain levels of the
bunny and pirate games suggests that EBGs can indeed foster learning through repetitive gameplay
mechanics. This is consistent with the work of SchimankeAuthor et al [
27
], who emphasized the
value of repetition in educational games. However, they suggested that it might be better to space out
the repetition and the games be designed with the spaced repetition approach instead of continuous
repetition.
Contrary to our initial assumptions, the complexity of game levels did not significantly affect learn-
ing outcomes. This result challenges the common belief that more complex educational games provide
better learning opportunities, suggesting instead that the core mechanics of burst games—regardless of
complexity—sufficiently engage learners and contribute to proficiency. This finding are comparable to
the conclusions of Wolfe [
28
], who reported that the learning is not linearly positively impacted by the
game complexity. However, the results largely depend on how the complexity was manipulated and
therefore these results must be interpreted with caution, considering the complexity implementation
strategy in the game.
Our study also highlighted the superior effectiveness of 2D game formats over 3D formats in
promoting learner proficiency irrespective of the order in which they were played. This is aligned with
the Mayer and Moreno’s [
23
] Cognitive Theory of Multimedia Learning and supports the argument
that simpler visual presentations in educational materials can facilitate better cognitive processing and
learning outcomes. Thus, future game designs might benefit from prioritizing clarity and accessibility
of content presentation over more complex, visually rich environments.
Interestingly, prior knowledge did not significantly influence learning outcomes within the game
settings. This suggests that EBGs have the potential to level the playing field for learners with varying
degrees of background knowledge, providing a valuable tool for inclusive education. This aligns with
Premlatha and Geetha’s [
29
] findings on the adaptability of digital learning tools to diverse learning
needs. However, this result is based on the self reported measure instead of an external assessment.
Our study’s insights are tempered by its limitations, including the small sample size and the
reliance on self-reported measures of proficiency. Future research should aim for larger, more diverse
participant pools and incorporate external assessments of learning outcomes to validate and expand
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upon our findings. Moreover, investigating the long-term retention of knowledge gained through
EBGs could provide deeper insights into their educational value.
7. Conclusion
This study provides valuable insights into the effectiveness of educational burst games (EBGs)
in enhancing learning outcomes across different subjects. Our findings highlight that while game
complexity and prior knowledge do not significantly impact learning efficiency, the format of the game
and its repetitive, engaging nature play crucial roles in educational achievement. The preference for
2D over 3D formats suggests a need for simplicity and focus in educational game design, emphasizing
content delivery over graphical sophistication. Despite limitations, including a small sample size,
this research underscores the potential of EBGs as accessible, inclusive tools for education. Future
investigations should aim to broaden participant diversity, assess long-term knowledge retention,
and refine game designs to maximize educational benefits. Through continued exploration, EBGs
can significantly contribute to the evolving landscape of digital education, offering dynamic, effective
learning experiences for diverse learners.
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