IAFOR Journal of Education: Studies in Education

Volume 9 – Issue 6 – 2021

Effects of Gamified Learning on Students of Different Player Traits in
Malaysia
Mageswaran Sanmugam
Universiti Sains Malaysia (USM), Malaysia
Anurita Selvarajoo
Nottingham University Malaysia
Jeya Amantha David
Universiti Sains Malaysia (USM), Malaysia

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Abstract
Diversified learning is the path to supplement students’ needs in the contemporary generation.
These students’ lives have revolved around technology since birth; as such, the role oftechnology cannot be ignored. Furthermore, this was prevalent during the lockdown imposedby the global pandemic which compelled the incorporation of educational technology into
student’s lives. As gamification harnesses the power of game elements, identifying how
gamified learning affects a student’s game player traits will be vital in identifying whether
specific learning methods can invoke, change and cultivate better learning outcomes. Thisquasi-experimental study involving two groups of students learning computer science in
Malaysia was carried out over eight weeks. Findings revealed that most prevalent player traits
changes were evident in the primary construct of social player traits, followed by subconstructs
of customization, relationship, socializing, and mechanics. These changes are attributed to theneed to reach out, communicate, and collaborate with their peers and look into how the system
works for them individually, within the context of the learning and explorative needs ofstudents. As such, gamified learning has not only managed to offer a new paradigm into thelearning ecosystem but has also shown that positive changes can be cultivated based on these
conditions.
Keyword: educational technology, gamification, diversified learning, learning ecosystem

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As the world soldiers on past the restrictions imposed by the COVID-19 pandemic; many fields
have turned to harness technology to ensure their survival and continuity in their respective
fields (Mohd Nasir et al., 2021; Pongsakornrungsilp et al., 2021; S. Rashid & Ratten, 2020;
Wagner, 2021). In the field of education, although an adaptation of technology has been
gradually taking place in the past few years (Tomlinson, 2018), the sudden need for emergency
remote teaching (Hodges et al., 2020; Schlesselman, 2020) to supplement the lessons taking
place during lockdown has been dependent on technological innovations that were placed on
the back burner before this scenario (Eradze et al., 2021). From learning management systems(Başal & Kaynak, 2020) to video conferencing tools (Correia et al., 2020; Gillies, 2008;
Lawson et al., 2010; Martin, 2005), everything available has been put up front to ensure it isused to supplement and support the learning process. However, simply using a primary LMS
platform may not be sufficient for the students from Generation Z (Kasasa, 2020; Widodo etal., 2020), as their needs may be different since their lives have been revolving aroundtechnologies since the day they were born. Although sophisticated and advanced technology
like Mixed Reality or Augmented Reality might be suitable for simulation-based education(Eradze et al., 2021), it not be suitable to be implemented within the context of mainstream
education, as this may require workforce training; as well as the equipping of students with thenecessary tools. The most readily available tools for diversification are gamified learning tools
such as Kahoot, Quizizz, and others, platforms which are freeware and are easily accessible.
Gamification-based learning is implemented to cater to the educational, technological learning
process, which allows for the integration of technology and education laced with gameelements to facilitate the learning process (Deterding & Dixon, 2011). By diversifying themethod of learning, it is also hoped that these methods and the elements can trigger as well as
cultivate player motivation that exists within a user (Monterrat et al., 2015; Mageswaran
Sanmugam, Abdullah, Mohd Zaid, et al., 2016; Schoenau-Fog & Henrik, 2014). Thus, thepurpose of this study will be to identify internal player traits that exist in students using
gamification in learning and exploring the relationship between the player traits, gameelements, achievement, and engagement levels.
Implementation of gamification on the teaching and learning process has been widely shown
to positively impact the student’s achievement (Wolf et al., 2018) and engagement levels(Molnar, 2018; M. Rashid & Suganya, 2017). As gamification uses game elements in a nongaming context, students tend to relate the experiences of acquiring game elements as a part of
the gaming experience. Therefore, game player traits or player motivation will play a vital role
in regulating a better-suited learning model for students. Thus, eliminating the one size fits allmodel implemented in technology-based learning.
Literature Review
Infusing technology into the traditional classroom is the next phase or next upgrade in theteaching and learning method. Nevertheless, treating technology as an add-on to the learning
process is a mistake as it leads to the students being disconnected (Selwyn, 2006) and bored(Craig et al., 2004), and their expectation of technology may be different from what is being
offered (Sleeman et al., 2020). Hence, just absorbing technology in education has not reaped
the same effects as games or using social networking services such as Facebook, Google+,
Twitter, and Myspace (Boyd & Ellison, 2007) as when it comes to games, it is something
intriguing (Cheng et al., 2015) and for some, it is part of their daily routines (Gardner & Eng,
2005; McGonigal, 2011). Therefore, with this arises the need to consider an approach that
fulfills the needs of various students and allows them to be immersed in the learning process.

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Finding a suitable tool that caters to the need of all users is near impossible, although finding
a common mousetrap may be the best solution in hand – as such, using games as a tool to
supplement learning may be the next best choice. Although game-based learning has long been
proven to have a significant impact on learners achievement, cognitive and social development(Prensky, 2001; Sung & Hwang, 2013), it is to be noted that the creation of a full-fledged gamefor a certain subject or subtopic, especially digital games are beyond reach for an educator(Muntean, 2011). Despite the use of game generators to invoke students’ critical thinking
which are readily available now (Meishar-Tal & Kesler, 2021), they are yet to teach targeted
topics. This approach is known as gamification – the use of game elements instead of creating
the whole game itself (Deterding et al., 2011) – is the next best alternative for educators andthe organization.
In the context of Malaysian education, gamification has been used to investigate the feasibilityof usage (Ong et al., 2013), offline usage in teaching (Hong & Masood, 2014), renderinglearning explicit while retaining fun factors (Tan et al., 2014), student attitude and acceptancetowards usage in learning (Fah et al., 2016), how game elements affected their learning process(Mageswaran Sanmugam, Abdullah, Mohamed, et al., 2016), using analogue gamification toenhance learners attitude (Mee Mee et al., 2021). However, there was a need to look extensively
into implementing gamification, where the identifying specialized need of the users will help
understand and identify personalized learning to cater to students within the Malaysian
education system.
When it comes to playing games, many types of player needs or motivation represent theirplayer traits when engaged in a game (Deterding et al., 2011). Although in gamification, Iosup& Epema, (2014) looked into gamification using Bartle’s player motivation scale (Bartle, 1996)
and reported a high success rate, what seemed to bring dispute is the change in describing theplayer traits. A revised version of Bartle’s player motivation scale by Yee (2006) considers theplayer motivation scale for online platform gamers compared to Bartle’s player motivation
scale created based on multi-user dungeon (MUD) gamers. The usage of player traits as an
early indication system to identify the individual needs of students will help future researchers
in either game-based learning, serious games, or gamification. Yee (2006) carried out researchwith 3000 multiplayer online role-playing games (MMORPG); based on 40 questionnaires(quantitative study), streamlined the previous research findings and came up with three main
player traits; Achievement, socializer, and immersion. Users under the Achievement spectrum
tend to get satisfaction gaining or achieving something within the gaming scenario. Users under
the Socializer spectrum, on the other hand, find satisfaction in connecting with others.
Moreover, finally, users under the Immersion spectrum strive to submerge themselves and their
gaming persona within the game’s lore.
The achievement player traits can be further expanded into 3 sub-components:
1. Advancement: – players/students who want to progress or gain something in their tasks
or learning process
2. Mechanics: – players/students who prefer to explore the elements of the game or system
to improve their learning task performances
3. Competition: players/students who use the challenge as the goal to achieve in theirlearning tasks

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The Socializer player traits have 3 sub-components:
1. Socializing: – players/students who like to communicate and help otherplayers/students
2. Relationship: – players/students who would like to create a bond with otherplayers/students
3. Teamwork: – players/students who like to collaborate to achieve the game goals
The Immersion player traits has 3 sub-components:
1. Discovery:– players/students who like to find something unique or new that others do
not find in a game or learning tasks given to them
2. Role-playing: – players who like to create an imaginary persona in a game or learningtasks
3. Customization: – players who like to change and modify the gaming persona that they
have in a game or learning tasks
With the Gen-Z students being born into a world filled with technology (Fister Gale, 2015b),technology in entertainment, especially games, is not a significant surprise. Thus, instead oflooking at the drawbacks of games and weeding them out, it would be more meaningful ifinterest in these games is adequately cultivated, to better suit educational outcomes. Many
researchers have stated that harnessing the power of games can lead to not only meaningfullearning (Muntean, 2011) but also engaging them to the lesson (Farhangi, 2012; Lister, 2015;
Meluso et al., 2012; Morrison & DiSalvo, 2014; M. Sanmugam et al., 2017) as well as
encouraging autonomous learning among the students (Jang, 2008). Nevertheless, before thislevel can be achieved, it will be essential to identify the explicit interest of these users to allow
the creation of a personalized path of learning as it has to be noted that technological pedagogy
in learning is not a one size suits all affair. Therefore, this current study used Yee’s player
motivation scale as it is a revised version of Bartle’s player motivation scale, and it was basedon online platform game player motivations. Besides that, Yee’s player motivation will besuitable as it helps map out players’ traits in participating in the gamified learning process.
Methodology
This study will implement a quasi-experimental study that applies quantitative methods (Cohenet al., 2007; Creswell, 2012). Convenience sampling was utilised (Creswell, 2012), and 53
participants completed the pre and post-intervention, with 25 participants from the treatmentgroup and 28 participants from the control group. The research process began with theadministration of the questionnaires involving both control and experimental group students pre-intervention to identify the levels of player types before any intervention. Then both theexperimental and control group students proceeded with their learning process; with theexperimental group learning using a gamified e-learning method using Kahoot (exercises will
be carried out in this platform), while, the control group learned with non-gamified learning
using Google Classroom (The google classroom will be used to only share exercises of thelessons). Upon completion of the second topic, a questionnaire was administered again to see
whether the learning process influenced the player types.
The selection criterion for the participants were students from boarding schools who werethemselves selected and enrolled into these learning ecosystems based on their excellence inacademics (Khalidah et al., 2014; Noriah Mohd Ishak, Ramlee Mustapha, 2006). As such, a93

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boarding school from the Southern state of Malaysia was chosen for the control and treatmentgroup. The selection was also based on the availability of a computer laboratory. This is toeliminate factors of technology influence and experience among students. Besides that, toreduce learning style bias and impact on the learning process, both control and experimental
batch were taught by teachers with the same level of teaching experience, between 5 to 10
years, which is sufficient to teach the subject properly (King Rice, 2010). For confidentiality
purposes, the schools’ identities are not revealed in this research. The data collection process
was carried out over nine weeks.
The instrument /questionnaire which was administered in this research included the Game
Player Types Inventory by Yee (2006), based on the technical specifications, the factor
loadings of the survey items used are as follows: Advancement (Cronbach’s α=.79), Mechanics(Cronbach’s α = .68), Competition (Cronbach’s α = .75), Socializing (Cronbach’s α = .74),
Relationship (Cronbach’s α = .80), Teamwork (Cronbach’s α = .71), Discovery (Cronbach’s α= .73), Role-play(Cronbach’s α = .87), Customization (Cronbach’s α = .74), and Escapism(Cronbach’s α = .65). Based on the principal components analysis the 10 factors were extractedwith eigenvalues greater than 1. Together, these factors accounted for 60% of the overallvariance.
The administration of the instruments was done in dual language (English and Bahasa Melayu)
as the Malay language is the mother tongue for most of the students, and this will create easy
access and responses by the participants. The instruments were rated using a Likert scaleranging from 1 (Strongly Disagree) to 5 (Agree) and was administered using SurveyMonkey.
As this study used a quasi-experimental design, the Pre- and Post-test design was applied tomonitor the change in the measures throughout the study. The use of a control group in thisresearch enhanced the ability to distinguish the effects of the intervention. Secondly, as there
was multiple observation, this method did not require exploring a large number of samples.
The design notation of the study is shown in Figure 1.
Figure 1
Design Notation of the Study
Topic 1
Group 1
Group 2

Time
Obs
Obs

LegendObs
Tx

Measurement
Treatment

Tx

Obs
Obs

The research utilized the mean analysis, paired sample tests, and independent-sample t-tests,
supplemented by Cohen D’s effect size. For the latter, Table 1 reveals the individual effectsizes.

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Table 1
Cohen D’s Effect Size
Relative Effect Size
Small
MediumLarge

Effect Size
0.2
0.5
0.8
Findings

For the mean analysis of the three main player traits, Achievement, Social, and Immersion,
among the control group pre-and post-intervention, achievement player traits for postintervention were higher (mean=3.712) than the Pre-intervention phase (mean=3.610).
Meanwhile, the Social player traits for post-intervention were higher (mean=4.223) than the
Pre-intervention phase (mean=3.679). Finally, the Immersion player traits for post-intervention
were higher (mean=3.770) than the Pre-intervention phase (mean=3.758).
For the mean analysis of the ten-player traits sub-constructs, Advancement, Mechanics,
Competition, Socializer, Relationship, Teamwork, Discovery, Role-playing, Customization,
and Escapism; among participants from the control group pre-and post-intervention; it can beseen that Advancement sub-construct for the post-intervention score was higher (mean=3.935)
than the Pre-intervention phase (mean=3.839). The Mechanics sub-construct for the postintervention score was higher (mean=3.955) than the Pre-intervention phase (mean=3.768).
The Competition sub-construct for the post-intervention score was higher (mean=3.134) than
the Pre-intervention phase (mean=3.107). The Socializer sub-construct for the postintervention score was higher (mean=4.223) than the Pre-intervention phase (mean=3.946).
The Relationship sub-construct for the post-intervention score was higher (mean=3.560) than
the Pre-intervention phase (mean=3.321). The Teamwork sub-construct for the postintervention score was higher (mean=3.821) than the Pre-intervention phase (mean=3.679).
The two mean values reveal that participants’ Discovery sub-construct for the post-intervention
score was higher (mean=3.839) than the Pre-intervention phase (mean=3.679). The Roleplaying sub-construct for post-intervention score was higher (mean= 3.813) than the Preintervention phase (mean=3.750). The Customization sub-construct for the post-intervention
score was lower (mean=3.762) than the Pre-intervention phase (mean=4.000). The two meansof Escapism sub-construct for the post-intervention score was similar to the Pre-intervention
phase (mean=3.631)
Meanwhile, the results of the repeated-measures T-test, in n Table 2, for primary constructs ofthe player traits and Table 3, for sub-constructs of the player traits, note that none of theconstructs were found to be statistically significant at the 0.05 level.

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Table 2Paired Sample Test Statistics for the Control Group Pre- and Post-Intervention for Main
Constructs
Main Construct (Control)
1
2
3

Paired Samples Test
Paired Mean
Differences

Achievement(Pre) –Achievement(Post)
Social (Pre) -Social (Post)
Immersion (Pre) - Immersion(Post)-0.102-0.218-0.013tdf

Sig. (2tailed)-0.762-1.990-0.123

27
27
27

0.452
0.057
0.903

Table 3
Paired Sample Test Statistics for the Control Group Pre- and Post-Intervention for the SubConstructs
Sub-Construct (Control)
1
2
3
4
5
6
7
8
9
10

Paired Samples Test
Paired Mean
Differences

Advancement (Pre) -Advancement(Post)
Mechanics (Pre) - Mechanics(Post)
Competition (Pre) - Competition(Post)
Socializer (Pre) - Socializer (Post)
Relationship (Pre) - Relationship(Post)
Teamwork (Pre) - Teamwork(Post)
Discovery (Pre) - Discovery (Post)
Role-playing (Pre) - Role-playing(Post)
Customization (Pre) - Customization(Post)
Escapism (Pre) - Escapism (Post)tdf

Sig. (2tailed)-0.095-0.57527

0.570-0.188-0.027-1.549-0.113

27
27

0.133
0.911

-0.277-0.238-1.792-1.842

27
27

0.084
0.077

-0.143-0.161-0.063-1.008-1.288-0.482

27
27
27

0.322
0.209
0.634

0.238

1.376

27

0.180

0.000

0.000

27

1.000

The next phase of the analysis looks into the treatment group. For the mean analysis of thethree main player traits, Achievement, Social, and Immersion, the achievement level for postintervention was lower (mean=3.600) than the Pre-intervention phase (mean=3.671).
Meanwhile, social player traits for post-intervention were lower (mean=3.593) than the Preintervention phase (mean=3.687). Finally, the Immersion player traits for post-intervention
were lower (mean=3.629) than the Pre-intervention phase (mean=3.683).
For the mean analysis of the ten-player traits sub-constructs, Advancement, Mechanics,
Competition, Socializer, Relationship, Teamwork, Discovery, Role-playing, Customization,
and Escapism; some differences were evident between participants from the control group preand post-intervention. From the two means, participants’ Advancement sub-construct for the
post-intervention score was lower (mean=3.787) than the Pre-intervention phase(mean=3.860). The Mechanics sub-construct for the post-intervention score was lower(mean=3.600) than the Pre-intervention phase (mean= 3.680). The Competition sub-construct

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for the post-intervention score was lower (mean=3.320) than the Pre-intervention phase(mean=3.380).
From the two means for the Socializer sub-construct, the post-intervention score was lower(mean=3.820) than the pre-intervention phase (mean=3.880). The Relationship sub-construct
for the post-intervention score was lower (mean=3.053) than the Pre-intervention phase(mean=3.573). The two means of the Teamwork sub-construct for the post-intervention score
were higher (mean=3.770) than the pre-intervention phase (mean=3.580).
The Discovery sub-construct for the post-intervention score was lower (mean=3.510) than the
Pre-intervention phase (mean=3.850). The Role-playing sub-construct for the post-intervention
score was lower (mean=3.710) than the Pre-intervention phase (mean=3.740). The
Customization sub-construct for post-intervention score was higher (mean=3.560) than Preintervention phase (mean= 3.480). The Escapism sub-construct for the post-intervention score
was higher (mean=3.747) than the Pre-intervention phase (mean=3.587).
Meanwhile, when it came to the results of the repeated-measures t-test, as seen in Table 4, it
was found that none of the primary constructs was significant at the 0.05 level or the 0.1 level.
Meanwhile, according to Table 5, the relationship sub-construct was significant, t(25) =2.564,
p<0.05.
Table 4Paired Sample Test Statistics for the Treatment Group Pre- and Post-Intervention for theMain Constructs
Main Construct1
2
3

Paired Samples Test
PairedMean
Differences

Achievement (Pre) - Achievement(Post)
Social (Pre) - Social (Post)
Immersion (Pre) - Immersion (Post)

97

t

df

Sig. (2tailed)

0.071

0.666

24

0.512

0.095
0.054

0.851
0.496

24
24

0.403
0.625

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Table 5Paired Sample Test Statistics for the Treatment Group Pre-and Post-Intervention for the SubConstructs
Sub-Construct
1
2
3
4
5
6
7
8
9
10

Paired Samples Test
Paired Mean
Differences

Advancement (Pre) -Advancement(Post)
Mechanics (Pre) -Mechanics (Post)
Competition (Pre) -Competition (Post)
Socializer (Pre) - Socializer (Post)
Relationship (Pre) -Relationship (Post)
Teamwork (Pre) -Teamwork (Post)
Discovery (Pre) -Discovery (Post)
Role-playing (Pre) -Role-playing (Post)
Customization (Pre) -Customization(Post)
Escapism (Pre) -Escapism (Post)tdf

Sig. (2tailed)

0.073 0.556

24

0.584

0.080
0.060
0.060
0.520
-0.1900.340
0.030-0.080

0.684
0.309
0.458
2.564
-1.5272.006
0.277-0.503

24
24
24
24
24
24
24
24

0.501
0.760
0.651
0.017
0.140
0.056
0.784
0.620

-0.160 -0.681

24

0.503

Based on Tables 6 and 7, for the construct of Achievement, participants from the Pre-control
group (mean=3.610) showed lower traits tendencies compared to the Pre-Treatment group(mean=3.671). For the construct of Social, participants from the Pre-control group(mean=3.679) showed a lower tendency than the Pre-Treatment group (mean=3.687).
Participants from the Pre-control group (mean=3.758) showed higher Immersion Player traits
tendencies than the Pre-Treatment group (mean=3.683). This difference was not significant for
all three traits.
Table 6
Mean Analysis from the Treatment Group Pre-Intervention (Control/Treatment) for the Main
Constructs
Main Construct
Achievement
SocialImmersion

Mean Analysis
Group
N
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment

98

28
25
28
25
28
25

Mean
3.610
3.671
3.679
3.687
3.758
3.683

Std. Deviation
0.490
0.476
0.388
0.463
0.442
0.522

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Table 7
Independent Sample test for the pre-intervention (Control/Treatment) for Main Constructs
Main Constructs

Achievement
SocialImmersion

EVA
EVNA
EVA
EVNA
EVA
EVNA

Independent Samples Test
Levene’s Test for
t-test for Equality of Means
Equality of Variances
F
Sig.
t
df
Sig. (2tailed)
0.007
0.933 -0.464
51.0000.644-0.46550.6220.644
1.091
0.301 -0.074
51.0000.941-0.07447.1140.942
1.738
0.193 0.565
51.0000.575
0.559
47.2890.579

Key: EVA: Equal variances assumed; EVNA: Equal variances not assumed

Based on Table 8 and Table 9 below, on average, participants from the Pre-control group(mean=3.839) showed lower Advancement Player traits tendencies than the Pre-Treatmentgroup (mean=3.860). Meanwhile, when it came to Mechanics Player traits, participants fromthe Pre-control group (mean=3.768) showed a higher tendency than the Pre-Treatment group(mean=3.680). For Competition Player traits, participants from the Pre-control group(mean=3.107) showed a lower tendency than the Pre-Treatment group (mean=3.380). Thisdifference was not significant for all the sub construct traits.
For the Socializer player traits, participants from the Pre-control group (mean=3.946) showed
higher traits tendencies than the Pre-Treatment group (mean=3.880). For the Relationshipplayer traits, participants from the Pre-control group (mean=3.321) showed lower traits
tendencies than the Pre-Treatment group (mean=3.573). Meanwhile, participants from the Precontrol group (mean=3.679) showed higher Teamwork Player traits tendencies compared to
the Pre-Treatment group (mean=3.580). This difference was not significant for all the subconstruct traits. Participants from the Pre-control group (mean=3.679) showed lower
Discovery Player traits tendencies compared to Pre-Treatment group (mean=3.850).
Participants from the Pre-control group (mean=3.750) showed a higher Roleplaying Player
traits tendencies compared to Pre-Treatment group (mean=3.740). Participants from the Precontrol group (mean=4.000) showed a higher Customization Player traits tendencies comparedto Pre-Treatment group (mean=3.480). This difference was significant t (51)=2.820, p<.05; andrepresenting a medium-sized effect size of r=0.78. Participants from the Pre-control group(mean=3.631) showed higher Escapism Player trait tendencies compared to the Pre-Treatmentgroup (mean=3.587).
Table 8Mean Analysis from the Treatment Group Pre-Intervention (Control/Treatment) for the SubConstructs
Sub-Construct
Advancement
Mechanics

Mean Analysis
Group
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment

99

N
28
25
28
25

Mean
3.839
3.860
3.768
3.680

Std. Deviation
0.479
0.560
0.531
0.503

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Competition
Socializer
Relationship
Teamwork
Discovery
Roleplaying
Customization
Escapism

Volume 9 – Issue 6 – 2021

Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment
Pre-Control
Pre-Treatment

28
25
28
25
28
25
28
25
28
25
28
25
28
25
28
25

3.107
3.380
3.946
3.880
3.321
3.573
3.679
3.580
3.679
3.850
3.750
3.740
4.000
3.480
3.631
3.587

0.837
0.851
0.602
0.733
0.517
0.697
0.518
0.562
0.466
0.505
0.549
0.557
0.660
0.681
0.843
1.164

Table 9
Independent Sample test for the pre-intervention (Control/Treatment) for Sub- Constructs
Independent Samples Test
Sub- Constructs
Levene’s Test for
t-test for Equality of Means
Equality of
Variances
F
Sig.
t
df
Sig. (2tailed)
Advancement
EVA
0.218
0.643 -0.145
51.0000.885
EVNA-0.14447.5380.886
Mechanics
EVA
0.000
0.983 0.616
51.000
0.540
EVNA
0.618
50.8130.539
Competition
EVA
0.171
0.681 -1.175
51.0000.245
EVNA-1.17450.1250.246
Socializer
EVA
1.312
0.257 0.362
51.000
0.719
EVNA
0.358
46.6080.722
Relationship
EVA
0.794
0.377 -1.504
51.0000.139
EVNA-1.47943.9340.146
Teamwork
EVA
0.644
0.426 0.664
51.000
0.509
EVNA
0.661
49.0770.511
Discovery
EVA
0.076
0.783 -1.285
51.0000.204
EVNA-1.27949.1090.207
Roleplaying
EVA
0.053
0.818 0.066
51.000
0.948
EVNA
0.066
50.151
0.948
Customization
EVA
0.562
0.457 2.820
51.000
0.007
EVNA
2.815
49.9340.007
Escapism
EVA
4.335
0.042 0.160
51.000
0.874
EVNA
0.157
43.3050.876
Key: EVA: Equal variances assumed; EVNA: Equal variances not assumed

Based on Table 10 and Table 11, participants from the post-control group (mean=3.712)
showed higher Achievement Player trait tendencies than the post-Treatment group100

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(mean=3.600). Participants from the Post-control group (mean=3.770) showed higher
Immersion Player trait tendencies than the post-Treatment group (mean=3.629); both traits
were not significant. Meanwhile, participants from the post-control group (mean=3.896)
showed higher Social Player traits tendencies than the post-Treatment group (mean=3.593).
This difference was significant t(51)=2.813, p<.05; and a small-effect size of r=0.32.
Table 10
Mean Analysis from the Treatment Group Post-Intervention (Control/Treatment) for the Main
Constructs
Main Construct
Achievement
SocialImmersionGroup
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment

Mean Analysis
N
28
25
28
25
28
25

Mean
3.712
3.600
3.896
3.593
3.770
3.629

Std. Deviation
0.529
0.439
0.391
0.393
0.488
0.429

Table 11
Independent Sample Test for the Post-Intervention (Control/Treatment) for Main Constructs
Main Construct

Achievement
SocialImmersion

EVA
EVNA
EVA
EVNA
EVA
EVNA

Independent Samples Test
Levene’s Test for
t-test for Equality of Means
Equality of Variances
F
Sig.
t
df
Sig. (2tailed)
0.573
0.453
0.831
51.0000.410
0.839
50.754
0.405
0.019
0.891
2.813
51.0000.007
2.813
50.271
0.007
0.681
0.413
1.118
51.0000.269
1.126
50.9920.265

Key: EVA: Equal variances assumed; EVNA: Equal variances not assumed

Based on Table 12 and Table 13, on average, participants from the post-control group(mean=3.935) showed higher Advancement Player traits tendencies than the post-Treatmentgroup (mean=3.787). Participants from the Post-control group (mean=3.134) showed lower
Competition Player traits tendencies than the post-Treatment group (mean=3.320); both traits
were not significant. Meanwhile, participants from the post-control group (mean=3.955)
showed higher Mechanics Player traits tendencies than the post-Treatment group(mean=3.600). This difference was significant t(51)=2.734, p<.05; and a medium-effect sizeof r=0.76.
Participants from the Post-control group (mean=4.223) showed higher Socializer Player traits
tendencies than the post-Treatment group (mean=3.820). This difference was significant
t(51)=2.578, p<.05; it represented a medium-sized effect size of r=0.71. Participants from the
Post-control group (mean=3.560) showed higher Relationship Player traits tendencies than the
post-Treatment group (mean=3.053). This difference was significant t(51)=3.161, p< .05; andrevealed a high-effect size of r=0.87. participants from the post-control group (mean=3.821)

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showed higher Teamwork Player traits tendencies than the post-Treatment group(mean=3.770). This difference was not significant t(41.973)=0.401, p>.05; and a small-effectsize of r=0.11.
Participants from the Post-control group (mean=3.839) showed higher Discovery Player traits
tendencies than the post-Treatment group (mean=3.510). Participants from the Post-control
group (mean=3.813) showed higher Roleplaying Player traits tendencies than the postTreatment group (mean=3.710); neither trait was significant. Participants from the Post-control
group (mean=3.762) showed higher Customization Player traits tendencies than the postTreatment group (mean=3.560). This difference was significant t(51)=1.202, p<.05; and asmall-effect size of r=0.33. Participants from the Post-control group (mean=3.631) showed
lower Escapism Player traits tendencies than the post-Treatment group (mean=3.747). Thisdifference was not significant t(47.266)=-0.497, p>.05; and revealed a small-effect size of
r=0.14.
Table 12Mean Analysis from the Treatment Group Post-Intervention (Control/Treatment) for the SubConstructs
Sub-Construct
Advancement
Mechanics
Competition
Socializer
Relationship
Teamwork
Discovery
Roleplaying
Customization
Escapism

Mean Analysis
Group
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment
Post-Control
Post-Treatment

102

N
28
25
28
25
28
25
28
25
28
25
28
25
28
25
28
25
28
25
28
25

Mean Std. Deviation
3.935
0.702
3.787
0.453
3.955
0.536
3.600
0.389
3.134
0.946
3.320
0.724
4.223
0.542
3.820
0.597
3.560
0.529
3.053
0.636
3.821
0.593
3.770
0.314
3.839
0.537
3.510
0.716
3.813
0.592
3.710
0.488
3.762
0.614
3.560
0.606
3.631
1.008
3.747
0.669

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Table 13
Independent Sample Test for the Post-Intervention (Control/Treatment) for Sub-Constructs
Sub-Construct

Advancement

EVA
EVNA
Mechanics
EVA
EVNA
Competition
EVA
EVNA
Socializer
EVA
EVNA
Relationship
EVA
EVNA
Teamwork
EVA
EVNA
Discovery
EVA
EVNA
Roleplaying
EVA
EVNA
Customization EVA
EVNA
Escapism
EVA
EVNA

Independent Samples Test
Levene’s Test for Equality
t-test for Equality of Meansof Variances
F
Sig.
t
df
Sig. (2tailed)
0.844
0.362
0.899
51.0000.373
0.921
46.635
0.362
1.791
0.187
2.734
51.0000.009
2.783
49.033
0.008
2.283
0.137
-0.79751.0000.429-0.80949.875
0.422
0.001
0.972
2.578
51.0000.013
2.563
48.790
0.014
0.059
0.809
3.161
51.0000.003
3.128
46.921
0.003
5.282
0.026
0.388
51.0000.700
0.40141.973
0.691
0.568
0.455
1.907
51.0000.062
1.876
44.219
0.067
1.518
0.224
0.683
51.0000.498
0.691
50.700
0.493
0.028
0.869
1.202
51.0000.235
1.203
50.468
0.235
5.714
0.021
-0.48651.0000.629-0.49747.266
0.621

Key: EVA: Equal variances assumed; EVNA: Equal variances not assumed

Discussion and Findings
For the Control Group, pre and post-test analysis, all player traits increased except
customization based on the mean value. Meanwhile, based on the repeated measures t-test forthe control group that carried out their lesson using Google classroom, no traits weresignificant. Based on the mean increase for all but one construct, it can be seen that theparticipants who were students aged 13-14 years old from the Gen-z batch seemed to haveshown some form of preference and eagerness towards using the Google classroom platform
to learn (Fister Gale, 2015b; Sparks&Honey, 2014). Any form of diversified learning triggered
their interest. However, when it came to customization construct, a drop in mean value wasevident, because the teacher fully controlled this platform (Finlay et al., 2004), and theparticipants could access the notes or tasks given within the platform. This is vital as students
would like to have at least the opportunity to have some control when it comes to the interactive
platform being used for learning (Javora et al., 2021).
For the treatment group that learned using a gamified platform (Kahoot), the pre and post-test
analysis, based on the mean value, all player traits decreased except teamwork, customization,and escapism. Meanwhile, based on the repeated measures T-test, no main player traits weresignificant, but the sub-construct of the relationship was significant. The findings revealed that103

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all means saw a drop between pre-and post-test, yet an increase was evident in the teamwork,
customization, and escapism part of the learning process. This can be attributed to the
lockdown phase, where access to a fully-fledged game was more prevalent to the students(Alsaad et al., 2021), and when they were introduced to a low-level and gamified/game-basedlearning platform, they were feeling a little detached from the learning ecosystem (An &Oliver, 2021). The teamwork sub-construct player traits saw an increase as the students may
have communicated to face the tasks together as a team or duo to make it easier for them andscore better marks (Misra & Mazelfi, 2021; Subhash & Cudney, 2018). The customization trait
can also be attributed to the fact that the students can access the Kahoot platform separately
and test out the systems for themselves (Javora et al., 2021; Roberts-Mahoney et al., 2016).
Finally, escapism can be attributed to the feeling of escaping the norms of online learning
imposed on them during the pandemic lockdown (Hussain et al., 2021; Labrecque et al., 2011).
Although not the best option, what was available to them allowed them to escape the hold of
online learning.
Based on the results of the independent sample t-test, the pre-intervention condition from bothgroups (Control and Treatment), the Customization sub-construct of the player traits was foundto be statistically significant due to the personal experience of these students when it came toreal-life situations. For instance, under lockdown, a student may be constricted to a specificroutine in the real world, which is fixed. Nevertheless, some students find the most superficial
satisfaction in changing or modifying aspects around them (Javora et al., 2021). For example,
changes in the theme or style of their digital devices may trigger these thoughts among thestudents. Based on the independent sample t-test, the post-intervention condition from bothgroups (Control and Treatment), it is evident that the social main player traits revealed asignificance due to two aspects; 1) the social status needs of Gen-z students (Dewi et al., 2021)
and 2) the interaction invoked by the game-like experience (Eck, 2006). Socializing status is
the need for the students of the current generation to connect and communicate with others(Fister Gale, 2015a; Sparks&Honey, 2014). This further seemed relevant for them under
lockdown conditions due to Covid-19. Besides that, the gaming ecosystem triggered the needto communicate with other players that play by their side (Tondello et al., 2019; Wölfl et al.,2021).
Meanwhile, when it came to the sub-constructs, Mechanics revealed a significant difference
since the gamified platform offered numbers and statistics (Barata et al., 2011; Mekler et al.,2013; Thom et al., 2012) that can be observed based on the tasks carried out by the students,
in comparison to the basic Google classroom. Meanwhile, socialization and relationships were
triggered due to casual chats triggered by the need for the students to communicate and reachout to others to help or get help, thus creating a bond/friendship among their peers (Subhash &Cudney, 2018). Finally, the Discovery sub-construct was significant due to the feeling of tryingout a gamified learning environment, which was unique and different from the usual lesson
that the students were going through (Plass et al., 2015).
Conclusions
From the findings, it is to be noted that although using gamified learning to supplement learning
was effective during the pandemic, diversified ways of learning were the best way to engage
the student’s interest. As such, the use of Google classroom and Kahoot as an add-on tool canhelp encourage the students to learn, since it motivated the students in testing times. However,
one of the distinct traits that were evident in each group was customization. This can be
attributed to the changes in the educational ecosystem seen before the lockdown, where the104

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teachers were merely facilitators and students took charge of their learning needs. Nevertheless,
the Covid-19 lockdown reversed the path of learning, with the teaching and learning process
being fully teacher-orientated sometimes leaving students as mere spectators. This scenario left
the students craving more freedom and control when it came to their learning. The significant
trait changes shown in Socializing, through the teamwork construct revealed a need forresilience among students under lockdown, through communicating and collaborating with
their peers. The discovery, mechanics, and customization traits can contribute to the need-toknow attitude or digital literacy of the young participants who would like to explore what is
put forth in a lesson. The curious findings of escapism, which reveals that the students felt
trapped within the learning system imposed by the covid lockdown. However, the current
situation falls under the emergency remote teaching phase, and it will be replaced slowly onceschools start reopening. Yet, the infusion of education and technology will not recede; instead,
it will be continually used as learning progresses in the new millennia. This may lead to faceto-face learning being one day restricted to certain subjects or topics, while others can be
carried out from the comfort of the home.
Acknowledgment
Acknowledgement to “Ministry of Higher Education Malaysia for Fundamental Research
Grant Scheme with Project Code: FRGS/1/2020/SSI0/USM/03/3 and Universiti Sains
Malaysia Short Term Grant, 304 / PMEDIA / 6315350

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Corresponding author: Mageswaran Sanmugam
Email: mageswaran@usm.my111