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Using Gamification to Enhance Learners’
Engagement: An Empirical Study of the Effects of
Awards on Learner’s Motivation
Abstract
The application of game principles (or gamification) has gained attention in non-game
contexts. The primary purpose of gamification is to increase motivation and performance
regarding a given activity. This research study evaluates the efficacy of gamification tools
as means of improving engagement and motivation in a short-duration, preparatory
tutoring course, aiming to prepare learners for the selection process for admission to a
University in Brazil. This study collected and analyzed data from Moodle learning
management system to test the impact of gamification activities over non-gamified
activities in the preparatory University courses. The study uses gamification as an
effective design of awards for various accomplishments of students. The gamified system
rewarded the students with badges on the completion of defined tasks. Entry to a higher
level in the course required completion of tasks indicated by the badges earned. The
assessment of over 193 participants in the research experiments, showed that the learners
are more inclined towards gamified activities than those of non-gamified activities by
approximately 25%. These results draw attention to educators to enhance and build
gamification tools to increase student performance in an entertainment environment.
Keywords
Gamification, Intelligent tutoring systems, Learner Performance, Learning Strategies,
Time Engagement, Leaner’s motivation
Introduction
The advancement in information and communication technology in recent times
has transformed the way people work and live, socially, and professionally.
Learners, if disengaged, might find themselves unfit to thrive in the new era of

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technology (Gilbert, Riis, & Riis, 2017). As noted in Project Tomorrow
(Tomorrow, 2010), the future citizens will be living in a rapidly changing,
technologically advanced society, where information and knowledge will dominate
the cultural and economic structure. The implication of advanced technology for
educators is to prepare learners to learn, collaborate, innovate, and make learning
a lifelong avenue, instead of treating it as an end by itself (Bloom & Doss, 2019).
With the success of technology in many domains, learners now expect similar
technological and engaging environments in learning pursuits. Therefore, it is
imperative to change ‘what we teach,’ ‘how we teach’ (Willms, Friesen, Milton,
& Association, 2013), and how to assess the learning process and outcomes.
Quantitative methods help evaluate the actual performance of learners
(Deterding, 2015), such as metrics for player activity and behavioral measures
(Morschheuser, Hassan, Werder, & Hamari, 2018).
Before the adoption of technology in education, learner engagement research
has centered on retaining learners in the institutions; as such, the focus was on
issues on achievement, learned behavior, and kinship with peers and authorities at
school to increase engagement and thus the performance of learners (Parsons &
Taylor, 2011). Learners are increasingly inclined to use computers and internet in

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their studies (Borrás-Gené, Mart’inez-Núñez, & Mart’in-Fernández, 2019;
Prensky, 2005); therefore, modern pedagogy could be enhanced with technology
and communication. According to a survey carried out in Holland College of the
first-year learners (Lister, 2015), more than half the learners showed t h e
preference of finding their syllabi on the internet. Likewise, almost 90% of
learners showed preferences for taking assignments and accessing the results
online.
The potential of gamification in education is based on the hypothesis that it
supports and motivates students; therefore, leading to enhanced learning
outcomes (Alhammad & Moreno, 2018). The community should employ digital
games to provide better learning outcomes that could serve society (Squire,
Gaydos, & DeVane, 2016). Technology and gamification could be adopted hand in
hand to provide real-life goals; for example, an experiment of two school years
aimed to reduce energy saving that depends on the usage of gamification over IoT-
based lab activities that increase student engagement in Italy (Paganelli, Mylonas,
Cuffaro, & Nesi, 2019). The inference from the studies is that gamification helps
draw the disengaged back to the classrooms or at best reduce the dropout rates by
analyzing of disengagement of the unprivileged class of the society (Willms et al.,
2013). In present times, the perception has changed, and educators’ intent of
engagement is to instill motivation for lifelong learning and thereby acknowledge
society (Gilbert et al., 2017). The fundamental concept of learner engagement lies
in a constructive proposition (Trowler, 2010), while gamification design and
properties could impact sustaining learner engagements (Gaydos, Harris, &
Squire, 2016; Welbers et al., 2019). Therefore, the ultimate target of
gamification is the impact of learners’ outcomes and experiences (T.-Y. Liu &
Chu, 2010; O’Shea & Link, 2019). As a result, learning is an outcome of the
concerted efforts of purposeful activities in which a learner engages, necessarily
implying that engagement leads to and influences the depth and range of activities
towards higher learning desires.
A game is an environment brought about by a set of rules that responds to
activities of the participants, encouraging the player to improve performances to
match others in the game, while keeping a record of players accomplishments
(Mayer & Johnson, 2010, p. 244). The game-based mechanics and dynamics in

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non-game learning environments are frequently referred to as the concept of
gamification of learning. Gamification of learning is an emerging trend that
obliterates the seriousness in learning by injecting playfulness into serious
activities that are considered non-game (Reiners & Wood, 2015; Schönbohm &
Urban, 2014). As a result, embedding playfulness in a learning environment helps
in improving engagement, dedication, concerted effort to expand learning different
facets of such activities, whether at work or in learning to improve outcomes (Chen,
Yang, Huang, & Fu, 2019).
Gamification has been applied in several domains by implementing several
techniques (Ibáñez, Di-Serio, & Delgado-Kloos, 2014). The main techniques
depend on gamification features such as points-scoring systems, leaderboards,
and awards of badges as rewards for completing ascending levels in the quest of
assigned tasks of learning (Poondej & Lerdpornkulrat, 2016). Studies show that
the gamification application could be used to improve the retention rate of online
learners. Several gamifications such as Bunchball1, Badgeville2, DevHub3 , are
used to earn the loyalty of clients and customers, employees and partners through
engagement and livelier experience in commercial and social networking domains

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1
https://www.bunchball.com/
2
https://www.calliduscloud.com/salesmotivate
3
https://www.devhub.com/
(Ibáñez et al., 2014).
In academics and educational context, the use of gamification to create similar
outcomes increases knowledge by using incentives (rewards) for performance in
the form of badges, points, and scores act as external motivators to learners in
gamified courses (Goehle, 2013). Therefore, in this context, courses are
segregated into short-term achievable goals that encourage learners to achieve
broader aims. The educators aim to have a seamless progression that makes
games productive and motivating, effectively encouraging learners to strive for
success despite failures that may appear in short-term goals (de-Marcos,
Domínguez, Saenz-de-Navarrete, & Pagés, 2014; O’Donovan, Gain, & Marais,
2013). However, identifying commercially available games to be used in academic
environments to improve engagement might pose difficulties (Lister, 2015;
O’Donovan et al., 2013). The issues are related to the cost of the commercial
course and matching curriculum and desire to learn the outcomes. Therefore, this
study uses an open-source learning module known as the Modular Object-Oriented
Dynamic Learning Environment (Moodle) to create specialized curricula for
chosen courses. The designed system is integrated with Moodle, the platform
that is most used by students; therefore, no training was needed for students.
Moreover, students were satisfied with that platform; many courses use it as an
assessment tool.
Following the success of gamification in engagement and motivation of learner
learning (Fogg, 2009; Karagiorgas & Niemann, 2017; Nakada, 2017), the objective
of this research is to assess the effect of gamification on the engagement of
learners in online learning pursuits offered through Moodle. This study uses data
extracted from Moodle to measure learner engagement. The measurement
criteria are based on badges as rewards for completing assigned tasks and
taking tests based on the knowledge acquired. The online Moodle allows for

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game-elements plugins and thereby facilitates the measurement of both
engagement and reward of each learner.
This study of learning engagement measurement is categorized in two
calculations using engagement duration (Klemke, Eradze, & Antonaci, 2018;
Poondej & Lerdpornkulrat, 2016). First, measuring and comparing the
engagement duration in gamified and non-gamified components of the subjects in
each course against the awards earned progressively. Second, measuring the
engagement durations in the components as a ratio of the total allotted times.
Therefore, the measurement provides further insight into the essential details
while analyzing the overall effect of embedding gamification in traditional
courses. The main contribution of this study is providing a tool for
gamification analysis and measurement to increase learner performance while
keeping an environment of playfulness as well as competitiveness. Overall, the
objective is to measure the engagement of students on online courses using
Moodle badges in gamification activities and to compare them with non-
gamification activities.
The paper is structured as follows. Section two illustrates the concepts of
gamification and engagement in the learning environment. Section two provides
the background for the choice of design in the present work. The details of the
design and principles of the study in this paper are presented in the following
section - ‘Case Study’. Section four describes the proposed methodology. Section
five is devoted to the results and analysis of the case study. Finally, this research
paper concludes with a discussion of the implication, limitations, and conclusions.
Background and Related Work
The effect of gamification on learner engagements have been well researched.
This section summarizes several concepts of engagements and gamification and
then considers work-related to gamification in engagements.

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Gamification Applications
Previous studies have established that games promote learning ( Liu, Rosenblum,
Horton, & Kang, 2014); therefore, the adoption of gamified courses and peer
communication and interaction can help learners share achievements and
perspectives, encouraging them to learn more in a competitive environment,
leading to sustainable, improved outcomes for all stakeholders (Nicholson, 2015).
Moreover, gamification has better potential as a source of motivation than
conventional non-gamified methods (Barab, Thomas, Dodge, Carteaux, & Tuzun,
2005; Batson & Feinberg, 2006; De Freitas & Griffiths, 2008). Following the
success in commercial and social interaction applications, educational enterprises
also started applying similar techniques to improve academic outcomes through
motivation and engagement embedded in gamification (Domínguez et al., 2013;
Heaslip, Donovan, & Cullen, 2014; Kuo & Chuang, 2016). It has been noted that
gaming elements have succeeded in engaging learners’ interest by making
learning a fun-based activity, replacing the seriousness with playfulness (Barata,
Gama, Jorge, & Gonçalves, 2013; Werbach & Hunter, 2012) and real-time
recognition that leads to motivation to exceed existing performance levels
(Poondej & Lerdpornkulrat, 2016).
Learning Engagement
Learning engagement is an essential component of the learning process towards
assimilation, reproduction, and application of academic content. One of the main
concerns of educators is to evolve ways to engage learners in learning (Gibbs,
2014). Many different approaches have been proposed to engage learners, such as
flipped classroom (Gilboy, Heinerichs, & Pazzaglia, 2015; McLaughlin et al.,
2014), learner-centred learning (Baeten, Kyndt, Struyven, & Dochy, 2010), and
technology-aided learning (Price & Kirkwood, 2011). However, these approaches
are limited in their scope, oriented to the outcome of improving the learning-
teaching process and lack the solution to the original premise of engagement issues
(Wood & Reiners, 2012).

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Researchers reported that the amount of learning and qualitative improvement in
learning is directly related to learner engagement level (Reeve, 2012; Trowler,
2010). Therefore, the engagement can be improved based on learners’ interests
in learning, passion, dedication towards assigned tasks, and achievement of
expected outcomes (Reeve, 2012; Trowler, 2010). T he success of the learning
process and growth of students is dependent on engagement with learning
activities offered (Carini, Kuh, & Klein, 2006; Klem & Connell, 2004; Mcmahon,
Munns, Smyth, & Zyngier, 2012; McMahon & Portelli, 2004). On the other hand,
the disengagement is a negative factor of learning and growth in academic pursuits
(Azar, Lavasani, Malahmadi, & Amani, 2010; Brint & Cantwell, 2012; Kaplan,
Peck, & Kaplan, 1997; Liem, Lau, & Nie, 2008); therefore, the lack of
engagement creates difficulties in learning improvement (Heaslip et al., 2014).
Subsequently, exploring the factors affecting engagement is critical to the learning
process success (Cothran & Ennis, 2000; Hampden-Thompson & Bennett, 2011).
The literature has highlighted many factors: cultural and environmental learning
issues, policy matters regarding outcomes, teaching quality and teacher’s
demeanor, and classroom and online learning activities complementing
traditional resources and learners’ overall disposition and curiosity (Anaya, 1996;
Beetham & Sharpe, 2007).
One of the significant attempts to improve engagement was through the
implementation of games-related activities in online teaching; consequently,
generating a playful learning environment created in classroom teaching
(Alsawaier, 2018). The advent and popularity of online games are used as a driving
force to improve the performance of learners, through diligent effort that
academics and administrators have been looking for to engage the growing
online learner community (Poondej & Lerdpornkulrat, 2016). However, the
pursuit of integrating game-based tools is in a nascent stage and limited by cost and
resource implications, and imposes further issues of maintenance and complexity
involved (Ibáñez et al., 2014). Nevertheless, game-based learning is considered
an attractive alternative to non-gamified face-to-face learning for learners,
especially at the early stages of learning.

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Related Work
According to Landers and Callan (Landers, Bauer, & Callan, 2017),
gamification in educational pursuits supported by virtual learning environments
(VLEs) has the potential to help learners gain more, and provide flexibility
from the classical learning approaches that do not employ gamification-based
learning. Moreover, the engagement approaches help students gain better
perspectives through more in-depth engagement in their academic content
(Anderson, Huttenlocher, Kleinberg, & Leskovec, 2014).
In literature engagement has been observed to include various aspects, such as
cognitive and academic, which make the proposition of engagement studies a
complicated field to study and render coherently and objectively (Harris, 2008;
Willms et al., 2013). The complexity requires extra efforts from educators and
researchers to design education in a way that engages learners towards
successful and sustainable learning outcomes. Engagement assessment techniques
have evolved to maximize effectiveness by providing for formative feedback in
addition to conjuring additional ways that measure learning motivation and
engagement to form a balance to the negative consequences of over-assessment
and under assessment on the motivational aspect in learners (Stiggins, 2005;
Stiggins, 2002). The standard (classic) education engagement measurements
employ techniques such as classroom attendance percentages, marks scored in
tests, dropout or completion rates; the qualitative factors of enjoyment in learning,
active participation, and punctuality in attending to tasks assigned were not taken
into consideration. Therefore, the educators and learners must have their views
represented in the active engagement measurement framework (Lopes, Babo,
Azevedo, & Torres, 2017; Orji, Vassileva, & Greer, 2018; Zhu, Herring, & Bonk,
2019).
Gamification introduces models to enhance the psychological aspects of
motivations, such as the natural human predisposition to learn to improve and

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overcome obstacles and win (Metwally, Yousef, & Wang, 2019). Online games
offer badges as a feedback and measurement tool to course designers and teachers
that can symbolically reflect achievement, skill, or knowledge in a learning
environment (Christophel, 1990). Since badges can be awarded for performance
of clear goals and challenging levels (Dickey, 2005), they have the potential to
positively affect engagement and motivation for learning (Fogg, 2009;
Karagiorgas & Niemann, 2017; Nakada, 2017). Therefore, this research adopts
the badges techniques as part of the proposed model. Badges can be used as a
practical way of skills, certification, and qualifications; therefore, badges intensify
positive behavior in pedagogical studies (Papadimitriou & Niari, 2019).
Learners with high situational interest performed better with badges as badges
that are linked with performance increase student motivation and
engagements (Biles, Plass, & Homer, 2018). According to a study, the acceptance
rate of digital badges was 79%, and the recipients found the badges a motivating
factor, while 58% stated that they would be useful for their careers (O’Brien,
2019). Nevertheless, most studies seek to compare engagement in the present
gamified courses with those of non-gamified courses at deferred academic
sessions (Lister, 2015), on two different sets of students (Klemke et al.,
2018; Poondej & Lerdpornkulrat, 2016), or through responses to questionnaires
addressing different themes (Antonaci, Klemke, Stracke, & Specht, 2017).
Since such comparison in gamified and non-gamified techniques is essential to
explore the engagement trends, this research follows concepts from a study by
Klemke et al. (2018), where the learner interest and intention were considered in
the design phase. Klemke et al. (2018) tried to modify the education process of
Massive Open Online Courses (MOOCs) by introducing the Flipped MOOC;
thereby, increasing the role of learners and educators through interaction. This
research borrows similar concepts in gamification design, where learners
participate actively by completing quizzes. Therefore, rather than using the
completion rate, the gamification is personalized, engaging, and motivating

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students who are willing to be enrolled in the University.
Case Study
Course Structure
The application of a case study allows multi-faceted explorations of a complex
real-life environment. A rigorous qualitative case study gives opportunities for
researchers to explore the gamification effects on student motivation and
engagements. The application of a case study in this context could reveal relevant
factors of student engagements while executing a gamification experiment. Since
the study aims to answer the effect of gamification on learning outcomes,
motivation, and engagement, the explanatory case study is applicable in the study
scenario (Yazan, 2015). Therefore, the output of the case study explanations
would link learning program implementation with program effects. This research
describes the general elements and features of the case study of a gamified set of
three courses to improve the students’ motivation and engagement. The selection
of courses is related to their usage by all students who are willing to join the
university, and they have a direct effect on their subsequent major in the
university coming semesters.
The study was conducted during a 45-hour preparatory course offered to the
learners who intended to secure admission in a particular major in the
anonymous University of Brazil. These hours reflect the total number of hours
for each course each semester, according to the University calendar. Expert
educators designed selected courses’ content in the University. In order to get
admission to the University, the learners must pass the entrance exam. To help
learners, the University offers the preparatory courses through which learners can
prepare themselves for the entrance exams. These courses are offered through
the Moodle platform, which is a common learning management system to

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support e-learning. The usage of Moodle simplifies the gamification design and
allows students to learn and engage quickly as they are used to the Moodle
environment. The University offered three preparatory courses, identified as
Pre-IFRS, to assist learners in getting admission. These courses were
meant to draw attention from the learners who prefer to study the subjects in
which they were struggling and were inclined to clear the entrance exam. The
university also offer courses for elementary drop out student. This course
prepare them for integrated high school course. These Pre- IFRS courses were
optional for the learners. For the research study, three courses were selected;
these courses play a useful role in students’ pre-selection process of students
majors in the University. The courses are both technical and educational for
three different levels of students: incomplete elementary and high schools, and
complete high school, as shown in Table 1. During the course, Moodle stores
data on a variety of learners’ actions and activities such as time spent on the course,
time spent in the learning tasks, the list of learning materials examined, and marks
scored in the quizzes.
Table 1. Course Information
All three courses had a similar structure composed of six different
components. The first component focuses on the functioning of the course and
collects information about learners, such as their educational level and courses
that they want to take in the future. The second component provides
information about the educational institution and about the selection process,
which is presented interactively. The remaining four components were
Course Name Requirements
INTCON - Pre-IFRS Candidates for
the Integrated/ Concomitant High
School Courses
Incomplete elementary school
SUB - Pre-IFRS Candidates for
Technical Courses Subsequent to
High School
Incomplete high school
SUP - Pre-IFRS Candidates for
higher education
Complete High School

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related to the following four subject areas: Mathematics, Nature Science,
Humanities Science, and Languages. Each subject area offers five quizzes to
the learners. The course structure is shown in Table 2. Since the two parts
cover the content of course subjects described in Table 1, the comparison of
engagement between these parts can provide insights on learners’ motivation.
Table 2. Course structure
Therefore, the total course duration of 40 hours out of a total of 45 hours
(2400 minutes) of learning and testing measured the engagement time of
students for gamified and non-gamified activities. The remaining five hours
are kept aside for learners so that they become more familiarized with the
course and other necessary activities required to get started in the course. The
Part Type Description Allotted duration
Learning (preparatory
part, non- gamified)
Content related to each
subject area is provided.
The content information
is in the form of text,
videos, and links
1440 minutes that is
divided equally
between the four
subjects (360 minutes
each).
Testing (gamified) Comprising of five
quiz sections. The
first three quiz
sections comprise five
questions each while
the remaining two
comprise of 10
quiz sections.
A composite time of
960 minutes divided
equally amongst the
four subjects (240
minutes each).

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design is such that overall engagement is encouraged. Based on concepts
from machine learning where they use approximately two-third of data for
learning and one third for testing (Atoum & Ayyagari, 2019), the study
follows a similar approach to split time between gamified and non-gamified
course parts. Therefore, the allotted time for gamified mode was 1440 min,
which is two-third (60%) of the whole 40 hours, while the allotted time for
the gamified version was 960 minutes. Consequently, students were rewarded
for completing the quiz sections that were based on the non-gamified
component. The learners need to answer the questions in the quiz section based
on the learning provided in the non-gamified section. The design of the course
and time was deduced based on the educator’s experience at the university for
years.
Research Questions and Hypotheses
This paper focuses mainly on the engagement and motivation of the learners in
short-duration preparatory tutoring courses that are intense training modules
for admissions to higher education courses at the University. The primary
research interest of this study is to examine the validity of the observations
found in the literature regarding the positive effect of the award of badges on
the engagement of learners. This study addresses the following research questions
in this paper:
RQ1: Do gamified badges earned by learners influence their time engaged in
learning pursuits?
RQ2: Do learners engage more with gamified learning activities that offer awards
compared to non-gamified activities?
Therefore, this study will explore the relative engagement times in gamified and
non-gamified parts of an online preparatory course. The actual recorded times in

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gamified and non-gamified components against the allotted times are compared to
evaluate the specific interest of students in learning and earning badges.
Therefore, the following null hypothesis addresses the research questions.
H0_RQ1: Awards earned by the learner does not influence time engagement
H0_RQ2: Time engagement in gamification mode is monotonic compared to non-
gamification mode.
Gamification Design
This study has applied a gamification process to the courses with the aim to
attract learners’ interest and to raise their motivation and engagement. Therefore,
first, the possibilities that Moodle offers for gamification were analyzed. There
are several plugins or tools for Moodle that can be used for the gamification
purpose. Some of Moodle gamification capabilities are user’s picture/avatar,
visibility of the students’ progress, display of quiz results, levels, feedback,
badges, leader boards (Muntean, 2011; Pastor Pina et al., 2015). For the
gamification purpose, the badges are selected to be used as encouragement for
improving engagement and motivation among learners using Moodle. Badges
were given to learners upon completion of several activities or for achieving a
certain level of knowledge and competence. They were used to display learners’
achievements and rewards. Moodle has a completion tracking feature that was
activated for each course. This option allows instructors to reward learners for each
successfully completed gamified activity as one possible award, in the form of a
badge. The presentation of the badges to learners is through a block available on
the homepage of the course accessible within the profile of the learner. Moodle
allows designers and implementers of the platform to incorporate further features
towards better control and measurement possibilities. One such feature is the
restriction. The restriction is a feature that allows an activity to be displayed or
hidden based on a rule or condition. The rules governing the gamified elements
of the courses are based on a set of possibilities, as shown in Fig 1. These

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restrictions were applied in the same way for all participants.

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Fig. 1. Restriction Feature
The restriction feature in Moodle provides greater configuration possibilities
and helps in automating the display of badges. The learners could earn nine
badges if he completed the whole course. Each badge has an unique emblem
associated with it. The information about badges, emblem, and restriction rules
are shown in Table 3.
Table 3. Rules and Awards
Course
Component
Badge Emblem Rule
First Learner
Profile
Fill the learner form
Second Conscious Player Read about the coursework
Knowledge
about
University
Read or watch content
about the University
Knowledge
sharing
Two or more hyperlinks
shared with the peer learners

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In each course, the learners were not given full access to the course content.
In the beginning, they have access to the first component of the course,
which are activities related to the general introduction about the course and
Moodle and learner’s information. The first accessible course component is
shown in Fig 2.
Fig. 2. The first component of the course
After the learners filled their profile information, they were rewarded with
unlocking the second-course component, and they earn the “Learner Profile”
badge. The badge was displayed in the “My Achievement” area of the page,
where the learner could check the earned awards, as shown in Fig 3.
Learner Profile

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Fig. 3. My Achievements area
After completion of this component, the learner could earn three more
badges for the second-course component that is related to information about
the educational institution and about the selection process. Consequently, the
learner will be able to earn the badges related to the four subject areas:
Mathematics, Nature Science, Humanities Science, and Languages after
completing five quizzes for each subject. Before attempting the quizzes, the
learners have to study information in non-gamified mode. In total, four badges
can be earned by the learner after completing these four components. Finally,
one last badge can be earned if all the above-mentioned activities are marked
as “completed.” Fig 4 illustrates a course clipping.

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Fig. 4. A Course Screenshot
Data Collection
The purpose of the study is to explore the increase in student engagements for
courses that are directly related to their career path. Well-engaged students
will get higher scores in subjects that are significant for their admission in
their favorite major. Observations by various educators in the university
showed a 5-10% dropout at a later stage of their study due to low scores that
they got in the preparatory courses. In this study, 765 learners showed their
interest in the offered courses by filling an online subscription form and
sending a copy of their official application. About 45% (348 out of 765) of
learners completed the Pre-IFRS courses. After the preliminary screening,
incomplete data of participants who did not complete the courses were omitted
because they were considered as not reliable enough for inclusion in the analysis.
The criteria to include a participant in the analysis were: (i) participants who
have an engagement time of more than 240 minutes which is 10% of the
allotted time (course duration), (ii) participants who have earned a minimum of

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four badges, and (iii) participants who have attempted at least one gamified
quiz.
The proposed criteria ensure that the learners have furnished all the
information expected before embarking on the course, exploring the content
of the course on offer, got acclimatized with the structure of preparation and
that they agree with the direction and guidance offered through Moodle.
Moreover, the four minimum badges require that learners complete four
tasks: fill the learner form, read about the coursework, read or watch content
about the University, and share two or more hyperlinks with peer learners.
These tasks and the badges earned thereby are not an accurate and complete
reflection of the engagement in non-gamified or gamified activities sought to
be explored in this work; the only aim for awarding the first four badges is to
incorporate only those students that exhibit due inclination in taking up the
course. The awards also help students get used to gamification (for the
uninitiated) and encourage students to earn the remaining badges. The course
designers are cognizant of the fact that many learners take up the course to get

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guidance on topics and subjects in which they have been comparatively weak.
However, based on feedback from previous educators, the gamified mode covers
all parts of the course as per the syllabus. Course designers focus on a broad
synthesis of abilities that syndicate information, skills, and principles into a whole
that reflects how students use knowledge. The students have the freedom to
attempt the quiz part directly without going through the lessons. The quiz
compromises of test on sub-topics. The difficulty level is incremental. The student
can review the non-gamified part when required to clear the gamified quiz and
earn the requisite badges to go to the next level. Therefore, the student can choose
the gamified or non-gamified part as per individual preference. Earning four
badges indicates his inclination to take the preparatory course. At the same time,
his interest (or lack of it) in earning badges is also taken into account. Secondly,
the time for which the student was engaged is comparative to those who earned a
higher number of badges. There will always be students who are interested in
learning rather than earning badges. This study shows that more students find
inspiration in earning badges. When offered the choice, what would students
choose as a majority? Effectively, does gamification encourage engagement?
The preparatory course is designed to empower the students to inculcate
adequate skills and knowledge to score competitively in the entrance
exams. Therefore, the course design requires that students take enough
effort to complete each assignment in all subjects and earn maximum
rewards by engaging thoroughly in a non-gamified activity. The successful
completion of each gamified activity earns the learner’s badges that can only
be achieved by engaging in non-gamified activity before attempting the
gamified activity. As a result of the inclusion criteria, 193 learners out of 348
learners complying with the inclusion criteria appears to be an acceptable
outcome. More specifically, data from 225 learners of INTCON, 29 learners of
SUB and 94 learners of SUP were targeted for the analysis.
Methodology

24
This study uses a quantitative analysis of data gathered from 193 students
meeting the inclusion criterion. The analysis pertains to the engagement times
recorded by students in an online learning management system of Moodle of the
45-hour preparatory course. The resultant data of 193 students were analyzed for
engagement times recorded in four subjects. The analysis of the data available
through online logging of each learner that met with the inclusion criterion has
been explained in the Data Collection section above. The analysis aims to
measure and compare the engagement duration of learners in the non-
gamified, and gamified, part of the course. Towards measuring the actual
engagement times in both parts of the course, the ratio of actual engagement to
the allotted time in each subject has been calculated. This step helps to
achieve normalization of engagement times; this step has been necessitated
because the allotted durations for each subject for non-gamified (360
minutes) and gamified (240 minutes) activities are different. The number of
students was opt out to future research as it is related to cognitive abilities
that assuming that it will be relatively similar in gamification and non-
gamification approach. The results depict engagement durations for non-
gamified and gamified activities for each subject read-off against the awards

25
earned by the students. After normalization of engagement times of each of
the subjects for a different number of awards earned by the students, the total
of non-gamified and gamified activities is calculated. These two durations are
then added to access the total engagement of the students in each category
(defined by awards earned). The next calculation is the ratio of the total
engagement time to the total allotted time for the activities.
For measuring engagement, the proposed approach analyzes the engagement of
students in gamified and non-gamified activities in the selected courses. Out of
the 45 hours, five hours was deducted as learners will spend some time in
providing the information required in components one and two. The allotted time
for completing the gamified component was 240 minutes for each subject (total
time for four subjects – 960 minutes). The gamified component required the
students to complete quizzes based on the non-gamified component for which the
time allotted was 360 minutes per subject (total allotted time – 1440 minutes).
The measured quantities were – 1) the total gamified vs non-gamified engagement
times for a different number of awards won by the learners, and 2) the ratio of
gamified, non-gamified, and total engagement to allotted times.
Results and Discussion
Based on the learners’ inclusion criteria— based on badges as rewards for
completing assigned tasks—the average number of badges earned was 6.43, while
the average of total engagement times for each award category is shown in Table
4.
Table 4. Engagement Analysis
Number
of
Awards
Number
of
students
Engagement (in
minutes)
Engagement Time
percentage
Total
Engagement
(in minutes)
Engagement
ratio
non-
gamified
gamified
Non-
gamified
gamified
4 43 649.54 666.97 45% 69% 1,317 55%
5 17 652.94 685.88 45% 71% 1,339 56%
6 25 650.16 658.88 45% 69% 1,309 55%
7 36 628.31 666.33 44% 69% 1,295 54%

26
To discuss time engagement over the award to test the hypothesis H0_RQ1:
Awards earned by the learner does not influence engagement and badges is shown
in Figure 6.

27
Fig. 6. Engagement time vs. Number of awards
Each point in the figure corresponds to average engagement durations for awards
earned over the total number of badges for all the three studied courses. After
the preparatory period, the engagement decreases as students earn the fifth, sixth
and seventh badge. After earning those three badges the difficulty level rises.
Consequently, the engagement times in both gamified and non-gamified activity
rises as students are encouraged to earn the remaining badges (8th and 9th).Most of
the earners of a total of 5 badges have attempted the first to the third component of
courses, which encompasses tasks related to badges of fill profile, conscious

28
player, knowledge about University, knowledge sharing and mathematics
experts’ badges. The results indicate that students take more time in a mathematics
course due to the proper gamification design that motivated them. Moreover, the
engagement time of the gamified part is 25% more than in the non-gamified part.
Although the allotted time for non-gamified activities was higher than those of
gamified activities, the results indicate that the students spend more time on
gamified activities, which indicates that they study more and engage more; that
resulted in earning better scores (badges). However, non-gamification mode, most
students stop engaging in courses after they earn badge 7, as they might feel bored
and less entraining of courses due to difficulty of getting that number of badges.
The observation of greater engagement in gamified activity by 25% over non-
gamified activity was further tested for significance of the finding. A paired-
sample T-test was conducted for the pairs gamified engagement time (gamengT)
with awards earned and with total time (totT). Effectively, the ratio of engagement
time in gamified activity with awards earned and with total engagement time of
the students is tested for significance. The results of the T-test Show that the
correlation significance for engagement time ratio is significant at.04 (< .05).
Further, the two-tailed significance for the one-sample ratio test of gamengT-totT
and gamengT-awards is .000 (in both cases). The null hypothesis can be rejected.

29
Table 5. Paired Samples Statistics
Table 6. Paired Samples Correlations
Table 7. Paired Samples Test
Mean N Standard
Deviation
Standard
Error Mean
Pair 1 GamengT 671.1967 6 9.81912 4.00864
totT 1320.6667 6 18.12917 7.40120
Pair 2 gamengT 671.1967 6 9.81912 4.00864
awards 6.50 6 1.871 .764
N Correlation Significance
Pair 1 gamengT & totT 6 .832 .040
Pair 2 gamengT & awards 6 .117 .825
Paired Differences t df Sig.
(2-
tailed
)
Mean Std.
Devia
tion
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lowe
r
Upper
Pair 1 gamen - 11.35 4.636 - - - 5 .000
gT - 649.4 801 89 661.3 637.5 140.
totT 7000 8950 5050 066
Pair 2 gamen 664.6 9.778 3.991 654.4 674.9 166. 5 .000
gT - 9667 30 97 3497 5836 508
award
s

30
Further, with a 71% increase in engagement time percentage, the null hypothesis
that awards reduce the engagement time of learners is rejected. To assure a
positive relationship from the preliminary analytical discussion, the ratio of the
“views of gamified activities” to the “total views” including gamified as well as
non-gamified activities appear contextual and further ratification of present
research study outcomes.
To discuss time engagement over the award to test the hypothesis H0_RQ2:
Awards earned by the learner in gamification mode is unordered. Table 4 shows
that the approximate respective percentage engagements for non-gamified,
gamified, and total are, therefore: 45%, 70%, and 55%, which means that the
engagement in gamified (70%) activities is significantly higher than non-gamified
engagement by 16%. Therefore, the findings could reject the null hypothesis
that gamification engagement time is monotonic in regard to no-gamification
mode in the study context.

31
Implications
This study draws attention to educators to enhance pedagogy using gamification
techniques, as gamification shows an engagement increase of 25% on gamified
mode. Moreover, the educators could relatively enhance the gamified course design
such that the degree of course difficulty does not stop the student from further
engagement for students who lose interest after earning a few badges. The
gamification restrictions feature should go automatically hand in hand with the level
of gamified difficulty (Domínguez et al., 2013). For academia, the revealed results
show that tools of gamification may enhance pedagogy, which should increase the
thrust of gamification tools for short and long duration courses.
Limitations
Although the proposed work has highlighted the effect of gamification in learner
engagement time, it does have certain limitations. The significance of the
observations of this analysis can be understood when seen in the light of
contrasting inferences drawn in an earlier study (Abramovich, Schunn, & Higashi,
2013). Abramovich et al. (Abramovich et al., 2013) infer that badge acquisition
patterns were different across the learner’s knowledge profile. Further, the study
draws the result that inborn traits affect learning negatively. However, the result of
this study shows the positive relationship between engagement and gamified
elements. Section two pointed out that engagement comprises of different
dimensions and evaluating all of them would require a sizeable experimental base,
spanning different courses, programs, and constructs across geographic and
demographic diaspora over longer durations. Therefore, the additional factors that
affect time engagement are considerations for future work.
Conclusion
This work aimed to explore whether incorporating gamified elements in an online
preparatory course helps in improving the engagement of learners. The
engagement duration (in minutes) of learners in four subjects (mathematics, natural
sciences, general knowledge, and language) was measured for each learner. The
awards earned were tabulated against the engagement durations. The records of
awards earned against the engagement showed that the engagement times in gamified

32
activity was 25% more than in non-gamified activity. The overall engagement of the
students was around 55%. The results indicate that the overall learning engagement
can be improved by embedding gamified components into learning environments in
an online course. Designers of the online courses can use this information to
incorporate judiciously designed gamified elements and help improve engagement.
This work, however, takes note of the observation in the literature that gamification
is not a panacea for issues about engagement in academic pursuits and therefore
proposes to undertake further analysis from different perspectives from the data
available as future work, a continuation of present work.
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Figure
Fig. 1. Restriction Feature

Figure
Fig. 2. The first component of the course

Fig. 3. My Achievements area
Learner Profile

Fig. 5. Engagement time vs. Number of awards

Table 1. Course Information
Course Name Requirements
INTCON - Pre-IFRS Candidates for
the Integrated/ Concomitant High
School Courses
Incomplete elementary school
SUB - Pre-IFRS Candidates for
Technical Courses Subsequent to
High School
Incomplete high school
SUP - Pre-IFRS Candidates for
higher education
Complete High School

Table 3. Rules and Awards
Course
Component
Badge Emblem Rule
First Learner
Profile
Fill the learner form
Second Conscious Player Read about the coursework
Knowledge
about
University
Read or watch content
about the University
Knowledge
sharing
Two or more hyperlinks
shared with the peer learners
Third Mathematics Expert Complete the five
quizzes in the
corresponding subjects
Fourth Scholar of Nature
Fifth Learner of
Humanities
Sixth Scholar of Languages
Overall Ready for the test Completed all the quizzes

Table 4. Engagement Analysis
Number
of
Awards
Number
of
students
Engagement (in
minutes)
Engagement Time
percentage
Total
Engagement
(in minutes)
Engagement
ratio
non-
gamified
gamified
Non-
gamified
gamified
4 43 649.54 666.97 45% 69% 1,317 55%
5 17 652.94 685.88 45% 71% 1,339 56%
6 25 650.16 658.88 45% 69% 1,309 55%
7 36 628.31 666.33 44% 69% 1,295 54%
8 32 651.22 669.53 45% 70% 1,321 55%
9 40 663.21 679.59 46% 71% 1,343 56%
Average 649.23 671.2 45% 70% 1,321 55%

Table 5. Paired Samples Statistics
Mean N Standard
Deviation
Standard
Error Mean
Pair 1 GamengT 671.1967 6 9.81912 4.00864
totT 1320.6667 6 18.12917 7.40120
Pair 2 gamengT 671.1967 6 9.81912 4.00864
awards 6.50 6 1.871 .764

Table 6. Paired Samples Correlations
N Correlation Significance
Pair 1 gamengT & totT 6 .832 .040
Pair 2 gamengT & awards 6 .117 .825

Table 7. Paired Samples Test
Paired Differences t df Sig.
(2-
tailed
)
Mean Std.
Devia
tion
Std.
Error
Mean
95% Confidence
Interval of the
Difference
Lowe
r
Upper
Pair 1 gamen - 11.35 4.636 - - - 5 .000
gT - 649.4 801 89 661.3 637.5 140.
totT 7000 8950 5050 066
Pair 2 gamen 664.6 9.778 3.991 654.4 674.9 166. 5 .000
gT - 9667 30 97 3497 5836 508
award
s

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