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International Journal of Human-Computer Studies
journal homepage: www.elsevier.com/locate/ijhcs
The effect of challenge-based gamification on learning: An experiment in the
context of statistics education
Nikoletta-Zampeta Legaki
⁎,a,b
, Nannan Xi
b
, Juho Hamari
b
, Kostas Karpouzis
a
,
Vassilios Assimakopoulos
a
a
School of Electrical and Computer Engineering, National Technical University of Athens, Zografou 15780, Greece
b
Gamification Group, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere 33100, Finland
ARTICLE INFO
Keywords:
Gamification
Applications in education
Statistics education
Teaching forecasting
Human-Computer interface
ABSTRACT
Gamification is increasingly employed in learning environments as a way to increase student motivation and
consequent learning outcomes. However, while the research on the effectiveness of gamification in the context of
education has been growing, there are blind spots regarding which types of gamification may be suitable for
different educational contexts. This study investigates the effects of the challenge-based gamification on learning
in the area of statistics education. We developed a gamification approach, called Horses for Courses, which is
composed of main game design patterns related to the challenge-based gamification; points, levels, challenges
and a leaderboard. Having conducted a 2 (read: yes vs. no) x 2 (gamification: yes vs. no) between-subject
experiment, we present a quantitative analysis of the performance of 365 students from two different academic
majors: Electrical and Computer Engineering (n=279), and Business Administration (n=86). The results of our
experiments show that the challenge-based gamification had a positive impact on student learning compared to
traditional teaching methods (compared to having no treatment and treatment involving reading exercises). The
effect was larger for females or for students at the School of Electrical and Computer Engineering.
1. Introduction
Gamification approaches are being applied with increasing fre-
quency in an attempt to positively affect behavior and cognitive pro-
cesses by enhancing the system or service with motivational affor-
dances and eventually by bringing similar experiences as games
do (Huotari and Hamari, 2017). Motivational affordances have been
widely used in many fields such as business (Alcivar and Abad, 2016; Xi
and Hamari, 2020), crowdsourcing (Morschheuser et al., 2017),
healthcare (Johnson et al., 2016) and education (Dichev and Dicheva,
2017; Hanus and Fox, 2015; Koivisto and Hamari, 2019; Majuri et al.,
2018; Osatuyi et al., 2018; Seaborn and Fels, 2015). Additionally, ga-
mification has been employed in many education related contexts,
across different educational levels (Caponetto et al., 2014; Dicheva
et al., 2015; Simões et al., 2013; de Sousa Borges et al., 2014) and in
various subjects (Dichev and Dicheva, 2017; Dicheva et al., 2015;
Kasurinen and Knutas, 2018; Seaborn and Fels, 2015), showing its
potential to improve learning outcomes (Koivisto and Hamari, 2019;
Seaborn and Fels, 2015).
According to reviews of gamification literature, gamification has
been employed mostly in the field of education (Koivisto and Hamari,
2019; Majuri et al., 2018; Seaborn and Fels, 2015). Gamified educa-
tional applications have been applied in non-academic areas as well:
language teaching (Duolingo counts 300 million active users
1
) or soft-
ware using (Ribbon Hero by Microsoft). Other popular gamified ap-
plications are: Kahoot and Quizizz, which can be easily configured and
used in a variety of subjects, bringing game elements into classrooms
without any special effort. Although gamification has an important
position in education both inside and outside universities, there is still
little effective guidance on how to combine different gamification fea-
tures to enhance learning performance in different educational
contexts (Hanus and Fox, 2015; Koivisto and Hamari, 2019; Seaborn
and Fels, 2015).
Beyond research problems pertaining to the general interest in ga-
mification and its effect on education, statistics education is an in-
creasingly fundamental skill to understand the world around us. The
lack of data literacy has been deemed one of the main causes behind our
inability to act against climate change, to properly ratify means towards
e.g. COVID-19 or generally as a hindrance for public understanding of
science. Therefore, there is a need to make teaching methods in
https://doi.org/10.1016/j.ijhcs.2020.102496
Received 12 November 2019; Received in revised form 9 June 2020; Accepted 12 June 2020
⁎
Corresponding author.
E-mail addresses: zampeta.legaki@tuni.fi,zabbeta@fsu.gr,nzabbeta@gmail.com (N.-Z. Legaki).
1
https://ai.duolingo.com/
International Journal of Human-Computer Studies 144 (2020) 102496
Available online 14 June 2020
1071-5819/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).
T
statistics and forecasting more engaging (Love and Hildebrand, 2002).
The acceleration of the daily data production, and ever-greater capacity
to store and process this information, has boosted the necessity for
students with a strong background in statistics and predictive analytics
skills, in business environments or even in everyday life. Consequently,
both statistics and forecasting techniques are of vital importance in the
economics curriculum (Loomis and Cox Jr, 2003) and in other fields
such as business (Makridakis et al., 2008) or social problems, where
data may help to make better decisions. However, often forecasting
courses are not even offered as an independent course in business
schools (Hanke, 1989) and when they are, students are discouraged to
participate in the courses because they find the topic too
complicated (Albritton and McMullen, 2006; Gardner, 2008; Snider and
Eliasson, 2013; Torres et al., 2018) and demanding (Craighead, 2004).
Therefore, regarding the education and especially the education in the
field of statistics and forecasting, student motivation is crucial for their
participation and understanding in order to reach their learning po-
tential, meet business needs and get insights of the data to support the
decision-making process.
Despite the long tradition in educational business games, thus far
there have only been a few studies on gamification or simple gamified
exercises, combined with traditional teaching methods in the area of
statistical forecasting. These studies have mostly used: score
(Craighead, 2004), spreadsheets (Gardner, 2008),
competition (Snider and Eliasson, 2013) and real-world forecasting
problems (Buckley and Doyle, 2016a; Gavirneni, 2008) in order to
encourage students' participation, without examining gamification ef-
fects. Other more quantitative studies have used forecasting in the
context of a prediction market as a tool to motivate students rather than
to teach forecasting aspects (Buckley and Doyle, 2016a; 2016b; 2017;
Buckley et al., 2011). While there are several types of games and ga-
mification designs, the challenge-based gamification (e.g points, levels,
leaderboard, clear goals/ tasks), as opposed to the immersion- and the
social-based gamification, has been suggested (Dicheva et al., 2015;
de Sousa Borges et al., 2014; Zichermann and Cunningham, 2011) and
applied to a high degree in practice as gamification design in
education (Koivisto and Hamari, 2019; Seaborn and Fels, 2015;
de Sousa Borges et al., 2014). One or more of these gamification ele-
ments have been used with promising results even in educational topics
relative to forecasting (Craighead, 2004; Gavirneni, 2008; Gel et al.,
2014; Snider and Eliasson, 2013). However, there is still a lack of ef-
fective design guides and empirical data on the combination or in-
tegration of these features in the context of educational information
systems (Koivisto and Hamari, 2019). Challenge-based gamification
introduces a design approach of integrating achievement gamification
features, positively related with intrinsic need satisfaction (Xi and
Hamari, 2019), in an educational service or application, in order to
explore its potential, motivate users and eventually improve learning.
The present study examines the impact of three treatments on stu-
dents’performance i) reading, ii) use of a challenge-based gamified
application, iii) the combination of the two. In order to do that, we
consider a variety of student characteristics such as gender, level of
studies, academic major, expertise in the English language, and use of
personal computers and games. We designed and implemented a web-
based gamified application, called Horses for Courses and we conducted
a series of experiments over the last 4 years. The total sample is com-
posed of 365 students, with 279 undergraduate and MBA students at
the School of Electrical and Computer Engineering of the National
Technical University of Athens, Greece (hereafter ECE, NTUA) and
86 undergraduate students at the Business Administration Department
in the School of Business and Economics of the University of Thessaly,
Greece (hereafter Business Administration). Our findings show that
challenge-based gamification improves students’learning outcomes on
a statistics course, contributing to the knowledge of challenge-based
gamification’seffect on statistics/stem education and eventually on
gamified pedagogy.
2. Background
2.1. Gamification in education
Gamification refers to a method of designing systems, services, or-
ganizations and activities in order to create similar experiences and
motivations to those experienced when playing games, with the added
educational goal of affecting user behavior (Huotari and
Hamari, 2017). Games are known to motivate and engage
players (Dichev and Dicheva, 2017) because of the enjoyment and the
excitement that this activity offers (Koivisto and Hamari, 2019). In this
regard, gamification aspires to create this experience in different con-
texts. This is usually attempted by using game mechanics or other
game-like designs in the target environment (Deterding et al., 2011).
Over the last decade, gamification research has affected a variety of
domains that deal with education (Koivisto and Hamari, 2019). The
educational domain is continuously evolving, incorporating the latest
developments in information technology even in elementary
schools (Karpouzis et al., 2007). Nonetheless still demands students'
commitment and persistence in order for them to gain in-depth
knowledge. Consequently, gamification has been of great interest to
educators who have been exploring its potential in improving student
learning (Dichev and Dicheva, 2017; Dicheva et al., 2015; Hamari,
2013; Koivisto and Hamari, 2019; Majuri et al., 2018; Seaborn and Fels,
2015). This potential has led to a growing literature on the effectiveness
of gamification, mainly in universities but also in other academic
contexts (Caponetto et al., 2014; Koivisto and Hamari, 2019; Seaborn
and Fels, 2015; de Sousa Borges et al., 2014) and in a variety of
subjects (Dichev and Dicheva, 2017; Kasurinen and Knutas, 2018). To
name a few: information technology (Osatuyi et al., 2018), math/
science (Attali and Arieli-Attali, 2015; Christy and Fox, 2014) and
taxation (Buckley and Doyle, 2016a; 2017).
Gamification types and types of game design have commonly been
horizontally categorized into main three overarching categories of
achievement/challenge-, immersion-, and social-based (Hamari and
Tuunanen, 2014; Koivisto and Hamari, 2019; Snodgrass et al., 2013; Xi
and Hamari, 2019; Yee, 2006; Yee et al., 2012) beyond the common
vertical categorization of e.g. the MDA model that separated game
design into mechanics, dynamics and aesthetics (Hunicke et al., 2004).
The immersion-based game design attempts primarily to engulf the
player or user into a story, roleplay and audiovisual richness. The so-
cial-based game design is commonly focused on different forms of
competition and collaboration. Finally, the achievement/challenge-
based game design is focused on overcoming challenges, progressing
and earning rewards and feeling competent. Within the achievement/
challenge-based gamification, the most commonly embodied mechanics
have been points, challenges, leaderboards, levels and badges (Koivisto
and Hamari, 2019; Majuri et al., 2018; Pedreira et al., 2015). According
to the self-determination theory, the use of these elements, which are
considered as achievement related features and immediate performance
indicators, is associated with intrinsic motivation for students (Xi and
Hamari, 2019). In this regard, these elements form challenge-based
gamification underpinnings in order to motivate students to maximize
their knowledge acquisition.
Review studies about the effectiveness of gamification are generally
optimistic, mainly listing either positive or mixed results of applied
gamified strategies (Buckley and Doyle, 2017; Caponetto et al., 2014;
Dicheva et al., 2015; Koivisto and Hamari, 2019; Lambruschini and
Pizarro, 2015; Majuri et al., 2018; Nah et al., 2014; Osatuyi et al., 2018;
Reiners et al., 2012; Seaborn and Fels, 2015; de Sousa Borges et al.,
2014). Nevertheless, they mention the need for more controlled ex-
perimental research on the impact of gamification, independently of the
application domain or used gamified strategy (Buckley and Doyle,
2017; Caponetto et al., 2014; Dichev and Dicheva, 2017; Dicheva et al.,
2015; Hanus and Fox, 2015; Koivisto and Hamari, 2019; Lambruschini
and Pizarro, 2015; Landers et al., 2018; Majuri et al., 2018; Nah et al.,
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
2
2014; Osatuyi et al., 2018; Reiners et al., 2012; Seaborn and Fels, 2015;
de Sousa Borges et al., 2014).
The effects of gamification are bound together with the target au-
dience and the context (Buckley and Doyle, 2017; Dichev and Dicheva,
2017; Hanus and Fox, 2015; Koivisto and Hamari, 2019; Seaborn and
Fels, 2015). Hence, the results of gamification vary regarding the sub-
ject and the field of application (Hanus and Fox, 2015; Sánchez-Martín
et al., 2017). Therefore, researchers generally agree on the need for
stronger empirical results (Buckley and Doyle, 2017; Hanus and Fox,
2015; Koivisto and Hamari, 2019; Landers et al., 2018; Maican et al.,
2016; Morschheuser et al., 2017). This study contributes to this body of
research with empirical data drawn from a series of experiments on the
impact of gamification with control and treatment groups in the context
of a forecasting course.
2.2. Teaching forecasting in higher education
As described in Garfield and Ben-Zvi (2007), statistics and statistical
literacy are of paramount importance, especially in the rapidly chan-
ging business environments. As interest in the available technology and
statistics is growing, forecasting skills are becoming more
sophisticated (Kros and Rowe, 2016) and the process of teaching fore-
casting is becoming more difficult and demanding. Statistics courses
focus on data analysis (Cobb, 1992), as competitive business environ-
ments require graduate students to interpret data and be able to use
statistical and judgmental forecasting methods and
applications (Giullian et al., 2000; Kros and Rowe, 2016). The im-
portance of forecasting skills is not a new discovery (Albritton and
McMullen, 2006; Craighead, 2004; Giullian et al., 2000; Kros and
Rowe, 2016; Loomis and Cox Jr, 2003; Makridakis et al., 2008; Snider
and Eliasson, 2013). However, recently these skills have have become
even more important since business decision-making must be supported
by data-based evidence and projections (Giullian et al., 2000). Another
aspect of forecasting that highlights its importance is its multi-
disciplinary nature, since the forecasting techniques are an essential
component in a number of fields such as business
statistics (Tabatabai and Gamble, 1997), supply chain
management (Gavirneni, 2008) and management
science (Makridakis et al., 2008).
However, the eagerness of the business sector to equip students with
a strong background in forecasting techniques is only partially reflected
in the education that universities and business schools provide. Thirty-
five years ago, 58% of the surveyed universities offered an independent
forecasting course (Hanke, 1984). The percentage is reduced to 34.48%
of the surveyed business schools based on a more recent study
by Kros and Rowe (2016) and it is almost the same (50%) regarding the
top 50 US Business Programs, which requires a forecasting time-series
course. Moreover, there is a variety of “e-learning-in-statistics in-
itiatives”, but even these modules do not focus on time-series and
forecasting methodologies (Gel et al., 2014).
Despite the growing popularity of and need for forecasting skills,
business schools slowly address this demand, and they generally dis-
regard the need for increasing student motivation (Debnath et al.,
2007). Business forecasting or statistical forecasting methods are
usually considered complicated (Albritton and McMullen, 2006;
Craighead, 2004; Gardner, 2008; Snider and Eliasson, 2013; Torres
et al., 2018), making it difficult for students to remain
motivated (Craighead, 2004). Taking this into account, Chu (2007);
Donihue (1995);Loomis and Cox (2000);Loomis and Cox Jr (2003);
McEwen (1994) suggest alternative teaching guidelines such as the use
of a software or new technology in combination with real data and
forecasting problems. Active learning has also been proposed (Love and
Hildebrand, 2002) in order to address the need to update the fore-
casting educational process. So the digitalization, which we experience
has boosted the statistical skills’importance. However, universities and
business schools have not responded immediately to this challenge and
they have been criticized for not placing enough focus on the specific
skills that will improve the students’future job
performance (McEwen, 1994) and career success (Pfeffer and
Fong, 2002).
2.3. Gamification and teaching statistical forecasting
This study puts emphasis on gamification only in terms of simple
educational activities or systems, usually including game
mechanics (Bunchball, 2010; Deterding et al., 2011). In this direction,
we reviewed journal articles that discuss simple active learning events,
gamified exercises or games in the context of a forecasting course. Our
results indicate that the use of score (Craighead, 2004),
spreadsheets (Gardner, 2008) and competition (Snider and
Eliasson, 2013) during lectures has positive effects regarding students’
attitude, but strong empirical data is not presented. The use of a cus-
tomized software (Spithourakis et al., 2015) and students’participation
in prediction of a basketball score appeared beneficial in the context of
an undergraduate forecasting techniques course.
Other active learning exercises have used competition based on
students’forecasting accuracy in order to increase students’participa-
tion and improve learning outcomes in a management
course (Buckley et al., 2011) and in a taxation course (Buckley and
Doyle, 2016a). Another simple game, named: FREDCAST has been de-
signed and recently used in order to teach forecasting in a macro-
economic course (Mendez-Carbajo, 2018). All of these examples, show
that forecasting due to its nature can be considered as a kind of an
artistic field (Gavirneni, 2008), where gamification could be efficiently
integrated in order to make it attractive to its audience.
Our preliminary review mainly positions gamification as a bene-
ficial tool in education of forecasting and related fields such as man-
agement (Buckley et al., 2011; Makridakis et al., 2008), decision-
making (Makridakis et al., 2008), taxation (Buckley and Doyle, 2016b),
supply chain management (Gavirneni, 2008) but highlights the need for
more empirical results. Additionally, an overview of teaching fore-
casting shows the importance of forecasting courses in economics
syllabus (Loomis and Cox Jr, 2003) or business school
curriculum (Buckley et al., 2011; Gavirneni, 2008). The need for fore-
casting skills is increasing as well, due to technological changes, as
management seeks data-based approaches in dealing with decision-
making on market opportunities, environmental factors and technolo-
gical resources. However, forecasting courses are not adequately sup-
ported by students' participation or university and business school
programming (Albritton and McMullen, 2006; Snider and Eliasson,
2013). A possible approach to address this issue could be gamification,
which under proper design guidelines has produced promising results
regarding student motivation and learning outcomes in management
courses (Buckley and Doyle, 2016a; Craighead, 2004; Gardner, 2008;
Snider and Eliasson, 2013) and in a forecasting
module (Gavirneni, 2008). Nevertheless, thus far, there have not been a
lot of studies on the effects of gamification on learning outcomes, in the
specific area of statistical forecasting. Therefore, this study experi-
mentally examines the potential of the challenge-based gamification, by
designing from scratch and using a gamified application in order to
improve student learning in a forecasting course.
3. Material and methods
3.1. Participants
A series of experiments were conducted at the ECE, NTUA and at the
Business Administration. More precisely, we performed the experiments
in different classes and academic majors, as follows:
•49 undergraduate students (class of 2015) at the ECE, NTUA.
•37 MBA students (class of 2015) at the ECE, NTUA.
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
3
•60 undergraduate students (class of 2016) at the ECE, NTUA.
•52 undergraduate students (class of 2018) at the ECE, NTUA.
•21 MBA students (class of 2018) at the ECE, NTUA.
•86 undergraduate students (class of 2018) at the Business
Administration.
•60 undergraduate students (class of 2019) at the ECE, NTUA.
The total sample is composed of 365 students; 270 students are
males and 95 are females. The experiments were performed in the
context of a forecasting course, with fourth-year undergraduate stu-
dents and second-year MBA students, respectively, at the ECE, NTUA.
At the Business Administration, the experiment was conducted in the
context of an information technology course, with first-year students.
However, the Business Administration’s curriculum contains an opera-
tional research course, which includes forecasting techniques. The ex-
perimental design of our experiments was followed strictly, in-
dependently of the academic level of the students, as described in the
next section.
3.2. Experimental design
We conducted a 2 (read: yes vs. no) x 2 (gamification: yes vs no)
factorial experiment. The dependent variable was student performance
in the learning task. Participants were randomly assigned to one of the
conditions of the experiment: i) Group Control: no treatment, ii) Group
Read: treatment of reading a research paper, named thenceforth as task
Read (see 3.3.3), iii) Group Play: treatment of using challenge-based
gamification, named thenceforth as task Play (see 3.3.4) and iv) Group
Read&Play: both tasks: “Read”and “Play”. Time was controlled and
was equal to 15 minutes for each task. Table 1, depicts the design of the
evaluation of our experiment and all the treatments are explained in the
following section.
3.3. Materials
3.3.1. Lecture
The learning objectives of this lecture were for students to under-
stand and be able to apply the “Method Selection Protocols”for reg-
ular/fast-moving and intermittent demand time-series based on specific
academic work of Petropoulos et al. (2014). During the lecture, the aim
of the Petropoulos et al. (2014) research was mentioned, along with the
data, and the research methodology used. Special attention was paid on
the results, the practical implications and the conclusions of this study
about the “Method Selection Protocols”. More precisely, we further
explained the relation between the time-series features and one stra-
tegic decision and the forecasting accuracy of the proposed methods for
both regular/fast-moving and intermittent demand time-series. The
visual material of the lecture was composed of 17 slides and lasted 15
minutes. The lecture content was focused on specialized knowledge of
the research of Petropoulos et al. (2014) that both undergraduate and
MBA students in any of the stages of their studies would not have
otherwise been taught.
3.3.2. Final evaluation form
An evaluation form at the end was used to measure students'
learning performance via 30 close-ended questions (i.e. questions
where the participants would select the right answer among possible
answers) of equivalent grade about the findings
of Petropoulos et al. (2014). This was the last task for all participants,
independently of the group to which they were assigned. The answers
to all the questions were covered in the lecture material. All the ques-
tions were about topics that have been discussed in the lecture de-
scribed at subsection 3.3.1, and therefore, in principle it would be
possible to attain the highest score by only participating in the lecture.
3.3.3. Task read
The material of the reading task was: “Petropoulos, F., Makridakis,
S., Assimakopoulos, V., Nikolopoulos, K., 2014. ‘horses for courses’in
demand forecasting. European Journal of Operational Research 237,
152 -163.”. The paper is 12 pages and is the foundational material of
the lecture content. The students read the article using a computer in
the computer lab.
3.3.4. Task play: Challenge-based gamification
Since there is a lack of free, computationally non-complex gamified
applications, specifically created to teach statistical forecasting, we
developed a gamification approach called Horses for Courses.Itisa
simple gamified application, which is composed of main design patterns
related to challenge-based gamification, as described in Section 3.3.4.2.
Horses for Courses aims to motivate students' participation, to improve
their learning outcomes regarding the choice of simple but accurate
statistical forecasting methods, and consequently enhance their fore-
casting skills. It is structured based on the above-mentioned founda-
tional material: “Petropoulos, F., Makridakis, S., Assimakopoulos, V.,
Nikolopoulos, K., 2014. ‘horses for courses’in demand forecasting.
European Journal of Operational Research 237, 152 -163.”.
3.3.4.1 Horses for Courses Architecture
In order to implement Horses for Courses, we considered the methods
and design principles of both gamification (Morschheuser et al., 2018;
Zichermann and Cunningham, 2011) and software
development (Barnett et al., 2005; Gallaugher and Ramanathan, 1996;
Lewandowski, 1998). As far as the architecture of the application is
concerned, a focus on flexibility, accessibility, high-level programming
and the ability to be integrated in different platforms led us to build a
web application on the Microsoft.NET framework (Barnett et al., 2005)
and to use an MS-SQL database. Horses for Courses is a web-based ga-
mified application, structured as a three-tier system with simple ap-
plication logic layer, which is fully accessible to registered users via a
browser. Users register with an email and a password in order to save
their progress in a database scheme, which serves as a data tier. A “Data
Module”is used to retrieve data from the database and to build it into
functional objects. A class named “Actions”, enables the interaction
between users and the “Data Module”in order to save updated data and
the user’s progress back into the database, composing the logic tier of
the application. The presentation tier consists of a graphical user in-
terface, including time-series, data visualization and system functions.
A compact graphical representation of the Horses for Courses application
is found in Fig. 1.
3.3.4.2 Horses for Courses Design
Guidelines for the design of the challenge-based gamification and
consequently of Horses for Courses application were divided into two
main directions: (1) the effective use of motivational affordances in
Table 1
Design of the evaluation of the experiments.
Task Description Group Control Group Read Group Play Group Read&Play
Attend Lecture (see 3.3.1)
✓
✓
✓
✓
Read the Paper (see 3.3.3)
✓
✓
Play (see 3.3.4)
✓
✓
Evaluation Form (see 3.3.2)
✓
✓
✓
✓
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
4
learning environments (Deterding et al., 2011; Dicheva et al., 2015;
DomíNguez et al., 2013; González and Area, 2013; Hanus and Fox,
2015; Maican et al., 2016; Nah et al., 2014; Pedreira et al., 2015;
da Rocha Seixas et al., 2016; Yildirim, 2017) and (2) the design and
development of gamified applications (Kapp, 2013; Morschheuser et al.,
2018; Zichermann and Cunningham, 2011). The most frequently used
and assessed motivational affordances in education and in general, so
far are: points, levels, badges/achievements and
leaderboards (Alhammad and Moreno, 2018; Koivisto and Hamari,
2019; Majuri et al., 2018; Pedreira et al., 2015). Thus, we incorporated
points, a level setting, challenges and a leaderboard into our application
in order to maximize the external validity of the experiment - i.e. to
mimic a possible real world implementation of this gamification style.
More precisely, Table 2 describes the motivational affordances in Horses
for Courses, along with their definitions from the literature and the
purpose they serve.
Apart from these motivational affordances, our design decisions
regarding Horses for Courses were also determined by a desire to create
a user-friendly and agile interface and work flow, with clear player
guidance and instructions (Kapp, 2013). Fig. 2 describes a full round of
the game. Initially, students had to register or sign in. Later, for each
level they had to select the most suitable forecasting method based on
the provided data and information, as it is depicted in Fig. 3. Then
participants win points according to their choices. Instructions are ea-
sily accessible as well as the“Method Selection Protocols”, through
colorful buttons as Fig. 3 illustrates, as well. New challenges arise at
each level, for example to identify time-series components for the real
data time-series as it is depicted in Fig. 4, encouraging the students to
assess their knowledge and win more points. The aim is to achieve a
high ranking on the final leaderboard, based on the collected points.
3.4. Procedure
The experiments took place as a replacement of a normal lecture of
the respective course. Therefore, participants (students) arrived at the
lecture as normal at the designated computer lab at the standard time.
After arriving at the class, the participants were informed about the
experiment and their informed consent was obtained. At ECE, NTUA,
participation was voluntary, however, the incentive for participation
was a bonus of 0.5/10 in the course’sfinal grade, instead of an
equivalent exercise in the final examination. In such manner, every
student, participating or not in the experiment, could receive the
highest grade in the final examination. The participation of under-
graduate students at Business Administration was mandatory as part of
the course and there were no additional incentive for them to take part
in the experiment.
Participants were instructed that they should pick a computer sta-
tion at the classroom (a computer lab), attend a lecture and then
complete an evaluation form, which was based on the content of the
lecture. They were informed that their performance in the evaluation
form would not affect their course grade, however, they should try to
correctly answer the questions based on their understanding of the
topic described in the lecture. The experimenter mentioned that the
participants would be randomly divided into four groups, without
further information, but the importance of the evaluation form along
with the time constraints for all groups was highlighted.
First part of the experiment procedure was a 15-minute lecture
about the findings of Petropoulos et al. (2014) research (see 3.3.1).
After the lecture, the participants were randomly assigned to different
conditions of the experiment: Group Control, Group Read, Group Play
and Group Read&Play. All students, independently of their group re-
ceived the same incentive in order to eliminate the recruitment bias.
Then, the experimenter informed the participants about their next task
and the available time. Instructions were given for each group respec-
tively. Each group had 15 minutes to complete the respective task. As
described above, Group Control did not have an extra task, Group Read
had 15 minutes to read the paper (task Read), Group Play had 15
minutes to fulfill a full round in the gamified application (task Play).
Group Read&Play had 30 (15 + 15) minutes to fulfill the task Read and
then the task Play. Finally, all groups had to complete the evaluation
Fig. 1. Horses for Courses architecture.
Table 2
Integrated motivational affordances in Horses for Courses application.
Affordance Definition Purpose in Horses for Courses
Points Numeric measure of players’performances. Reward for the correct application of method selection protocol.
Levels Difficulty moderated based on players’expertise. Indicator of progression and difficulty.
Challenges Predefined quests and increasingly more difficult objectives. Positive impetus to keep players engaged to maximize their points.
Leaderboard Direct comparison of players’performance. Increase of competition among students.
Source: (Buckley and Doyle, 2017; Bunchball, 2010; Kapp, 2013; Maican et al., 2016; Nah et al., 2014; Seaborn and Fels, 2015; Zichermann and Cunningham, 2011).
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
5
form within 15 minutes, which measured their performance (see Ma-
terials 3.3.2). Participants completed all of the tasks’stages in-
dependently and were not allowed to communicate with other parti-
cipants. The procedure was the same at both participating campuses,
including the materials and the experimenter.
4. Results
The objective of this study is to identify the impact of the challenge-
based gamification on students’performances in the evaluation form
and consequently on their comprehension. Thus, we examine the re-
lative performance of a control group in comparison with that of the
treatment groups. Students’performances on the evaluation form were
calculated as the sum of the right answers on the questionnaire, nor-
malized to a maximum of 100. Summary statistics of results are pre-
sented in Table 3 for each treatment along with the students’gender,
their academic major: ECE, NTUA or Business Administration and their
educational level: Undergraduate or MBA. The distribution of the per-
formances is illustrated in percentiles with box-plot diagrams in Fig. 5,
which are further separated into different treatments, including gender,
major and educational level. Finally, for a subset of the sample
(N=146), three extra variables have been examined along with the
treatment groups, namely students’expertise in English, and use of
personal computers and games. Students’responses were ranging from
1=beginner to 5=proficient. Horses for Courses is a web-based gami-
fied application which demands the use of a personal computer and the
language of its interface is English. Therefore, we examined the re-
lationship between these variables and students’performances by
testing if these variables would be statistically significant in students’
performances in conjunction with the treatment received.
The analysis of the results was conducted in three steps. First, we
investigated the mean values of the students’performances and their
statistically significant differences with respect to specific treatments.
Table 3 presents the number of students per treatment, the mean value
of the students’performances, and the standard deviation in each
treatment group, regarding their gender, academic major and educa-
tional level. Overall, the groups that experienced the challenge-based
gamification achieved greater mean values of performances than the
other groups. More precisely, Group Read&Play, which read the re-
spective paper and used the gamified application, reached the highest
mean performance of 58.05 out of 100, and had the second lower level
of standard deviation in results (SD=17.00). Group Play, which only
experienced the gamified application, had the second highest mean
performance of 52.55 out of 100 and the highest level of standard de-
viation in results (SD=19.74). Group Read had a lower mean perfor-
mance of 46.13 out of 100 (SD=18.68). Finally, Group Control had the
Fig. 2. Horses for Courses’flowchart of a full game round.
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
6
lowest mean performance, 36.13 out of 100, but also the lowest stan-
dard deviation (SD=11.57).
Based on the Shapiro-Wilk test on the ANOVA residuals, the as-
sumption of normality was violated. In order to study the significant
differences in the average values of all the groups’performances, we ran
the non-parametric Kruskal-Wallis rank sum test (Kruskal and
Wallis, 1952). The null hypothesis of equal differences is rejected (chi-
squared=70.842, df=3, p<0.001) and we can therefore establish
significant differences between the groups. Furthermore, we conducted
pairwise multiple comparisons without making assumptions about
normality, using the Dunn procedure (Dinno, 2015; Dunn, 1961; Zar
et al., 1999), with a confidence interval equal to 95%. Kruskal-Wallis
with Dunn’s post-test was chosen to test the significant differences be-
cause data was not normally distributed in all cases. Table 4 presents
the outcomes concluding that all treatment groups resulted in sig-
nificantly higher performances compared to Group Control (p.
adj<0.001, Kruskal-Wallis with Dunn’s post test). Additionally, Table 4
displays the respective effect size of these treatments compared to
Group Control, based on non-parametric Cliff’s Delta estimator (Cliff,
2014; Macbeth et al., 2011; Wilcox, 2006). The only pairwise com-
parison without statistically significant differences in students’perfor-
mances is Group Play versus Group Read&Play. Finally, Group
Read&Play outperformed all the other groups. This group noted the
highest improvement regarding the mean values of performances of
Group Control equal to 60.67%. Group Play and Group Read follow
with improvement equal to 45.45% and 27.68% respectively.
The performances of all groups were compared directly, focusing on
the assessment of questions in the evaluation form, despite that treat-
ments did not have the same duration. In order to deal with this
limitation of our study, we used independent binary variables for the
tasks Read and Play respectively. The value of the variable Read is
equal to 1 if the respective group completed the task, and 0 otherwise.
The same applies for the variable Play. Then, the Scheirer-Ray-Hare test
was performed (Scheirer et al., 1976), using students’performances and
these variables. Results show that each of the tasks: Read the research
(H=16.014, p<0.001) or Play with Horses for Courses application
(H=52.81, p<0.001) had a significant impact on students’perfor-
mances, but their interaction was not significant (H=2.019, p=0.156).
Along with the impact of different treatments on students’perfor-
mances, the independent variables gender, academic major and edu-
cational level were examined using the Scheirer-Ray-Hare test. While
the students’gender and their academic major appeared to have a great
impact on their performances, based on the results presented in Table 5,
the interactions between them and the respective treatments they un-
derwent did not have significant impact. The students’educational level
was not an important variable, nor was its interaction with the treat-
ments.
Regarding the impact of additional variables on a subset of our
sample, only the impact of the students’expertise in English resulted in
statistically significant differences in students’performances. The last
rows of Table 5 show the results of the Scheirer-Ray-Hare test for the
respective samples. Results about the mean values and the standard
deviations of these extra variables are presented in Table 6. However,
these variables will not be further analyzed because students reported
their answers only in the recent experiments.
Table 7 demonstrates the impact of different treatments on students’
performances, regarding the statistically significant variables of gender
and academic major. We calculated the improvement of more specified
Fig. 3. View of 1
st
level challenges of Horses for Courses application.
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
7
groups regarding these variables compared to the mean value of the
students’performances of Group Control (equal to 36.13) as a bench-
mark value. Students at the ECE, NTUA have noted the highest im-
provement regarding the mean values of their performances in the
evaluation form. Furthermore, female students, independently of their
major, who had used the gamified application benefited more from
gamification; their mean performances are higher from those of the
respective groups composed of male participants. These findings do not
apply in non-gamified groups.
To conclude, we divided all the data of students’performances into
two larger groups, instead of four: the non-gamified group, composed of
165 students (M=41.04, SD=16.22), who did not use the Horses for
Courses application (Group Control and Group Read) and 200 students
(M=55.30, SD=18.58) who used it (Group Play and Group
Read&Play), the gamified group. We adopted this approach in order to
examine the overall impact of the challenge-based gamification on
students’learning outcomes. Fig. 6 illustrates the students’perfor-
mances for each group in percentiles with box-plot diagrams. Normality
is not confirmed, thus Wilcoxon-Mann-Whitney rank sum test was
performed, with a confidence interval equal to 95%. The null hypoth-
esis of equal differences in means is rejected (W=23821, p<0.001),
while the use of Horses for Courses presents a moderate level of impact
Fig. 4. View of 4
th
level of Horses for Courses application.
Table 3
The challenge-based gamification results per treatment and variable.
Variable Group Control Group Read Group Play Group Read&Play
n M SD n M SD n M SD n M SD
Gender
Female 15 27.08 10.07 19 35.14 19.19 28 49.08 20.46 33 57.81 18.75
Male 69 38.10 10.98 62 49.49 17.32 72 53.90 19.43 67 58.16 16.21
Academic major
ECE, NTUA 61 39.56 10.58 65 50.56 16.55 74 59.78 15.67 79 61.53 15.28
Business Administration 23 27.04 8.97 16 28.13 16.18 26 31.97 15.19 21 44.94 17.07
Educational Level
UG 71 36.28 11.87 68 47.59 19.10 85 52.45 21.03 83 56.00 16.90
MBA 13 35.34 10.16 13 38.46 14.62 15 53.13 10.02 17 68.01 14.00
Total 84 36.13 11.57 81 46.13 18.68 100 52.55 19.74 100 58.05 17.00
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
8
based on non-parametric Cliff’s Delta estimator (delta estimate=0.44
(medium)) and an improvement regarding mean values of perfor-
mances, equal to 34.75%.
Students’performances in the final evaluation form (questionnaire)
should not be confused with their game performances. Weak positive
correlation was found between students’performances at the final
evaluation form and their game performances for students who ex-
perienced the challenge-based gamification (r(146)=0.339, p <0.001).
However, the value of the Pearson’s Correlation Coefficient is calcu-
lated only for a subset of students (N=148), who used the gamified
application, since there was no specific instruction to students to use
the same personal details in the gamified application and in the eva-
luation form.
Fig. 5. Students’performances per treatment and variable.
Table 4
Pairwise multiple comparisons among the groups based on Kruskal-Wallis with
Dunn’s post test and CliffDelta effect size.
Groups Z P.adj Delta
estimate
Improvement (%)
Control vs. Read -3.70 0.001 0.35
(medium)
27.68%
Control vs. Play -6.16 <0.001 0.51 (large) 45.45%
Control vs. Read&Play -8.04 <0.001 0.69 (large) 60.67%
Play vs. Read 2.25 0.049 - -
Play vs. Read&Play -1.96 0.05 - -
Read&Play vs. Read 4.10 <0.001 - -
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
9
5. Discussion of results
Overall, the results suggest that the challenge-based gamification
improves learning outcomes in a forecasting course. This type of ga-
mification presents the greatest improvement in students’performances
when it is combined with traditional teaching methods. However, our
results show that even this gamified application alone, integrated in a
lecture, may have a positive impact on learning outcomes.
In general, groups who experienced the challenge-based gamifica-
tion have greater performances than the groups that only participated
in traditional teaching methods such as only attending the lecture
(Group Control) or reading the paper (Group Read). More precisely, the
group whose participants read the respective paper and used the ga-
mified application had the highest performance regardless of their
gender, academic major and level of studies. However, the mean value
of the performances of this group is not statistically significant different
from the group who only used the gamified application. Despite the fact
that Group Read&Play had extra 15 minutes to read the respective re-
search and then use the gamified application, the interaction of these
two tasks did not seem to have a great impact on students’perfor-
mances; while each of the tasks, Read or Play did. According
to Fisher et al. (1981), the amount of time that students are focused or
engaged in an activity is generally positively associated with their
learning outcomes. Given that, we might speculate that the students
who had to complete two tasks may not have been fully engaged
throughout the duration of the tasks. Nevertheless, the aim of our study
is to investigate the impact of gamification on students’performances.
So, based on our analysis the use of this gamified application presents
an improvement regarding the mean values of performances, equal to
34.75%. In addition, the challenge-based gamification may improve
students’performance by up to 89.45% compared to only being present
at a lecture. Under certain conditions, the use of gamification within
less time, may have almost the same impact as reading and using the
gamified application, as far as learning outcomes in forecasting are
concerned.
Moreover, we can state that a gamified application combined in a
lecture may improve learning outcomes at both schools. However, its
impact is even more important in the case of engineering students,
where both female and male participants had significantly better per-
formances. This finding is in agreement with the fact that gamification
has already been incorporated into software engineering and math/
science education to a greater degree (Alhammad and Moreno, 2018;
Dicheva et al., 2015; Pedreira et al., 2015). Dicheva et al. (2015) argue
that one possible explanation could be the lack of fully customized
gamified applications in a variety of educational fields or the fact that
instructors in these schools are more qualified to develop such appli-
cations. Another explanation might be that gamification helps to in-
crease student interest in difficult concepts in
engineering (Markopoulos et al., 2015). Thus, engineering students
may benefit more by experiencing these subjects as more manageable.
Although the research in this field is at a preliminary stage (Alhammad
and Moreno, 2018; Pedreira et al., 2015), Pedreira et al. (2015) support
that gamification has great potential in software engineering education
mainly because of the nature of tasks, which demand high motivation
Table 5
Impact of the treatments, variables and their interactions.
Variables Gender Academic major Educational Level
Groups (N=365) df H Sig. df H Sign. df H Sign.
Treatment (df=3) 1 7.74 0.005 1 73.41 <0.001 1 0.20 0.657
(H=70.84, p<0.001)
Interaction 3 5.10 0.164 3 7.02 0.071 3 7.63 0.054
Variables English Proficiency PC Expertise Game Expertise
Groups (n=146) df H Sig. df H Sign. df H Sign.
Treatment (df=3) 4 15.26 0.004 4 7.83 0.098 4 0.51 0.972
(H=24.77, p<0.001)
Interaction 9 2.58 0.995 9 5.41 0.797 12 11.31 0.502
Table 6
Students’performances per treatment and extra variables.
Group Performance English Proficiency PC Expertise Game Expertise
Control M=32.3 M=3.36 M=3.43 M=3.29
(n=28) SD=11.4 SD=1.37 SD=1.14 SD=1.49
Read M=44.4 M=3.59 M=4 M=3.37
(n=27) SD=18.4 SD=1.39 SD=0.83 SD=1.04
Play M=43.2 M=3.77 M=3.96 M=3.26
(n=47) SD= 20.6 sd=1.37 SD=1.02 SD=1.15
Read&Play M=56.7 M=4.23 M=4.07 M= 3.27
(n=44) SD= 20.0 SD=0.96 SD=0.90 SD=1.34
Table 7
Improvement of students’performances per treatment, gender and academic major.
Group Academic major Gender n M SD Improvement (%) Delta Est.
ECE, NTUA Female 4 32.81 5.41 -9.18 -0.173 (small)
Control (9.49%) Male 57 40.03 10.72 10.80 0.195 (small)
(0%) Business Administration Female 11 25.00 10.73 -30.81 -0.524 (large)
(-25.17%) Male 12 28.91 6.94 -20.00 -0.388 (medium)
ECE, NTUA Female 12 44.44 16.74 23.00 0.283 (small)
Read (39.93%) Male 53 51.94 16.35 43.76 0.594 (large)
(27.66%) Business Administration Female 7 19.20 11.04 -46.87 -0.731 (large)
(-22.16%) Male 9 35.07 16.59 -2.94 -0.193 (small)
ECE, NTUA Female 15 59.95 16.07 65.91 0.775 (large)
Play (65.45%) Male 59 59.74 15.70 65.34 0.779 (large)
(45.44%) Business Administration Female 13 36.54 17.97 1.13 -0.068 (negligible)
(-11.51%) Male 13 27.40 10.61 -24.15 -0.424 (medium)
ECE, NTUA Female 16 68.45 13.70 89.45 0.935 (large)
Read&Play (70.30%) Male 63 59.77 15.26 65.43 0.764 (large)
(60.65%) Business Administration Female 17 47.79 17.53 32.28 0.400 (medium)
(24.38%) Male 4 32.81 7.86 -9.18 -0.179 (small)
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
10
by students (Pedreira et al., 2015). Our results strengthen this state-
ment, indicating that challenge-based gamification was even more ef-
fective in engineering students’performances, without neglecting its
potential in business schools, as well.
Another finding, based on the results presented in Table 7, is that
female users of this gamified application, independently of their edu-
cational background, had higher performances and a higher level of
improvement compared to their male counterparts. However, this
finding does not apply in non-gamified groups (Group Control and
Group Read). More specifically, the female participants of gamified
groups at the ECE, NTUA have achieved the highest performances and
the highest improvement. Differences in motivations (Carr, 2005) and
activities that engage players (Codish and Ravid, 2017; Koivisto and
Hamari, 2014), within genders have already been mentioned. Thus,
since female participants are generally more motivated by challenge
than by competition (McDaniel et al., 2012), females students would
probably be even more motivated by challenge-based gamification,
which would lead to better performance. Another possible explanation
could be that female students receive higher levels of playfulness in a
gamified educational content (Codish and Ravid, 2015), which in-
creases their motivation and improves their learning outcomes. It is
important to note that while the difference in the sample size of the
genders may be the cause of the results, this assumption requires fur-
ther exploration, and for the time being we may conclude that both
female and male students appear to benefit from
gamification (O’Donovan et al., 2013).
Last but not least, it is surprising that the variables of students’
expertise in use of personal computers and games were not statistically
significant for the subset of the participants who reported this data
(engineering students=60, business school students=66 and MBA
students=20). However, based on the results of Table 6, these groups
have similar mean values regarding these variables, sharing probably
common characteristics. The students’expertise in English is another
parameter which had a statistically significant impact on their perfor-
mances, since the slides, the research and the interface of the applica-
tion are all in English. The difference between the mean values of this
variable, achieved by the engineering students (M=4.45, SD=0.86)
and business school students (M=3, SD=1.29) could justify the lower
performances of the business school students in the evaluation form for
all groups.
5.1. Limitations
While challenge-based gamification has a positive impact on
learning outcomes for both engineering and business school students,
some limitations should be acknowledged. We gathered and compared
performances of all groups although the treatments did not have the
same duration as the tasks did. However, this study focuses on the as-
sessment of the evaluation form (questionnaire), which was the same
for all groups and experiments, in order to evaluate the challenge-based
gamification impact. Although conducting both pre- and post-test
would provide further methodological rigor, in the scope of the present
study, the extant knowledge of the participants could be assumed to be
homogeneous because of the specialized content of the lecture. The
content of the lecture, and consequently the topic of the gamified ap-
plication and the evaluation form are focusing on the specific topic of
“Method Selection Protocols”(Petropoulos et al., 2014). Thus, students
in any of the stages of their studies would have not otherwise learned
this topic. Therefore, the present study’s design was economized by
conducting only the post-test of knowledge on the topic. Furthermore,
engineering students noted better performances than business school
students. This fact is probably due to the discrepancy between en-
gineering and business school students in their proficiency in English
and in their years of studies.
Additionally, another possible limitation is that there is a difference
between the two schools in terms of the incentives to participate in our
experiments. Finally, although non-parametric tests have been con-
ducted because of the fact that data was non-normal or heteroscedastic
or both, the differences in the sample size of different groups should be
considered as a limitation (i.e. 307 undergraduate students versus
58 MBA students). Thus, further experiments could contribute to the
conclusions of this study.
6. Conclusion
Overall this study contributes to the core literature of how gamifi-
cation affects desired outcomes (i.e. skills, knowledge, motivations and
behavior). According to several state-of-the-art analyses of the
field (Koivisto and Hamari, 2019; Majuri et al., 2018; Nacke and
Deterding, 2017; Rapp et al., 2019), there has been a relative gap of
randomized controlled experiments that could reliably show effects of
gamification. Therefore, this study contributes to the corpus via such an
Fig. 6. Students’performances of non-gamified and gamified groups.
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
11
experiment by showing that challenge-based gamification (i.e. im-
plementation including points, levels, challenges and a leaderboard)
improves learning outcomes. Our findings contribute to the literature of
serious games (Connolly et al., 2012) and game-based learning (Hamari
et al., 2016; Squire, 2003), as well. More specifically, the study informs
the area of scientific and statistics education (Chu, 2007; Love and
Hildebrand, 2002), because the object of learning in the experiment
was in forecasting techniques. The contribution in this area gets more
importance by considering the need for updating and making more
engaging the traditional teaching methods (Love and Hildebrand, 2002;
Surendeleg et al., 2014), since the statistical skills are fundamentals to
get insight of the available data in order to increase awareness towards
social problems and understanding our world.
In this study, we conducted a factorial design experiment, using a
developed gamification approach named Horses for Courses, which
provides valuable empirical evidence on how challenge-based gamifi-
cation and reading differently influence learning performance. The
findings of our empirical study, based on a quantitative analysis of our
results, are in line with the positive effects of gamification on
learning (Buckley and Doyle, 2016a; Hamari et al., 2016; Kuo and
Chuang, 2016; Maican et al., 2016; da Rocha Seixas et al., 2016; Simões
et al., 2013; Yildirim, 2017) as well as on software engineering edu-
cation (Alhammad and Moreno, 2018; Pedreira et al., 2015). The results
demonstrate that the challenge-based gamification improves students’
performances by 34.75% regarding a statistical forecasting topic and
that the effect was larger for females or engineering students. The
greatest improvements take place when gamification is combined with
traditional methods such as reading, however even simply integrating a
gamified application into a lecture benefits students.
This research sheds light upon the effect of challenge-based gami-
fication on statistics education by demonstrating improvement in
learning outcomes. Apart from the theoretical contribution, this study
also provides practical implications to gamification designers and
educators. Challenge-based gamification (i.e. points, levels, challenges
and leaderboard), can be effectively combined with traditional teaching
methods such as lectures and reading in order to improve the learning
outcomes in a variety of educational fields related to statistics and stem
education. Finally, gamification designers should take into account
students’profiles, since our results show that benefits differ across
students’characteristics.
With our study, we position challenge-based gamification as a useful
educational tool in statistics education in different academic majors
under certain circumstances, but also we acknowledge its limitations.
Further investigation of the effects of individual game elements or
different gamified approaches in statistics or data-related courses with a
larger sample is necessary, in order to enhance the scope of the research
and further refine its findings. An extension of our research could be to
investigate the impact of additional motivational affordances combined
or compared with challenge-based gamification, under proper and
cautious design. These additional affordances could be related to the
actual content of the course or the actual behaviors that the instructors
want to promote, e.g. social sharing or responding to forum questions.
CRediT authorship contribution statement
Nikoletta-Zampeta Legaki: Conceptualization, Methodology,
Software, Formal analysis, Investigation, Writing - original draft,
Writing - review & editing, Visualization, Validation, Funding acquisi-
tion. Nannan Xi: Conceptualization, Writing - original draft, Writing -
review & editing. Juho Hamari: Conceptualization, Methodology,
Writing - original draft, Writing - review & editing, Supervision,
Funding acquisition. Kostas Karpouzis: Methodology,
Conceptualization. Vassilios Assimakopoulos: Conceptualization,
Methodology, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgments
Special thanks to the students of the forecasting courses at the
School of Electrical and Computer Engineering of the National
Technical University of Athens, Greece and to the students at the
Business Administration Department in the School of Business and
Economics of the University of Thessaly, Greece who participated in our
experiments and helped to investigate the effect of challenge-based
gamification on student learning.
An early version of this study was presented at the International
GamiFIN Conference 2018, at the HICSS-52 2019 52nd Hawaii
International Conference on System Sciences and at the International
GamiFIN Conference 2019. This work was supported by the European
Union's Horizon 2020 research and innovation program under the
Marie Sklodowska-Curie [grant agreement ID 840809]; Business of
Finland [Grant No. 5654/31/2018]; Business of Finland [Grant No.
4708/31/2019]; Liikesivistysrahasto [Grant No. 14-7798].
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Nikoletta-Zampeta Legaki is a Marie - Curie Researcher at
Gamification Group, Tampere University. She pursuits her
PhD in School of Electrical and Computer Engineering of
National Technical University of Athens (NTUA), Greece.
She has been a researcher and teaching assistant in
Forecasting and Strategy Unit, School of Electrical and
Computer Engineering since 2012. During this period, she
has participated in various research projects about fore-
casting and data analytics and she has worked as a con-
sultant in Financial Services and Risk Management, in EY,
Greece. Her research interests lie on time series forecasting,
business forecasting information systems, gamification and
educational methods in teaching forecasting.
Dr. Nannan Xi is a Postdoctoral researcher in Gamification
Group. She got her PhD in marketing management from
Zhongnan University of Economic and Law (ZUEL), China
and holds M.Sc. in International Business from the
University of Lincoln, UK. Her dissertation was awarded as
the excellent Ph.D. dissertation in ZUEL. Xi’s current re-
search focuses on gamification in marketing, especially in
gamified interaction in brand management. In addition, her
research interests include customer management in gami-
fication and virtual reality/augmented reality/mixed rea-
lity in business and sharing economy.
Dr. Juho Hamari is a Professor of Gamification at the
Faculty of Information Technology and Communications,
Tampere University. He leads the Gamification Group. His
research covers several forms of information technologies
such as games, motivational information systems, new
media (social networking services, eSports), peer-to-peer
economies (sharing economy, crowdsourcing), and virtual
economies. Dr. Hamari has authored several seminal em-
pirical, theoretical and meta-analytical scholarly articles on
these topics from perspective of consumer behavior,
human-computer interaction, game studies and information
systems science. His research has been published in a
variety of prestigious venues.
Dr. Kostas Karpouzis is currently an Associate Researcher
at the Institute of Communication and Computer Systems
(ICCS) of the National Technical University of Athens
(NTUA) in Greece. His research interests lie in the areas of
human computer interaction, emotion understanding, af-
fective and natural interaction, serious games and games
based assessment and learning. He has participated in more
than twenty research projects at Greek and European level.
He is also a member of the Student Activities Chair for the
IEEE Greece Section and a member of the Editorial Board
for international journals.
Prof. Vassilios Assimakopoulos is a professor of
Forecasting Systems at the School of Electrical and
Computer Engineering of the National Technical University
of Athens (NTUA). He is the author of over than 60 original
publications and papers in international journals and con-
ferences. Moreover, he has conducted research on in-
novative tools for management support and decision sys-
tems design. He has participated and led numerous projects,
funded by National and European institutes. He specializes
in various fields of Strategic Management, Design and
Development of Information systems, Business Resource
Management, Statistical and Forecasting Techniques using
time series.
N.-Z. Legaki, et al. International Journal of Human-Computer Studies 144 (2020) 102496
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