Special Article : Traditional and Digital Game Preferences of Children
Asimoglu (2012), as a result of a study on teaching preschool students traditional games via
creative drama, stated that by the help of drama, students’ interest in the subject increased,
the subject triggered curiosity, and rhythmic, musical and kinetic skills developed.
Birsen (2017), based upon a study where they achieved foreign language education via the
method of game-based learning using a modernized form of traditional games, Pictionary, for
students on the level of primary education, found that the success and perceptions of students
regarding learning words increased, and stated that traditional games should be utilized as an
effective factor in increasing interest towards and performance in a subject.
Iwata, Yamabe, Polojarvi and Nakajima (2010) stated that card games are still attractive, and
the emotional effect of a game is increased by tangible game objects and spatial interaction.
Traditional games develop children’s learning, creativity, imagination and social connections
(Parson, 2011). Most parents and educators agree that playing is essential for healthy
development of children (Clements, 2004).
Huizenga, Admiraal, Akkerman, ten Dam (2009) found as a result of the study they conducted
for teaching history to middle school students using digital games that the success of students
in the learning field increased significantly and they were more willing to attend activities.
Kula and Erdem (2005), in their study on providing primary education students with arithmetic
skills via educational digital games, determined that students’ quantitative success did not
change, but their qualitative responses changed from simple to more complex. Additionally,
they reported increases in students’ in-class interactions and motivations towards the subject.
In general, it is seen that it was attempted to support traditional learning environments by
games, dramatization and card games (Amory & Seagram, 2003), and gather learners together
with different interfaces such as 2D, 3D augmented-reality-supported platforms and massively
multiplayer online role-playing games (MMORPG) (Hamalainen, 2008; Zhong, 2011). Games,
which have an important place in the instruction process, are also preferred to create
competition, challenges, social communication, diversity, and dreamy environments (Amory,
Naicker, Vincent & Adams, 1999; Bakar, Tuzun & Cagiltay, 2008; Lucas & Sherry, 2014).
Prensky (2002) listed the characteristics of students of the 21st century, whom he defined as
digital natives, as multitasking, preferring graphical content more, wanting to reach
information fast, technology-friendly, and preferring to learn by discovering and gaming. These
characteristics lead us to digitalization and elements of games. Characteristics of digital games
such as active participation in the process, customization for the user, socializing in the virtual
environment, entertainment, sense of success and rewarding the user overlap with the
characteristics of digital natives completely (Agaoglu & Metin, 2015; Downey, Hayes & Brian,
2004; Esen, 2008; Inal & Cagiltay, 2005; Nedim Bal & Metehan, 2016; Van Rheenen, 2012).
Accordingly, it may be argued that digital games are some of the most ideal learning
environments for digital natives and the generation in question learns best via games
(Karlsson, 2007; Kiili, 2005; Prensky, 2003).
Studies reported that, while the time spent by children playing games at home and in video
game arcades was 4 hours a week on average in 1980s; this time increased up to 5.5 hours for
girls and 13 hours for boys in 2004 (Christakis, Ebel, Rivara & Zimmerman, 2004; Fis Erumit,
2016), and it was suggested that it is a necessity to include this game-playing time in the time
children spent learning (Fromme, 2003; Funk, 1993).
In addition to contributing to the learning of students in a relevant field, digital games are
known to increase students’ attention and motivation via fun learning environments that are
created in games, and contribute to development of self-esteem and self-efficacy, problemsolving, and strategic and algorithmic thinking skills (Akpinar, 1999; Batdi, 2017; Bayirtepe &
Tuzun, 2007; Bottino, Ferlino, Ott & Travella, 2006; Demirel, Seferoglu & Yagci, 2003; Prensky,
2001). With these characteristics, digital games provide children with opportunities for
learning. For example, they experience being a pilot while using plane simulators or being an
engineer while building cities. Therefore, it is clear that learning environments prepared by
considering student characteristics, readiness levels and experiences will create more
meaningful learnings for them (Garris, Ahlers & Driskell, 2002; Gunes, 2015; Karamustafaoglu
& Kaya, 2013; Tural, 2005). The Turkish Ministry of National Education also includes more
efforts in recent years in this regard for developing digital games for almost every subject
(URL-2017). For this process to be effective and learning-based, one needs to look at the
theoretical basis of game-based learning.
Figure 1. A Model of Game-based Learning [Garris, Ahlers & Driskell, 2002]
Game-based learning used games as a reinforcing/complementary factor in order to develop
capability in specific subjects. Such games either provide preliminary learning or replace
learning (Cankaya & Karamete, 2008; Kearney & Pivec, 2007). In difference to gamification, this
does not concern the entirety of the learning process (Arabul Yayla, 2015; Karatas, 2014; Sahin
et al., 2017). As in the chart that represents the learning process as given in the figure above,
in the game cycle into which educational content and the game enter together, educational
content is in a blurred formation with the characteristics of the game (Prensky, 2001). The
response to the action of the player comes through feedbacks from the system. The player,
this way, starts to discover the structure in the game and adapt to the discovered structure
(Kula & Erdem, 2005; Prensky, 2001). The connection between the game cycle and learning
outputs occurs in the process of questioning. This process involves the players’ adoption and
application of what is learnt in the game in real life (Garris, Ahlers & Driskell, 2002). Learning
outputs following the process of questioning may be generally listed as cognitive, sensory and
motor skills (Deterding, Dixon, Khaled & Nack, 2011). It should be ensured that the outcomes
in question are provided to the player in the cycle of experience by dividing these outcomes
into small steps. The emotional dimension of the game is about achieving a given task. A game
rewards the player with points, cups, etc. in exchange of success. Failure leads to removal of the player from the given task (Domínguez et al., 2013). The most important concept here is
the motivation of the player. If the motivation is achieved from within the student without the
need for an external influence, this is internal motivation. Internal motivation is performance
of learning activities by a person’s own will (Fis Erumit, 2016).
It is stated that especially the educational games in digital environments will not only support
their active participation in the process but contribute to their interactions with peers and
connection to the process of learning (Caglar & Arkun Kocadere, 2015), and when
entertainment, motivation and dedication to the setting are prioritized in addition to learning,
quality will also increase (Cankaya, 2007). It was hoped that the educational potential of
educational digital games developed with this purpose would be strong, but it was seen that,
in comparison to entertainment-oriented games, educational games did not attract students
or increase attention and motivation much (Kula & Erdem, 2005). Theory and practice,
unfortunately, did not comply with each other (Fis Erumit, 2016).
It seems possible to transform the games students play with great motivation and desire into a
more efficient form by collaboration between the educators and the game sector (Korkusuz &
Karamete, 2013). Being able to determine which types of games students prefer and the
games and locations that get pleasure out of playing, is only one dimension of studies that will
pave the way to add educational content into games without disrupting their playability and
fun (Bozkurt & Genc Kumtepe, 2014; Catak, 2011; Dickey, 2007; Hacisalihoglu Karadeniz, 2017;
Tolay, 2013; Tugrul et al., 2014). This study aimed to determine the games middle school play
willingly and fondly play and present the variables that affect game preferences. In this
context, answers were sought for the following questions:
Methods
There are different methods and techniques that aim to identify game preferences of students.
Vance, Miller and Hand (1995) argued that cognitive structure may be revealed by using
different methods and strategies in order to determine current situations of minds and
conceptual changes. These alternative assessment and evaluation techniques are used to
determine not only students’ possessed knowledge, but also the way students relate concepts,
their cognitive structures, to what extent they understand the similarities between their
possessed knowledge and daily events in their surroundings (Bahar, Nartgun, Durmus & Bicak,
2006) and alternative concepts that they build (Kurt et al., 2013). The-Draw-and-Write
technique, which is one of the most important measurement tools, was utilized in this study in
order to identify the participants’ game preferences. Especially drawing enables children of all
ages to reflect on their own cognitive situation and how they learn (Aydogdu & Kesercioglu,
2005). Several studies have used the drawing method in order to reveal the possessed
knowledge of students about specific subjects (Ehrlén, 2009; Harman, 2012; Sahin, Ipek &
Ayas, 2008).
The quantitative research method of survey was used in this study to determine middle school
students’ game preferences. The survey method is defined as a scientific research tool that
enable researchers to make generalizations on the population from the sample being studied
(Johnson & Christensen, 2000). In other words, survey research aims to describe or explain
characteristics of a very large group or groups such as societies, things, institutions and events
(Cohen, Manion & Morrison, 2007; McMillian & Schumacher, 2001). Since this study aims to define the game preferences of middle school students, it was thought it would be useful to
form a very large sample out of 464 students and employ the survey method.
Participants
In the scope of the study, 478 middle school students were reached from 12 randomly
selected schools in the central district and other districts of the province of Trabzon in Turkey.
10 student who did not respond to the survey in the study completely and 4 students who
made more than 4 game drawings were left out of the sample. The remaining 464 middle
school students (212 girls, 252 boys) of grades 5, 6, 7 and 8, from different socioeconomic
groups constituted the sample.
A total of 464 participants were included in the study. Table 1 demonstrates participant
students’ gender and grade level information. As shown in Table 1, 464 students participated
in the survey. This number included 212 girls and 252 boys.
Table 1. Distribution of Participants by Grade in School and Gender
Grade 5 6 7 8
Girl 48 47 59 58
Boy 65 49 67 71
Total 113 96 126 129
Data Collection Tool
The data of this study, which was conducted in Spring 2015, were gathered by using a two-part
survey. This two-part survey was developed according to three different field expert-opinions.
The first part consisted of demographic questions and the second part asked students to draw
a game that they had the most fun playing (the most played games). A pilot study was
conducted with 50 students, who were not included in the sample of this study, in order to
test the survey questions. The pilot study showed that 30-40 minutes of time was enough
to complete the survey. Additionally, two questions that were generally misunderstood by the
students were redesigned and the final survey was obtained.
Data Analysis
The SPSS 20.0 software was used in the quantitative data analysis, while the qualitative data
analysis involved the method of content analysis, which comprises stages of coding, finding
themes, organizing the data based on codes and themes (Yildirim & Simsek, 2011). The
drawings in the survey forms filled out by the students were coded by transcription. For
reliability of coding, two different experts of the field performed coding on randomly selected
examples and compared the results. The relationships among the codes were examined,
similarities and differences were detected, and the main lines of the finding of the study were
derived. In this context, while developing the categories, three levels (indoor games, outdoor
games and digital games) were determined in accordance with the body of literature and the
content of the drawing questions (Yengin, 2012). Additionally, the three levels mentioned
above were sub-categorized as games with simple rules, games with rules and symbolic games (Barnes, 2004; Pilten & Pilten, 2013). The drawings of the students were categorized accordingly and are shown in the tables. The demographic data are presented as percentages and frequencies. In a qualitative study, it is important in terms of achieving validity in the study to report the data in detail, included direct quotes from individuals and explain the results (Yildirim & Simsek 2011). An example of a drawing for each category was presented in tables based on mutual decision. The factors that affect students’ game preferences are determined by CHAID analysis, a decision tree method. The difference of CHAID analysis from other comparative analyses (such as t-test, ANOVA) is that it clusters the independent variable based on a certain dependent variable and starts a new clustering operation on the derived sub-clusters based on other independent variables. This way, while it analyzes the dependent variable in terms of the independent variables, it produces a result by evaluating various independent variable (Kilmen & Kosterelioglu, 2017). In general terms, as depicted by Kass (1980), decision trees are nonlinear methods that incrementally divide independent variables into smaller groups (Ture et al., 2005). CHAID analysis is an effective decision tree that repeatedly splits subsets of the space into two or more nodes, beginning with the entire data set (Michael & Gordon, 2004). To identify the best split at any node, any allowable pair of categories of the predictor variables is merged. The splitting continues until there is no statistically significant difference within the pair with respect to the target variable (Kass, 1980). CHAID is useful for analyzing a large number of predictor variables, and unlike similar statistical analyses, the CHAID algorithm does not require the data to be normally distributed or need assumptions such as homogeneity of the variants (Horner, Fireman & Wang, 2010). The only required assumption for CHAID analysis is to identify scale types of the predicted and predictor variables (SPSS, 2012). CHAID was preferred in this study because it also enables simultaneous analysis of nominal, interval and ratio scale data and demonstrates the relationships between the predicted and predicting variables in detail including all possible hierarchy (Yildiz, 2006; Horner, Fireman & Wang, 2010).