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Pedagogical Research
2023, 8(2), em0154
e-ISSN: 2468-4929
https://www.pedagogicalresearch.com Research Article OPEN ACCESS
Examining coding skills of five-year-old children
Sermin Metin 1 , Mehmet Basaran 2* , Damla Kalyenci 3
1 Department of Preschool Teacher Educa tion, Faculty of Education, Hasan Kalyoncu University, Gaziantep, TURKEY
2 Department of Educational Sciences, Faculty of Education, Gaziantep University, Gaziantep, TURKEY
3 Ministry of National Education, Adiyaman, TURKEY
*Corresponding Author: mehmetbasaran@gantep.edu.tr
Citation: Metin, S., Basaran, M., & Kalyenci, D. (2023). Examining coding skills of five-year-old children. Pedagogical Research, 8(2), em0154.
https://doi.org/10.29333/pr/12802
ARTICLE INFO
ABSTRACT
Received: 24 Sep. 2022
Accepted: 02 Jan. 2023
The purpose of this research is to examine the coding skills of five-year-old children in terms of some variables.
The research sample comprises 160 children aged five years studying in kindergarten affiliated with the Ministry
of National Education in Gaziantep city center in the 2021-2022 academic year. As a data collection tool in the
research, the “personal information form,” which includes personal information about children and their parents,
and “CodingTest 2”, the short form of “CodingTest” and “CodingTest,” developed by Kalyenci et al. (2022), were
used to evaluate the coding skill levels of five-year-old children. Pearson correlation analysis, t-test, and ANOVA
were used to analyze data. As a result of the findings obtained from the research, it was concluded that coding
skills were not related to gender but were related to whether the children had coding education, the education
level of parents, and their families’ income level.
Keywords: early childhood, coding, coding skills
INTRODUCTION
Coding, which is a new literacy of the 21st century, is not only a necessity when we look at the technological developments and
the needs of our age, and the responsibilities that will arise in the future, but it is expected that today’s children will have skills
such as critical for cognitive development, algorithmic thinking, problem-solving, and solving similar situations (Dallasega et al.,
2018; Sayin & Seferoglu, 2016).
Developed or developing countries have realized the importance of many skills, such as problem-solving, analytical thinking,
computational thinking, critical thinking, and design-oriented thinking to go further in the 21st century and have made changes in
their education systems in this direction (Campbell & Walsh, 2017; Harel, 1988; Johnston et al., 2018; Kucukkara & Aksut, 2021; Lee
& Junoh, 2019; Manches & Plowman, 2017; Mohaghegh & McCauley, 2016; Rogoff, 1995).
Technological developments reveal the importance of individuals gaining technological skills early on. Knowing the
importance of experience in the early years has enabled early childhood classes to be enriched with technological tools. However,
using these tools for educational purposes is not enough. Besides, children must gain digital literacy skills to understand these
language tools. Coding is a tool for acquiring these skills (Marsh et al., 2016; Sulistyaningtyas et al., 2021).
Early childhood is a critical time to develop core competencies and trends for a future driven by innovation and technology
(Marsh et al., 2016). Children should be acquainted with coding, an essential digital literacy and digital language of the age, in their
early years (Futschek & Moschitz, 2010). Coding skills gained in the early years will contribute significantly to children’s social-
emotional, mental, and language development beyond raising future engineers or software developers. (Bers, 2008; Clements,
1999; Lee et al., 2013; Sulistyaningtyas et al., 2021¸ Sullivan & Bers, 2013). Coding also contributes to developing digital literacy
skills by enabling children to interact with technology and providing them with basic programming concepts. In this process,
children also acquire a symbolic language (Bers, 2008, 2010, 2014, 2018, 2019; Bers et al., 2002, 2022; Cejka et al., 2006; Mason,
2017; Mclennan, 2017; Sullivan & Bers, 2013; Wyeth, 2008).
Coding skills, an important skill to be acquired in the early years and added to the curricula of many countries, have also
become an important issue in Turkey, and many studies and educational practices have been made on this subject. For these
reasons, determining the coding skills of preschool children and revealing the factors affecting the acquisition of these skills are
seen as critical issues in determining the studies and educational approaches in the field. This study was carried out to reveal the
factors affecting the coding skills of preschool children.
2 / 13 Metin et al. / Pedagogical Research, 8(2), em0154
CODING
Considering the definitions made about coding in the literature, the process algorithm that emerges in compiling and running
these commands together in which computers make the operations they can understand into sequences of commands, is called
coding (Van Roy & Haridi, 2004). In other words, “it is defined as an application development process using command sets to solve
problems, provide human-computer interaction, and perform a specific task by the computer” (Bers et al., 2019; Demirer & Sak,
2016; Fesakis & Serafeim, 2009; Kalelioglu et al. al., 2016; McLennan, 2018; Vorderman, 2019; Wing, 2006).
Coding, which allows analysis, problem-solving, and concept development, creates algorithms by separating problems and
expressing these algorithms with a programming language. With coding, an expression tool like language, children also express
their thoughts and opinions (Bers, 2008). As with language (Vygotsky, 1978), which is a means of thinking and expressing the
obtained knowledge, children also acquire a new way of thinking by learning a programming language (García-Peñalvo et al.,
2016).
Coding, a new language, must be taught to children in ways appropriate to their development. Children construct knowledge
based on experiences, actively participating, and interacting with peers and adults (Piaget, 1973; Vygotsky, 1980). Bers (2018)
defines this experience process as “a developmental progression that begins with discovering what coding activities,
programming, and technology are, and results in the ability to deliberately create a program to express themselves in a meaningful
way.” Concrete experiences are essential in providing learning for children mentally in the preoperational period (Wang et al.,
2011). Studies have shown that these activities are successful based on children’s active participation (Menon et al., 2019; Sullivan
& Bers, 2016; Wang et al., 2011). Many operations and concepts remain abstract for students in the coding teaching process,
especially since young children are in the preoperational and concrete operational periods.
Basic skills in coding in early childhood; includes directional signs, sequencing, debugging, function creation, looping,
program development, and algorithmic thinking (Futschek, 2006; Kalyenci et al., 2022; Lee & Junoh, 2019; Mittermeir, 2013; Relkin
et al., 2021; Welch et al., 2019; Zamin et al., 2018). In addition, K-12 standards for coding skills were set forth by CSTA (2003, 2011,
2019) and ISTE (2016). Coding skills for early childhood are given in Table 1.
Supporting children’s coding skills has been a rapidly developing field in the international arena. However, the issue of
supporting these skills in early childhood has become clear in recent years. Due to the developmental characteristics of early
childhood, there has been a widespread belief that coding practices, teaching methods, and techniques should be based on
concrete experiences (Bers, 2019; Bers et al., 2019; Futschek & Moschitz, 2010; Kazakoff, 2014; Kazakoff & Bers, 2012; Metin, 2020).
Educational Approaches
Coding education should be based on concrete experiences due to the developmental characteristics of early childhood.
According to Piaget (1973), children act with their senses in early childhood to understand and give concrete meaning to the world.
However, today, children are exposed to tangible tools and objects and digital and virtual tools (Strawhacker & Bers, 2019). This
exposure is realized through technological tools. Papert’s constructivism forms the basis of coding and robotics. Influenced by the
ideas of Piaget (1973), with whom he worked, Papert took constructivism one step further and developed a constructionism
learning approach. While Piaget’s (1973) constructivism is based on the person’s structuring of information through the
information in his inner world, Papert’s constructivism also includes the use of computers and technology in this structuring
process and the child’s construction through these tools (Bers et al., 2014). Many ways are suggested, such as coding in a
computerless environment, coding in a computerized environment, robotics, interdisciplinary approaches, and activities. Since
programming is abstract, coding activities should be suitable for children’s developmental levels and integrated into the
curriculum. Here, activity-based non-computer coding (unplugged coding) allows children to perform abstract operations with
concrete applications.
Unplugged coding activities allow learning by concretizing abstract concepts and enabling learning to be designed and
created. Studies have shown that it is more appropriate for children to learn through activities rather than complex tools such as
computers to understand the basic logic of coding (Bell et al., 2012; Bell & Vahrenhold, 2018; Bers, 2018; Metin, 2020; Wang et al.,
2011). One of the unplugged activities is the use of robotic tools to support coding skills. Resnick and Rosenbaum (2013) state that
educational robotic kits provide meaningful learning. In addition, these robotic kits allow children to collaborate with their peers
and see the concrete outputs of their programs more clearly (Bers et al., 2019; Campbell & Walsh, 2017; García-Peñalvo et al., 2016;
Resnick & Siegel, 2015; Sullivan & Bers, 2016).
If the concrete product creation phase of coding takes place in the digital world, children do not only manipulate objects; they
create them; they learn the rules, test them, and write them; they construct, review, share and renew works in virtual
environments. Therefore, coding activities allow students to collaborate with their peers and provide sustainable participation in
Table 1. Coding process
Coding content
Signs
Direction arrows.
Arrangement
Algorithm steps are the instruction steps given while performing.
Debugging
It detects and corrects incorrect statements and operations in the algorithm.
Loops
Repeating a code sequence multiple times.
Modularity
It is the process of breaking down tasks into simpler ones.
Algorithm
The algorithm is the consolidation of smaller tasks into more complex tasks.
Program development
Create a plan for what a program will do.
Metin et al. / Pedagogical Research, 8(2), em0154 3 / 13
problem-solving and reasoning (Fox & Farmer, 2011). Thus, studies show that coding practices contribute significantly to the
cognitive development of children (Hwang et al., 2008; Grover & Pea, 2013; Kazakoff & Bers, 2012; Kazakoff et al., 2013; Linn &
Clancy, 1992; Papadakis et al., 2016; Strawhacker et al., 2015; Strawhacker & Bers, 2019; Turan & Aydogdu, 2020).
21st century skills are the skills that individuals must have, and coding is one of these skills and shows that it should be added
to the competencies that every student should gain in their school life (Voogt et al., 2015). Coding provides the necessary
motivation for children to learn programming in more detail. In this way, it provides an environment for them to turn their ideas
into products and affects their development in many ways (Heikkilä, 2020). Studies carried out; on cognitive and social
development, motor skills (Flannery & Bers, 2013), sequencing skills (Caballero-Gonzalez et al., 2019; Chou, 2020; Kazakoff et al.,
2013; Kazakoff & Bers, 2014), peer collaboration, social relations (Lee et al., 2013), academic and social experiences (Pugnali et al.,
2017), problem-solving skills (Akyol Altun, 2018; Fessakis et al., 2013), creativity (Resnick et al., 2009; Siper Kabadayi, 2019; Sullivan
et al., 2017), decision-making skills (Strawhacker & Bers, 2015), self-regulation (Kazakhoff, 2014), and computational thinking
(Kalogiannakis & Papadakis, 2017; Kazakoff et al., 2013; Papadakis et al. al., 2016), visual-spatial skills and executive functions (Di
Lieto et al., 2017). At the same time, gaining coding skills also gives children 21st century skills such as computational thinking,
technology literacy, problem-solving, and critical thinking (Bers, 2008; Bers et al., 2002; Bers & Horn, 2010; Clements & Gullo, 1984;
Clements & Meredith, 1993; Kazakoff & Bers, 2012; Lee et al., 2013; Portelance et al., 2016; Strawhacker et al., 2015).
Evaluation of Coding Skills
In the evaluation of coding skills in early years, Bers (2019), Bers et al. (2019), Chaldi and Mantzanidou (2021), González and
Muñoz-Repiso (2018), and Sáez-López et al. (2016) survey; Metin (2020), Patan (2016), Sullivan and Bers (2019), and Wang et al.
(2011) observation form; checklists of Bers et al. (2019), Kalelioglu (2015), Kalelioglu and Gulbahar (2014), and Pugnali et al. (2017)
used a problem-solving inventory.
In recent years, tests with validity and reliability have been developed rapidly. CodingTest by Kalyenci et al. (2022) to measure
the coding skills of children (five-seven years); Strawhacker et al. (2022) evaluation of applications made with KIBO robot coding
stages evaluation-(CodingStagesAssessment (CSA)-KIBO); CSA-ScratchJr (coding stages assessment-ScratchJr) was developed by
Unahalekhaka and Bers (2022) to evaluate ScratchJr skills of children (five-seven years). Govind and Bers (2021) developed the
KIBO project rubric to assess children’s robotic skills. In this study, children’s coding skills Kalyenci et al. (2022). The short form of
“CodingTest” developed will be tested with “CodingTest 2” and examined in terms of different variables.
Factors Affecting Coding Skills
Approaches and studies on coding education in early childhood, especially conformity to development recommended by the
CSTA (2019), ISTE (2016), and NAECY (2012) standards are considered in educational practices. It is an essential factor to be kept
in front of students and affects children’s coding skills (Campbell & Walsh, 2017; Levy & Mioduser, 2010; Metin, 2020; Resnick &
Siegel, 2015; Sullivan & Bers, 2016; Wang et al., 2011). Studies reveal that children can learn to code from age three (Bers et al.,
2019; Ciftci & Bildiren, 2020; Papadakis et al., 2016; Strawhacker et al., 2022). Therefore, Bers (2019) emphasizes that early
experience is essential for children to learn this artificial language as they learn the language.
Studies and applications for coding, an essential skill, and literacy in the last ten years have increased. Some factors affect
children’s coding skills. One of the factors affecting children’s coding skills is seen as gender. The gap between girls and boys in
technology use, access to technology, and gender inequality has been known for years. It is emphasized that gender is essential,
especially in success and interest in STEM disciplines and that women tend to this field less (Butler, 2000; Ceci et al., 2009;
Heemskerk et al., 2009; Hill et al., 2010; Landivar, 2013; National Science Foundation, 2013; National Center for Women &
Information Technology, 2017; Pila et al., 2019; Wang et al., 2013). However, in recent years, efforts to eliminate this inequality
have accelerated, and inequality has decreased, especially in STEM fields (Hill et al., 2010; Madkins et al., 2020; National Center for
Women & Informational Technology, 2011). Sullivan and Bers (2016) report that gender stereotypes have become more firmly
rooted (Metz, 2007; Steele, 1997; Sullivan & Bers, 2016).
Studies on the relationship between coding and gender are limited. It has been tried to start from the studies related to STEM.
No or insignificant differences were found in learning STEM content (Martinez et al., 2015; Petersen & Hyde, 2014; Sullivan & Bers,
2013, 2019; Voyer & Voyer, 2014). Sullivan and Bers (2016) kindergarten, Nourbakhsh et al. (2004) high school, and Nourbakhsh et
al. (2004) in their study on coding with secondary school children showed that girls and boys are equally enthusiastic and
participatory in coding. However, there are also studies showing no relationship between coding and gender in studies conducted
with kindergarten children (Papadakis et al., 2016; Pila et al., 2019; Portelance & Bers, 2015). Erete et al. (2016) found that six-year-
old girls believe they are better at robotics and programming than boys, but exposure to coding and robotics can alleviate these
stereotypes and help develop feelings of self-efficacy.
In recent years, digital applications designed to teach young children coding skills through fun and play have become
widespread (García-Peñalvo et al., 2016; Murcia et al., 2020; Seow et al., 2017; Sullivan & Bers, 2013). However, most of these
applications require either a tablet or a computer. There may be children who do not have access to these technological tools, or
despite the availability of these tools, there may be a lack of skills in terms of parental education status or their necessity and how
to guide their children. The family’s socioeconomic status affects children’s technology use and coding skills.
Research shows that individuals who are disadvantaged in the use of technology have low digital skills, which means that they
are also disadvantaged in the labor market. He states that digital skills increase employability in the job market. European
Commission (2020); states that low socioeconomic status is associated with low digital skills in most European countries. These
findings raise some concerns about the inequality that started in the early years. These concerns are disadvantaged children show
less interest in digital technology and tools at school and home, have low digital competence, and tend to work in less skilled jobs
4 / 13 Metin et al. / Pedagogical Research, 8(2), em0154
(Karpinski et al., 2021). In addition, access to technology may vary according to race, socioeconomic status, and other factors
(Google Inc. & Gallup Inc., 2016). European Commission (2020); found that low socioeconomic status was associated with low
digital skills in most European countries. In addition, a 2021 international study revealed that those from economically
advantageous backgrounds have higher information technology skills than children from disadvantaged regions (Karpinski et al.,
2021).
Kafai and Burke (2014) stated that it is a responsibility to develop the CT skill to return to the schools the coding that has been
used and interested mainly by computer scientists until today. Today, coding is recognized by educators, academic and scientific
communities as a crucial skill for all children, an international necessity, and even a new form of literacy (Kafai & Burke, 2014;
Stamatios, 2022; Papadakis et al., 2016). For this reason, many researchers and countries focused on instructional approaches to
CT and coding for preschool children, and many applications were made (Campbell & Walsh, 2017; Johnston et al., 2018; Lee &
Junoh, 2019; Manches & Plowman, 2017; Stamatios, 2022).
Resnick and Silverman (2005) state that while everyone believes in the value of learning to program, they know the difficulties
of learning to program. They emphasize that many programmers and children can now write simple programs but must go further.
As efforts and practices towards coding, which is a skill that should be among the skills such as mathematics, science, and literacy
that should be gained in early years, have increased, the programs to be prepared to gain these skills have increased, and
nowadays there are applications to support children’s coding skills, and the quality of these programs has begun to be discussed.
For this reason, many factors must be addressed to prepare developmentally appropriate programs for preschool children.
Therefore, this study aimed to reveal the factors affecting children’s coding skills. It is thought that the findings of this study will
prepare an infrastructure for educational applications to be prepared in the future.
METHOD
Research Design
This research is based on the general screening model. The screening model is a model that is carried out with a sample to be
taken from the universe in order to reach a general judgment about the universe with different elements (Buyukozturk et al., 2012).
Working Group
In order to determine the study group consisting of five 160 children attending kindergarten in Gaziantep Province, a
convenient sampling method was used due to its convenience in terms of time and workforce (Buyukozturk et al., 2012) (Table 2).
Data Collection Tools
The researcher prepared the “personal information form,” “CodingTest” was developed by Kalyenci et al. (2022), and the short
form “CodingTest 2” were used as a data collection tool to collect information about children and their families.
Coding Test
The “coding test,” which measures the coding skills (computerless and robotic coding) of children aged five-seven, was
developed by Kalyenci et al. (2022). The test consists of two forms to measure computer-free coding skills (form A) and robotic
coding skills (form B). A validity and reliability analysis of the test was performed. The reliability results of the test were found to
be KR-20=0.973>0.70 for form A and KR-20=0.978>0.70 for form B.
Form A contains a 9×9 square coding sheet, signs, and story cards, while form B contains a 6×6 square coding sheet, story
cards, and a robotic tool. Each form consists of two examples and six applications. The coding skills in the applications are ordered
from simple to challenging. Each app measures different coding skills and has a plot-related story. Form A consists of 13 items,
and form B consists of 14 items, and correct and incorrect answers are scored (0/1).
All materials are introduced to the children during the application process, and the application starts when the child is ready.
The practitioner tells a story for each question, and the child is expected to show the coding with his finger first and then code
using the cards on the coding carpet. The practitioner records the child’s answers. Before moving on to the next question, the child
Table 2. Distribution of the sample by demographic characteristics
n
%
n
%
Gender
Female
85
53.1
Family income level
1,000-5,000 TL
24
15.0
Male
75
46.9
5,001-9,001 TL
40
25.0
Father working status
Working
160
100.0
9,002-13,002 TL
23
14.4
Not working
0
0.0
13,003 TL and above
73
45.6
Status of receiving coding training
Yes
103
64.4
Mother working status
Working
98
61.3
No
57
35.6
Not working
62
38.8
Mother education status
Primary school
0
0.0
Father educational status
Primary school
0
0.0
Middle School
7
4.4
Middle School
3
1.9
High school
40
25.0
High school
33
20.6
Undergraduate
34
21.3
Undergraduate
31
19.4
Master’s degree
66
41.3
Master’s degree
73
45.6
Doctorate
13
8.1
Doctorate
20
12.5
Metin et al. / Pedagogical Research, 8(2), em0154 5 / 13
is expected to put the cards back in their place. The application of the test takes an average of 30-45 minutes. Practitioners must
receive special training and materials to use the CodingTest. The reliability analysis of CodingTest 2 was made to reach more
people, save application time, and provide ease of use.
Coding Test 2
The reliability of “CodingTest,” the short form of “CodingTest,” was checked to measure children’s coding skills without a
computer easily and quickly. For the short form of Coding Test 2, the stories were shortened, and three visual response options
(Figure 1) to evaluate each skill were added. Answer options consist of 6×6 squares. Before the application, a page introducing
the materials was added to the children (Figure 1 and Figure 2). As in the original test, the child is read the story of each question
and is expected to say or show the correct option from among the three options.
In order to check whether the questions in the test are clear and understandable, whether they measure the skill to be
measured, and whether they are scientifically appropriate (Baykul & Guzeller, 2015), the opinions of nine experts were taken (two
instructional technologies education, two child development, and education, two preschool education, measurement and
evaluation, graphic and visual arts, and a Turkish language and education lecturer). The pre-application of the modified test was
tested on 30 children with different socioeconomic conditions. The individual test takes 10-15 minutes on average.
Figure 1. “CodingTest 2” stylish (Kalyenci, 2020)
Figure 2. “CodingTest 2” signs and images (Kalyenci, 2020)
6 / 13 Metin et al. / Pedagogical Research, 8(2), em0154
“CodingTest 2” can be applied using a tablet, computer, and A4 paper. The application to be made with an A4 paper has been
prepared, so there will be question-and-answer options on one page. The coding carpet in the test consists of 6×6 squares. Six
applications in “CodingTest” were transformed into two sample questions and 11 main questions in “CodingTest 2”. The stories
in “CodingTest 2” are shorter than the stories in “CodingTest,” and there are three answer choices for each question (Figure 1).
Only one of these answer choices is the correct answer. The child who writes or says the correct answer to the question gets one
point, and the child who shows the wrong answer gets zero points. Application 6 in “CodingTest” corresponds to Question 11 in
“CodingTest 2”. Question 11 measures children’s programming skills. There are no answer options in question 11. Here, the child
is expected to draw by making his program. If the child makes and draws the program, he gets one point; if he cannot program or
draw, he gets zero points. A total score of 11 points is taken from the test.
During the application phase of the test, the test or tablet is placed so the child can easily see it. The practitioner reads the
questions and asks the child to show or mark the answer option. The application of the test starts with sample question 1. If the
child answers sample question 1 correctly, the test is started, and if the child gives a wrong answer, sample question 2 is asked.
The primary practice starts with the child who answers sample question 2, and the test is terminated when answered incorrectly.
Guiding answers are not given to the child’s questions about what to do during the test application, and the practitioner does not
make any other explanations by rereading the question.
Data were collected using “CodingTest” and “CodingTest 2”. The application was made in a quiet room and individually—the
practitioner communicated with the child by chatting before the application. First, the “Coding Test” and then “Coding Test 2”
were administered to 80 children. “CodingTest 2” was applied first to the other 80 children, and then “CodingTest” was applied.
In the application phase of the test, first, the heroes of the story in the test and the coding signs are introduced. At the practice
table, the practitioner and the child sit face to face. “CodingTest 2” was administered to 80 children using the A4 printout and the
other 80 children using tablets. Before the application, the practitioner opened the page with the signs in the test and the images
related to the story, chatted with the child, and introduced the materials. After the practitioner’s questions, which were put into
practice, were read comprehensibly and clearly, he was asked to choose one option. He recorded the answers given by the child.
The application took about five-10 minutes.
As a result of the correlation analysis (Table 3), it was seen that the relationship between “CodingTest” and “CodingTest2”
was significant and positive (r=.967, p<.001).
Analysis of Data
Data analysis was conducted using the SPSS 23 statistical package program and the Jamovi package program to determine
the children’s coding skills. The data set was examined with QQ graphs and determined to show a normal distribution. Descriptive
statistics (arithmetic mean, standard deviation), t-test for independent samples, and one-way analysis of variance (ANOVA) were
used in data analysis.
RESULTS
The findings of the study, which examined the coding skills of five-year-old children according to some variables, are presented
below.
Table 4 shows no statistically significant difference between the mean scores of children’s coding skills (t(4,836)=0.889) according
to gender. This finding shows that gender does not affect children’s coding skills.
It was found that the children’s coding education made a significant difference in their coding skill mean scores
(t[1.803]=24.711). The coding skill averages of the children who had previously received coding education (X=11.155) were
significantly higher than those who did not (X=2.404) (Table 5).
It was determined that the difference between the children’s coding skill scores and the mother’s education level was
statistically significant (Table 6). The coding skill means the score of the children whose mother’s education level is a university
(X=7.91) is higher than the mean (X=3.25) of the children whose mother’s education level is a secondary school (X=2.14) and high
Table 3. Result of Pearson correlation analysis
n
Pearson’s
p-value
CodingTest-CodingTest2
160
0.967*
<.001
Note. *p<.001
Table 4. t-test results of coding skill scores by gender
Group
n
Mean
SD
SE
t
p-value
Codiing
Male
75
0.140
4.836
0.558
0.140
0.889
Female
85
0.074
4.638
0.503
Table 5. t-test results of coding skill scores according to previous coding training status
Group
n
Mean
SD
SE
t
p-value
Codiing
Trainee
103
11.155
1.803
0.178
24.711
<0.001
Uneducated
57
2.404
2.658
0.352
Metin et al. / Pedagogical Research, 8(2), em0154 7 / 13
school. Similarly, the coding skill mean scores of the children whose mothers had a master’s degree (X=10.93) were higher than
the averages of the children whose mothers had secondary education (X=2.14) and high school (X=3.25). On the other hand, the
mean of coding skills (X=12.08) of the children whose mother’s education level is doctorate is higher than the children in secondary
school (X=2.14) and high school (X=3.25).
It is seen that there is a significant difference between the mean scores of children’s coding skills and the education levels of
the fathers (Table 7). The mean coding skills of the children of fathers with university education (X=5.03) are higher than those of
fathers with secondary school (X=1.67) and high school (X=3.15). Similarly, the mean coding skills of the children of fathers with a
master’s degree (X=10.75) are higher than those of fathers with secondary school (X=1.67) and high school (X=3.15). In the same
way, the mean coding skills of the children of fathers with a doctorate (X=11.80) are higher than those of fathers with secondary
school (X=1.67) and high school (X=3.15).
The children’s coding skill scores and the family’s income status were statistically significant (Table 8). The coding skill mean
score of the children whose family income is 13,003 TL and above (X=11.59) is higher than the mean score of the children whose
family income is 13,003-13,002 TL (X=10.17) and 5001-9001 TL (X=3.18). This shows that the children’s coding skills increase as the
family income level increases.
DISCUSSION, CONCLUSION, AND IMPLICATIONS
In this study, the coding skills of five-year-old children were revealed according to certain variables. The research examined
the reliability of “CodingTest 2”, the short form of “CodingTest.” As a result of the reliability analysis of the “Coding Test,” whose
validity and reliability studies were conducted, it was seen that the short form of the test, “Coding Test 2”, is a valid tool for
measuring coding skills. “CodingTest 2” has been developed because the application of “CodingTest” requires unique materials
and a long time. This test provides convenience to practitioners in terms of being able to be applied with both paper and digital
technology tools and saving time.
In the study, the relationship between coding skills and gender was examined, and it was seen that gender did not significantly
affect coding skills. Sullivan and Bers (2016) state that there are stereotypes about boys and girls in STEM fields and technology.
However, their study has revealed that girls and boys showed the same interest in the robotic kit they used, and if appropriate
materials were provided, the gender difference would disappear. Sullivan and Bers (2013, 2016, 2019) also revealed that gender
does not make a significant difference in their other studies on programming. On the other hand, Papadakis et al. (2016) and Pila
et al. (2019) also conducted studies supporting these views.
Contrary to studies stating that gender does not make a difference in coding skills, Gomez and Benotti (2015) stated in their
study with children aged three-11 that girls performed slightly better than boys in essential computer science concepts of children.
Nourbakhsh et al. (2004) found that girls initially had less confidence in technology than boys, but their self-confidence increased
significantly throughout the course. Nourbakhsh et al. (2004), in another study with high school students, found that girls initially
Table 6. ANOVA results of children’s coding skills according to their mothers’ educational status
Variables
SS
SD
MS
F
p-value
Significant difference
Coding skills
Between groups
2,011.137
4
502.784
51.048
.000
University>Middle school, High school
Masters>Middle school, High school
PhD>Middle school, High school
Within groups
1,526.638
155
9.849
Total
3,537.775
159
Within groups
910.334
155
5.873
Total
2,360.975
159
Note. *p<.05
Table 7. ANOVA results of children’s coding skills according to their fathers’ educational status
Variables
SS
SD
MS
F
p-value
Significant difference
Coding skills
Between groups
1,888.593
4
472.148
44.375
.000
University>Middle school, High school
Masters>Middle school, High school
PhD>Middle school, High school
Within groups
1,649.182
155
10.640
Total
3,537.775
159
Within groups
1,023.905
155
6.606
Total
2,360.975
159
Note. *p<.05
Table 8. ANOVA results of children’s coding skills by family income
Variables
SS
SD
MS
F
p-value
Significant difference
Coding skills
Between groups
2,512.066
3
837.355
127.353
.000
13,003 TL and above>9,002-13,002 TL
and 5,001-9,001 TL
Within groups
1,025.709
156
6.575
Total
3,537.775
159
Within groups
626.369
156
4.015
Total
2,360.975
159
Note. *p<.05
8 / 13 Metin et al. / Pedagogical Research, 8(2), em0154
had difficulty programming and had less self-confidence, but at the end of the course, girls’ self-confidence increased more
(Nourbakhsh et al., 2004).
Another finding from the research is that the children’s coding skills are affected by the educational status of their parents.
The higher educational status of parents allows children to have a more advantageous background. Thus, children with good
backgrounds are more exposed to technological tools, allowing them to develop their coding skills better from their early years
(Karpinski et al., 2021). It can be said that the high education level of the parents and their awareness of the importance of digital
competence for children contribute to the development of coding skills.
The income status of families also creates a significant difference in the development of children’s coding skills. Robotic kits
and applications are of great importance in developing coding skills. These technological tools and applications require families
to have a certain socioeconomic level, equipment, and knowledge (García Peñalvo et al., 2016; Sullivan & Bers, 2013, 2019; Sullivan
et al., 2017). When suitable environmental conditions are provided for children to access technological tools, these tools support
their coding skills (Ananadou & Claro, 2009; Govind & Bers, 2020; Lee & Junoh, 2019; Resnick & Siegel, 2015; Sullivan & Bers, 2016).
Research reveals that disadvantaged children have low digital skills and that socioeconomic conditions affect their access to and
use of technology (European Commission, 2020; Google Inc. & Gallup Inc., 2016; Karpinski et al., 2021).
Coding skills, one of the basic concepts of 21st century skills, new literacy, and computer science, are among the essential skills
children should acquire in their early years. Factors that affect coding skills, which are tried to be added to educational
environments and curricula, are essential in structuring these programs. In this study, while the gender of the children is not a
factor in their coding skills, the family’s education and income status affect the children’s coding skills. The level of awareness of
the family towards education and the educational needs of their children in the developing technological world enables them to
support their children in this direction. In addition, it is seen that children’s access to technology and the family’s income status
are essential factors in integrating technology into education. For this reason, it is thought that educational practices to support
children’s coding skills in the early years should consider the educational status of the family, raise awareness of families on this
issue, and work to support families who have difficulties in accessing technology will increase the impact of educational practices
in this area.
In this study, the coding skills of five-year-old children were examined in terms of gender, educational status of parents, and
income level of the family. Based on these findings, it can be suggested that future studies should be conducted to evaluate
children’s coding skills over different variables and different age groups.
Author contributions: SM: conceptual framework, data collection, & analysis; & MB & DK: data’s conceptual framework, interpretation, &
discussion. All authors have agreed with the results and conclusions.
Funding: No funding source is reported for this study.
Ethical statement: The authors stated that written permission was obtained on 17.03.2020 with the approval code -804.01-E.2003170030
from Hasan Kalyoncu University Graduate Education Institute in order to carry out the study. Ethical considerations were stri ctly followed
during this study. The authors also noted that the participants in this study were not exposed to any risk or potential harm. Official permission
was obtained from the school principals of the schools where the research would be conducted. In addition, informed consent was obtained
from all study participants.
Declaration of interest: No conflict of interest is declared by authors.
Data sharing statement: Data supporting the findings and conclusions are available upon request from the corresponding author.
REFERENCES
Akyol Altun, C. (2018). Okul öncesi öğretim programına algoritma ve kodlama eğitimi entegrasyonunun öğrencilerin problem çözme
becerisine etkisi [The effect of the integration of algorithm and coding education into the preschool curriculum on the problem
solving skills of the students] [Unpublished master’s dissertation]. Ankara University.
Ananiadou, K., & Claro, M. (2009). 21St century skills and competences for new millennium learners in OECD countries. OECD
education working papers, no. 41. OECD Publishing (NJ1). http://dx.doi.org/10.1787/218525261154
Bell, T., & Vahrenhold, J. (2018). CS unplugged—how is it used, and does it work? In H.-J. Böckenhauer, D. Komm, & W. Unger (Eds.),
Adventures between lower bounds and higher altitudes (pp. 497-521). Springer, Cham. https://doi.org/10.1007/978-3-319-
98355-4_29
Bell, T., Fellows, M., Rosamond, F., Bell, J., & Marghitu, D. (2012). Unplugging education: Removing barriers to engaging with new
disciplines. In Proceedings of the Society for Design and Process Science (pp. 1-8).
Bers, M. U. (2008). Blocks to robots: Learning with technology in the early childhood classroom. Teachers College Press.
Bers, M. U. (2010). The TangibleK robotics program: Applied computational thinking for young children. Early Childhood Research
& Practice, 12(2), 2.
Bers, M. U. (2014). Tangible kindergarten: Learning how to program robots in early childhood. In C. I. Sneider (Ed.), The go-to guide
for engineering curricula PreK-5: Choosing and using the best instructional materials for your students (pp. 133-145). Corwin.
Bers, M. U. (2018). Coding and computational thinking in early childhood: The impact of ScratchJr in Europe. European Journal of
STEM Education, 3(3), 8. https://doi.org/10.20897/ejsteme/3868
Bers, M. U. (2019). Coding as another language: A pedagogical approach for teaching computer science in early childhood. Journal
of Computers in Education, 6(4), 499-528. https://doi.org/10.1007/s40692-019-00147-3
Metin et al. / Pedagogical Research, 8(2), em0154 9 / 13
Bers, M. U., & Horn, M. S. (2010). Tangible programming in early childhood. High-Tech Tots: Childhood in A Digital World, 49, 49-70.
Bers, M. U., Flannery, L., Kazakoff, E. R., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early
childhood robotics curriculum. Computers & Education, 72, 145-157. https://doi.org/10.1016/j.compedu.2013.10.020
Bers, M. U., González-González, C., & Armas-Torres, M. B. (2019). Coding as a playground: Promoting positive learning experiences
in childhood classrooms. Computers & Education, 138, 130-145. https://doi.org/10.1016/j.compedu.2019.04.013
Bers, M. U., Govind, M., & Relkin, E. (2022). Coding as another language: Computational thinking, robotics and literacy in first and
second grade. In A. Ottenbreit-Leftwich & A. Yadav (Eds.), Computational thinking in PreK-5: Empirical evidence for integration
and future directions (pp. 30-38). ACM. https://doi.org/10.1145/3507951.3519285
Bers, M. U., Ponte, I., Juelich, C., Viera, A., & Schenker, J. (2002). Teachers as designers: Integrating robotics in early childhood
education. Information Technology in Childhood Education Annual, 2002(1), 123-145.
Butler, D. (2000). Gender, girls, and computer technology: What’s the status now? The Clearing House, 73(4), 225-229.
https://doi.org/10.1080/00098650009600957
Buyukozturk, S., Kilic Cakmak, E., Akgun, O., Karadeniz, S., & Demirel, O. (2012). Bilimsel araştırma yöntemleri [Scientific research
methods]. Pegem Academic Publishing.
Caballero-Gonzalez, Y. A., Muñoz-Repiso, A. G. V., & García-Holgado, A. (2019). Learning computational thinking and social skills
development in young children through problem solving with educational robotics. In Proceedings of the 7th International
Conference on Technological Ecosystems for Enhancing Multiculturality (pp. 19-23). https://doi.org/10.1145/3362789.3362874
Campbell, C., & Walsh, C. (2017). Introducing the’new’digital literacy of coding in the early years. Practical Literacy, 22(3), 10-12.
Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological
considerations. Psychological Bulletin, 135(2), 218. https://doi.org/10.1037/a0014412
Cejka, E., Rogers, C., & Portsmore, M. (2006). Kindergarten robotics: Using robotics to motivate math, science, and engineering
literacy in elementary school. International Journal of Engineering Education, 22(4), 711.
Chaldi, D., & Mantzanidou, G. (2021). Educational robotics and STEAM in early childhood education. Advances in Mobile Learning
Educational Research, 1(2), 72-81. https://doi.org/10.25082/AMLER.2021.02.003
Chou, P. N. (2020). Using ScratchJr to foster young children’s computational thinking competence: A case study in a third-grade
computer class. Journal of Educational Computing Research, 58(3), 570-595. https://doi.org/10.1177/0735633119872908
Ciftci, S., & Bildiren, A. (2020). The effect of coding courses on the cognitive abilities and problem-solving skills of preschool
children. Computer Science Education, 30(1), 3-21. https://doi.org/10.1080/08993408.2019.1696169
Clements, D. H. (1999). The future of educational computing research: The case of computer programming. Information Technology
in Childhood Education Annual, 1999(1), 147-179.
Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children’s cognition. Journal of Educational
Psychology, 76(6), 1051. https://doi.org/10.1037/0022-0663.76.6.1051
Clements, D. H., & Meredith, J. S. (1993). Research on logo: Effects and efficacy. Journal of Computing in Childhood Education, 4(4),
263-290.
Dallasega, P., Rauch, E., & Linder, C. (2018). Industry 4.0 as an enabler of proximity for construction supply chains: A systematic
literature review. Computers in Industry, 99, 205-225. https://doi.org/10.1016/j.compind.2018.03.039
Demirer, V., & Sak, N. (2016). Dünyada ve Türkiye’de programlama eğitimi ve yeni yaklaşımlar [Programming education and new
approaches in the world and in Turkey]. Eğitimde Kuram ve Uygulama [Theory and Practice in Education], 12(3), 521-546.
Di Lieto, M. C., Inguaggiato, E., Castro, E., Cecchi, F., Cioni, G., Dell’Omo, M., Laschi, C., Pecini, C., Santerini, G., Sgandurra, G., &
Dario, P. (2017). Educational robotics intervention on executive functions in preschool children: A pilot study. Computers in
Human Behavior, 71, 16-23. https://doi.org/10.1016/j.chb.2017.01.018
Erete, S., Pinkard, N., Martin, C. K., & Sandherr, J. (2016). Exploring the use of interactive narratives to engage inner-city girls in
computational activities. In Proceedings of the 2016 Research on Equity and Sustained Participation in Engineering, Computing,
and Technology (pp. 1-4). IEEE. https://doi.org/10.1109/RESPECT.2016.7836168
Fesakis, G., & Serafeim, K. (2009). Influence of the familiarization with” scratch” on future teachers’ opinions and attitudes about
programming and ICT in education. ACM SIGCSE Bulletin, 41(3), 258-262. https://doi.org/10.1145/1595496.1562957
Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5-6 years old kindergarten children in a computer programming
environment: A case study. Computers & Education, 63, 87-97. https://doi.org/10.1016/j.compedu.2012.11.016
Flannery, L. P., & Bers, M. U. (2013). Let’s dance the “robot hokey-pokey!” children’s programming approaches and achievement
throughout early cognitive development. Journal of Research on Technology in Education, 46(1), 81-101.
https://doi.org/10.1080/15391523.2013.10782614
Fox, R. W., & Farmer, M. E. (2011). The effect of computer programming education on the reasoning skills of high school students.
In Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (p. 1).
Futschek, G. (2006). Algorithmic thinking: the key for understanding computer science. In Proceedings of the International
Conference on Informatics in Secondary Schools-Evolution and Perspectives (pp. 159-168). Springer.
https://doi.org/10.1007/11915355_15
10 / 13 Metin et al. / Pedagogical Research, 8(2), em0154
Futschek, G., & Moschitz, J. (2010). Developing algorithmic thinking by inventing and playing algorithms. In Proceedings of the 2010
Constructionist Approaches to Creative Learning, Thinking and Education: Lessons for the 21st Century (pp. 1-10).
García-Peñalvo, F. J., Rees, A. M., Hughes, J., Jormanainen, I., Toivonen, T., & Vermeersch, J. (2016). A survey of resources for
introducing coding into schools. In Proceedings of the 4th International Conference on Technological Ecosystems for Enhancing
Multiculturality (pp. 19-26). https://doi.org/10.1145/3012430.3012491
González, Y. A. C., & Muñoz-Repiso, A. G. V. (2018). A robotics-based approach to foster programming skills and computational
thinking: Pilot experience in the classroom of early childhood education. In Proceedings of the 6th International Conference on
Technological Ecosystems for Enhancing Multiculturality (pp. 41-45). https://doi.org/10.1145/3284179.3284188
Google Inc. & Gallup Inc. (2016). Trends in the state of computer science in US K-12 schools. https://news.gallup.com/reports/
196379/trends-state-computer-science-schools.aspx
Govind, M. & Bers, M. U. (2020). Family coding days: Engaging children and parents in creative coding and robotics. Connected
Learning Summit, Cambridge, MA. https://sites.bc.edu/devtech/files/2022/06/Family-Coding-Days.pdf
Govind, M., & Bers, M. (2021). Assessing robotics skills in early childhood: Development and testing of a tool for evaluating
children’s projects. Journal of Research in STEM Education, 7(1), 47-68. https://doi.org/10.51355/jstem.2021.102
Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38-43.
https://doi.org/10.3102%2F0013189X12463051
Harel, I. R. (1988). Software design for learning: Children’s construction of meaning for fractions and Logo programming [Doctoral
dissertation, Massachusetts Institute of Technology].
Heemskerk, I., ten Dam, G., Volman, M., & Admiraal, W. (2009). Gender inclusiveness in educational technology and learning
experiences of girls and boys. Journal of Research on Technology in Education, 41(3), 253-276.
https://doi.org/10.1080/15391523.2009.10782531
Heikkilä, M. (2020). What happens when the robot gets eyelashes? Gender perspective on programming in preschool. In A.
MacDonald, L. Danaia, & S. Murphy (Eds.), STEM education across the learning continuum (pp. 29-44). Springer.
https://doi.org/10.1007/978-981-15-2821-7_3
Hill, C., Corbett, C., & St Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. American
Association of University Women.
Hwang, W. Y., Wang, C. Y., Hwang, G. J., Huang, Y. M., & Huang, S. (2008). A web-based programming learning environment to
support cognitive development. Interacting with Computers, 20(6), 524-534. https://doi.org/10.1016/j.intcom.2008.07.002
Johnston, K., Highfield, K., & Hadley, F. (2018). Supporting young children as digital citizens: The importance of shared
understandings of technology to support integration in play‐based learning. British Journal of Educational Technology, 49(5),
896-910. https://doi.org/10.1111/bjet.12664
Kafai, Y. B., & Burke, Q. (2014). Connected code: Why children need to learn programming. MIT Press.
https://doi.org/10.7551/mitpress/9992.001.0001
Kalelioglu, F. (2015). A new way of teaching programming skills to K-12 students: Code. org. Computers in Human Behavior, 52, 200-
210. https://doi.org/10.1016/j.chb.2015.05.047
Kalelioglu, F., & Gulbahar, Y. (2014). The effects of teaching programming via Scratch on problem solving skills: A discussion from
learners’ perspective. Informatics in Education, 13(1), 33-50. https://doi.org/10.15388/infedu.2014.03
Kalelioglu, F., Gulbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review.
Baltic Journal of Modern Computing, 4(3), 583-596.
Kalogiannakis, M., & Papadakis, S. (2017). Pre-service kindergarten teachers acceptance of “ScratchJr” as a tool for learning and
teaching computational thinking and Science education. In Proceedings of the 12th Conference of the European Science
Education Research Association, Research, Practice and Collaboration in Science Education (pp. 21-25).
Kalyenci, D. (2020). Erken çocukluk döneminde kodlama becerilerinin değerlendirilmesi-Test geliştirme [Master’s thesis, Hasan
Kalyoncu University].
Kalyenci, D., Metin, S., & Basaran, M. (2022). Test for assessing coding skills in early childhood. Education and Information
Technologies, 27(4), 4685-4708. https://doi.org/10.1007/s10639-021-10803-w
Karpinski, Z., Biagi, F., & Di Pietro, G. (2021). Computational thinking, socioeconomic gaps, and policy implications. International
Association for the Evaluation of Educational Achievement. https://www.iea.nl/publications/series-journals/iea-compass-
briefs-education-series/january-2021-computational
Kazakoff, E. R. (2014). Cats in space, pigs that race: Does self-regulation play a role when kindergartners learn to code? [Doctoral
dissertation, Tufts University].
Kazakoff, E. R., & Bers, M. U. (2014). Put your robot in, put your robot out: Sequencing through programming robots in early
childhood. Journal of Educational Computing Research, 50(4), 553-573. https://doi.org/10.2190%2FEC.50.4.f
Kazakoff, E. R., Sullivan, A., & Bers, M. U. (2013). The effect of a classroom-based intensive robotics and programming workshop
on sequencing ability in early childhood. Early Childhood Education Journal, 41(4), 245-255. https://doi.org/10.1007/s10643-
012-0554-5
Kazakoff, E., & Bers, M. (2012). Programming in a robotics context in the kindergarten classroom: The impact on sequencing skills.
Journal of Educational Multimedia and Hypermedia, 21(4), 371-391.
Metin et al. / Pedagogical Research, 8(2), em0154 11 / 13
Kucukkara, M. F., & Aksut, P. (2021). Okul öncesi dönemde bilgisayarsiz kodlama eğitimine bir örnek: Problem çözme becerileri
için etkinlik temelli algoritma [An example of computer-free coding education in preschool: Activity-based algorithm for
problem solving skills]. Journal of Inquiry Based Activities, 11(2), 81-91.
Landivar, L. C. (2013). Disparities in STEM employment by sex, race, and Hispanic origin. Education Review, 29(6), 911-922.
Lee, J., & Junoh, J. (2019). Implementing unplugged coding activities in early childhood classrooms. Early Childhood Education
Journal, 47(6), 709-716. https://doi.org/10.1007/s10643-019-00967-z
Lee, K. T., Sullivan, A., & Bers, M. U. (2013). Collaboration by design: Using robotics to foster social interaction in kindergarten.
Computers in the Schools, 30(3), 271-281. https://doi.org/10.1080/07380569.2013.805676
Levy, S. T., & Mioduser, D. (2010). Approaching complexity through planful play: Kindergarten children’s strategies in constructing
an autonomous robot’s behavior. International Journal of Computers for Mathematical Learning, 15(1), 21-43.
https://doi.org/10.1007/s10758-010-9159-5
Linn, M. C., & Clancy, M. J. (1992). The case for case studies of programming problems. Communications of the ACM, 35(3), 121-132.
https://doi.org/10.1145/131295.131301
Madkins, T. C., Howard, N. R., & Freed, N. (2020). Engaging equity pedagogies in computer science learning environments. Journal
of Computer Science Integration, 3(2), 1. http://doi.org/10.26716/jcsi.2020.03.2.1
Manches, A., & Plowman, L. (2017). Computing education in children’s early years: A call for debate. British Journal of Educational
Technology, 48(1), 191-201. https://doi.org/10.1111/bjet.12355
Marsh, J., Plowman, L., Yamada-Rice, D., Bishop, J., & Scott, F. (2016). Digital play: A new classification. Early Years, 36(3), 242-253.
https://doi.org/10.1080/09575146.2016.1167675
Martinez, C., Gomez, M. J., & Benotti, L. (2015, June). A comparison of preschool and elementary school children learning computer
science concepts through a multilanguage robot programming platform. In Proceedings of the 2015 ACM Conference on
Innovation and Technology in Computer Science Education (pp. 159-164). https://doi.org/10.1145/2729094.2742599
Mason, S. (2017). The coding versus language debate. MLTAQ Journal, 166, 23-28.
Mclennan, D. P. (2017). Creating coding stories and games. Journal of Teaching, 10(3), 18-21.
McLennan, D. P. (2018). Code breaker: Increase creativity, remix assessment, and develop a class of coder ninjas! Journal of
Teaching Learning, 12(1), 51-52. https://doi.org/10.22329/jtl.v12i1.5544
Menon, D., Bp, S., Romero, M., & Viéville, T. (2019). Going beyond digital literacy to develop computational thinking in K-12
education. In D. Linda (Ed.), Smart pedagogy of digital learning. Routledge. https://doi.org/10.4324/9780429319501-2
Metin, S. (2020). Activity-based unplugged coding during the preschool period. International Journal of Technology and Design
Education, 32, 149-165. https://doi.org/10.1007/s10798-020-09616-8
Metz, S. S. (2007). Attracting the engineers of 2020 today. In R J. Burke, & M. C. Mattis (Eds.), Women and minorities in science,
technology, engineering, and mathematics: Upping the numbers (pp. 184-209). Edward & Elgar Publishing.
Mittermeir, R. T. (2013). Algorithmics for preschoolers—A contradiction? Creative Education, 4(9), 557-562.
https://doi.org/10.4236/ce.2013.49081
Mohaghegh, M. & McCauley, M. (2016). Computational thinking: The skill set of the 21st century. International Journal of Computer
Science and Information Technologies, 7(3), 1524-1530.
Murcia, K., Pepper, C., Joubert, M., Cross, E., & Wilson, S. (2020). A framework for identifying and developing children’s creative
thinking while coding with digital technologies. Issues in Educational Research, 30(4), 1395-1417.
Nourbakhsh, I. R., Hamner, E., Crowley, K., & Wilkinson, K. (2004). Formal measures of learning in a secondary school mobile
robotics course. In Proceedings of the IEEE International Conference on Robotics and Automation (1831-1836). IEEE.
https://doi.org/10.1109/ROBOT.2004.1308090
Papadakis, S., Kalogiannakis, M., & Zaranis, N. (2016). Comparing tablets and PCs in teaching mathematics: An attempt to improve
mathematics competence in early childhood education. Preschool and Primary Education, 4(2), 241-253.
https://doi.org/10.12681/ppej.8779
Patan, B. (2016). Okul öncesi kodlama öğretim programının geliştirilmesi [Development of preschool coding curriculum] [Master’s
thesis, Bahcesehir University].
Petersen, J., & Hyde, J. S. (2014). Gender-related academic and occupational interests and goals. Advances in Child Development
and Behavior, 47, 43-76. https://doi.org/10.1016/bs.acdb.2014.04.004
Piaget, J. (1973). Psychology and epistemology: Towards a theory of knowledge. Record.
Pila, S., Aladé, F., Sheehan, K. J., Lauricella, A. R., & Wartella, E. A. (2019). Learning to code via tablet applications: An evaluation of
Daisy the Dinosaur and Kodable as learning tools for young children. Computers & Education, 128, 52-62.
https://doi.org/10.1016/j.compedu.2018.09.006
Portelance, D. J., & Bers, M. U. (2015). Code and tell: Assessing young children’s learning of computational thinking using peer
video interviews with ScratchJr. In Proceedings of the 14th International Conference on Interaction Design and Children (pp. 271-
274). https://doi.org/10.1145/2771839.2771894
12 / 13 Metin et al. / Pedagogical Research, 8(2), em0154
Portelance, D. J., Strawhacker, A. L., & Bers, M. U. (2016). Constructing the ScratchJr programming language in the early childhood
classroom. International Journal of Technology and Design Education, 26(4), 489-504. https://doi.org/10.1007/s10798-015-
9325-0
Pugnali, A., Sullivan, A., & Bers, M. U. (2017). The impact of user interface on young children’s computational thinking. Journal of
Information Technology Education. Innovations in Practice, 16, 171. https://doi.org/10.28945/3768
Relkin, E., de Ruiter, L. E., & Bers, M. U. (2021). Learning to code and the acquisition of computational thinking by young c hildren.
Computers & education, 169, 104222. https://doi.org/10.1016/j.compedu.2021.104222
Resnick, M., & Rosenbaum, E. (2013). Designing for tinkerability. In M. Honey (Ed.), Design, make, play (pp. 163-181). Routledge.
https://doi.org/10.4324/9780203108352-15
Resnick, M., & Siegel, D. (2015). A different approach to coding. International Journal of People-Oriented Programming, 4(1), 1-4.
https://doi.org/10.4018/IJPOP.2015070101
Resnick, M., Maloney, J., Monroy-Hernandez, A., Rusk, N., Eastmond, E., Brennan, K., Millner, A., Rosenbaum, E., Silver, J.,
Silverman, B., & Kafai, Y. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60-67. https://doi.org/
10.1145/1592761.1592779
Rogoff, B. (1995). Observing sociocultural activity on three planes: Participatory appropriation, guided participation, and
apprenticeship. In J. V. Wertsch, P. del Río, & A. Alvarez (Eds.), Sociocultural studies of mind (pp. 139-164). Cambridge University
Press. https://doi.org/10.1017/CBO9781139174299.008
Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the
curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129-141.
https://doi.org/10.1016/j.compedu.2016.03.003
Sayin, Z., & Seferoglu, S. S. (2016). Yeni bir 21. yüzyıl becerisi olarak kodlama eğitimi ve kodlamanın eğitim politikalarına etkisi
[Coding education as a new 21st century skill and the effect of coding on education policies]. In Proceedings of the Academic
Informatics Conference.
Seow, P., Looi, C. K., Wadhwa, B., Wu, L., Liu, L., Kong, S. C., Sheldon, J., & Li, K. (2017). Computational thinking and coding
initiatives in Singapore. In Proceedings of International Conference on Computational Thinking Education (pp. 164-167).
Siper Kabadayi, G. (2019). Robotik uygulamalarının okul öncesi çocukların yaratıcı düşünme becerileri üzerine etkisi [The effect of
robotic applications on creative thinking skills of preschool children]. Hacettepe University. http://www.openaccess.
hacettepe.edu.tr:8080/xmlui/handle/11655/8874
Stamatios, P. (2022). Can preschoolers learn computational thinking and coding skills with ScratchJr? A systematic literature
review. International Journal of Educational Reform, 10567879221076077. https://doi.org/10.1177/10567879221076077
Steele, C. M. (1997). A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist, 52(6),
613. https://doi.org/10.1037/0003-066X.52.6.613
Strawhacker, A., & Bers, M. U. (2015). “I want my robot to look for food”: Comparing kindergartner’s programming comprehension
using tangible, graphic, and hybrid user interfaces. International Journal of Technology and Design Education, 25(3), 293-319.
https://doi.org/10.1007/s10798-014-9287-7
Strawhacker, A., & Bers, M. U. (2019). What they learn when they learn coding: Investigating cognitive domains and computer
programming knowledge in young children. Educational Technology Research and Development, 67(3), 541-575.
https://doi.org/10.1007/s11423-018-9622-x
Strawhacker, A., Portelance, D., Lee, M., & Bers, M. U. (2015). Designing tools for developing minds: The role of child development
in educational technology. In Proceedings of the 14th International Conference on Interaction Design and Children.
Strawhacker, A., Relkin, E., & Bers, M. U. (2022). Designing an adaptive assessment for preschool children’s robotics knowledge.
Journal of the International Society for Design and Development in Education, 15.
Sulistyaningtyas, R. E., Yuliantoro, P., Astiyani, D., & Nugraheni, C. (2021). A literature review of coding for early childhood. In
Proceedings of the 2nd Borobudur International Symposium on Humanities and Social Sciences. https://doi.org/10.4108/eai.18-
11-2020.2311709
Sullivan, A. A., Bers, M. U., & Mihm, C. (2017). Imagining, playing, and coding with KIBO: Using robotics to foster computational
thinking in young children. In Proceedings of the International Conference on Computational Thinking Education.
Sullivan, A., & Bers, M. U. (2013). Gender differences in kindergarteners’ robotics and programming achievement. International
Journal of Technology and Design Education, 23(3), 691-702. https://doi.org/10.1007/s10798-012-9210-z
Sullivan, A., & Bers, M. U. (2016). Robotics in the early childhood classroom: Learning outcomes from an 8-week robotics curriculum
in pre-kindergarten through second grade. International Journal of Technology and Design Education, 26(1), 3-20.
https://doi.org/10.1007/s10798-015-9304-5
Sullivan, A., & Bers, M. U. (2019). Computer science education in early childhood: The case of ScratchJr. Journal of Information
Technology Education. Innovations in Practice, 18, 113. https://doi.org/10.28945/4437
Turan, S., & Aydogdu, F. (2020). Effect of coding and robotic education on preschool children’s skills of scientific process. Education
and Information Technologies, 25(5), 4353-4363. https://doi.org/10.1007/s10639-020-10178-4
Unahalekhaka, A., & Bers, M. U. (2022). Evaluating young children’s creative coding: rubric development and testing for ScratchJr
projects. Education and Information Technologies, 27, 6577–6597. https://doi.org/10.1007/s10639-021-10873-w
Metin et al. / Pedagogical Research, 8(2), em0154 13 / 13
Van Roy, P., & Haridi, S. (2004). Concepts, techniques, and models of computer programming. MIT press.
Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda
for research and practice. Education and Information Technologies, 20(4), 715-728. https://doi.org/10.1007/s10639-015-9412-6
Vorderman, C. (2019). Computer coding for kids: A unique step-by-step visual guide, from binary code to building games. Dorling
Kindersley Ltd.
Voyer, D., & Voyer, S. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174.
https://doi.org/10.1037/a0036620
Wang, D., Zhang, C., & Wang, H. (2011). T-Maze: A tangible programming tool for children [Paper presentation]. The 10th International
Conference on Interaction Design and Children. https://doi.org/10.1145/1999030.1999045
Wang, M. T., Eccles, J. S., & Kenny, S. (2013). Not lack of ability but more choice: Individual and gender differences in choice of
careers in science, technology, engineering, and mathematics. Psychological Science, 24(5), 770-775.
https://doi.org/10.1177/0956797612458937
Welch, L., Kozlowski, J., & Evans, H. (2019). Coding to develop early mathematical and computational thinking in kindergarten: A
case study. European Journal of STEM Education 3(3), 8.
Wing, J. M. (2006). Computational Thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
Wyeth, P. (2008). How young children learn to program with sensor, action, and logic blocks. The Journal of the Learning Sciences,
17(4), 517-550. https://doi.org/10.1080/10508400802395069
Zamin, N., Ab Rahim, H., Savita, K., Bhattacharyya, E., Zaffar, M., & Jamil, S. (2018). Learning block programming using Scratch
among school children in Malaysia and Australia: An exploratory study [Paper presentation]. The 2018 4th International
Conference on Computer and Information Sciences. https://doi.org/10.1109/ICCOINS.2018.8510586
