Investigating the Learning Impact of Autothinking Educational
Game on Adults: A Case Study of France
Nour El Mawas
, Danial Hooshyar
and Yeongwook Yang
CIREL (EA 4354), University of Lille, Lille, France
Institute of Education, University of Tartu, Tartu 50103, Estonia
Keywords: Technology Enhanced Learning, Computational Thinking, Educational Game, Adaptive Learning, Adult
Abstract: Adults have different needs for education and training throughout their lives in order to maintain and progress
in their job or find a new one. Nowadays, Computational Thinking is one of the 21st century skills that adults
must acquire and develop. In this context, some adults have difficulties to find new teaching and learning
methodologies that help them learn Computational Thinking. Technology Enhance Learning and specifically
Educational Games give the opportunity to learners to enhance their Computational Thinking skills and
conceptual knowledge. This paper presents a research study on the learning impact of an adaptive educational
game, called AutoThinking, developed for promoting Computational Thinking skills and conceptual
knowledge. The game was used by adults in a Master class at the Université de Lille in France. Pre- and Post-
tests results analysis has shown that the game helped the adults to acquire knowledge on the Computational
Thinking: 92% of adults have answered correct at least 4 questions out of 7 in the post-test versus only 34%
of learners in the pre-test.
These days, an individual will have a wide range of
employment opportunities during his/her lifetime.
Lifelong learning is becoming a central asset,
beginning with the university and continuing through
the professional career with different jobs. Adults
have different needs for education and training
throughout their lives (El Mawas et al., 2017). Many
job seekers/employees find themselves in need of
acquiring or improving their technology skills to
maintain and progress in their jobs or find new career
opportunities. Computational Thinking (CT) skills
are among those skills (El Mawas et al., 2018) that
adults need to keep up-to-date according to the OECD
CT is defined as the mental ability enabling
learners to develop a computational solution for a
problem in hand (Wing, 2006). In other words, CT is
a cognitive ability reflecting the application of key
reasoning process and concepts of computer science
into science, technology, engineering, and
mathematics (STEM) domains, as well as wide range
of problems and activities in everyday life (Wang,
As a practical skill computer programming shares
common and similar ideas with CT’s construct as a
cognitive ability, such as concept of sequence, loops,
conditionals, and parallelism. Additionally, CT
involves some key cognitive counterparts of
computer programming concepts, namely
algorithmic thinking, decomposition, conditional
logic, pattern recognition, debugging, simulation, and
generalization. As stated by the founder of CT, Wing
(2006), CT is not computer programming (coding in
particular), and instead it refers to problem solving by
way of computing. More specifically, CT’s products
are ideas and concepts used to approach and solve
problems, and it starts before writing the code. Given
the fact that CT denotes a general and applicable
problem solving strategy for wide range of domains,
it has been highlighted as one of the main and
fundamental 21st century skill (Wing, 2008).
Several research have shown that learners’
analytical skills could potentially be improved by
teaching CT concepts and skills, and possessing such
abilities could possibly be seen as indication of
El Mawas, N., Hooshyar, D. and Yang, Y.
Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France.
DOI: 10.5220/0009790301880196
In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 2, pages 188-196
ISBN: 978-989-758-417-6
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
learners’ academic success (Haddad & Kalaani,
2015). Thus, similar to numeracy and literacy, CT is
considered as a vital competence for everyone, not
just computer scientists, that should be acquired and
taught early in education. Recently, several
reformations and adaptations of educational
programs have taken place in different education
level all over the world as both cognitive and non-
cognitive benefit of integration of CT into educational
curricula is indicated by many research (e.g., Brown,
Sentance, Crick, & Humphreys, 2014; Repenning et
al., 2015). For instance, several recent references
related to governmental institutions and educational
programs have highlighted that CT is being added to
primary, secondary, higher educational programs,
and adult learning all over the world (European
School Network, 2020) (OECD, 2013). However,
there exists two major challenges in fostering CT
which are lack of motivation and opportunities to
improve learners’ CT skills. To this end, some
research show that school learners usually show
negative attitude toward learning CT, hindering
proper development of CT skills (e.g., Yardi &
Bruckman, 2007). To approach these issues, different
methods have been employed to make CT more
accessible to learners, educational games among
Educational games have gained a lot of attentions
lately as they have proven to be effective learning
tools engaging and motivating learners (El Mawas et
al., 2019). Findings from several research show that
educational games are capable of bringing about
improvements in both learners’ motivation and
learning achievements (Hooshyar et al., 2018a).
Although there exists several educational games for
fostering CT, they chiefly ignore promoting CT skills
(as such) and providing adaptivity in game-play and
teaching process (Kazimoglu et al., 2012). Instead,
they reinforce CT’s theoretical knowledge while
promoting learners’ motivation. What’s more, they
mostly follow predefined and rigid computer-assisted
instruction concepts (ignoring adaptivity which
considers individual needs and characteristics)
making them fall short when it comes to different
player’s needs. Regarding the former issue (ignoring
CT skills), while educational games developed for
promoting CT indeed improve abstract and
theoretical knowledge, they do not provide learners
with opportunities to develop their CT skills
(Kazimoglu et al., 2012).
Basically, in games with focus on improving CT
abstract and theoretical knowledge, contextual
relationship between the focus of the game and the
knowledge being acquired is of less importance and
may even be completely abstract, providing less
opportunities to develop CT skills. On the other hand,
games that aim to teach CT skills offer opportunities
to practice the conceptual knowledge through game-
play. Thus, we must distinguish between games that
target teaching applied knowledge and skills, and
those that aim reinforcing theoretical knowledge. In
terms of the latter issue of CT games (ignoring
adaptivity), despite several calls urging researchers
and practitioners to pay more attention to adaption
and personalization to the individual needs, existing
CT games mainly follow unadaptable and rigid
computer-assisted instruction concepts, resulting in
plaguing the full educational potential of computer
games (e.g., Kickmeier-Rust et al., 2011; Hooshyar et
al., 2018b).
Given the societal relevance and importance of
CT, and the existing gaps in CT game research that
undermine their educational potential, we developed
an adaptive game for teaching both CT concepts and
skills, engaging learners with individually tailored
gameplay (called AutoThinking) (Hooshyar et al.,
2019). To evaluate the effectiveness of our proposed
game, in this study, we design and conduct a study to
investigate possible effect of AutoThinking on adults.
This research work is dedicated to Education and
Computer Science active communities and more
specifically to directors of training centres / CT
teachers, and lifelong learners who meet difficulties
to learn CT concepts.
The outline of this paper is as follows: Section 2
reviews the related studies in the area of educational
games research aimed at fostering CT. Section 3
presents our AutoThinking game, while Section 4
illustrates our case study and the results analysis.
Section 5 offers conclusion of this study.
Because computer programming shares common and
similar ideas with CT’s construct as a cognitive
ability, several learning environments use
programming, coding in particular, to teach CT to
learners (Grover & Pea, 2013). Most of these
environments use block-based and visual
programming environments, or adapt game design
principles to reduce the complexities associated to
programming languages syntax by simplifying it
down to drag-and-drop interactions. Some example of
such environments are Scratch (Resnick et al., 2009),
Snap! (Harvey & Mönig, 2010), and Blockly (Fraser,
2013). Even though these approaches have shown
Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France
some success in improving learners’ motivation in
programming activities and CT, they fall short when
it comes to promoting deeper learning (e.g., Brennan
& Resnick, 2012; Meerbaum-Salant, Armoni, & Ben-
Ari, 2011). One reason is that even though CT’s main
focus is conceptualization and underlying taught
processes of solving a problem not coding, using
these environments learners still get distracted and
overwhelmed by syntax of programming languages
presented to them in different forms (e.g. blocks). In
other words, alignment of these environments with
CT skills is incomplete. Furthermore, though such
environments rely on game design principles and are
often named as games for fostering CT, they cannot
be considered as educational games as they lack
several essential elements of educational games, such
as timely feedback, encouraging engagement,
improving retention, and incentives.
Educational games which are well-known
vehicles for developing many different skills in
education and proven to be effective learning tools
have also been developed and used for developing
learners’ CT knowledge (e.g. Weintrop & Wilensky,
2012). Usually, educational games aimed at fostering
CT use motivating context to engage learners in
process of developing solutions to solve a problem
(e.g. Kazimoglu et al., 2012). Compared to block-
based or visual programming environments (or
designed-based learning environments), such
educational games have a capacity to foster more
purposeful learning with richer learning support
through different game elements (e.g., Land, 2000).
For instance, Eagle and Barnes (2009) developed an
educational game called Wu's Castle; Esper, Foster,
Griswold, Herrera, and Snyder (2014) developed
CodeSpell; and Ayman, Sharaf, Ahmed, and
Abdennadher (2018) developed MiniColon for
teaching programming and promoting CT. Even
though these games are reported to be useful for
developing learners’ CT and a number of studies on
these games found their positive impact on learners
programming and CT learning, they are not aligned
with CT as they employ a text-based programming
language that begs a substantial attention of learners
to syntax details (Zhao & Shute, 2019).
On the other hand, such educational games still
mostly suffer from two issues: ignoring development
of CT skill of learners and adaption to each learners’
need. In regards to the former, educational games
aimed at fostering CT mainly reinforce CT’s
theoretical knowledge while promoting learners’
motivation, providing less opportunities to develop
CT skills. Concerning the latter issue, games for
fostering CT mainly ignore adaption and
personalization to the individual needs. In other
words, such games follow unadaptable and rigid
computer-assisted instruction concepts, resulting in
plaguing the full educational potential of computer
games. In brief, research has shown promising results
concerning application of educational games to CT
among learners. However, there still exist some room
for improvement of such games. To improve the
existing games, we developed an adaptive CT game
engaging users with individually tailored gameplay
and learning process that helps to foster both learners’
CT concepts and skills.
3 THE AutoThinking GAME
3.1 Overview of the Game
AutoThinking ( is an
adaptive educational game developed for promoting
CT skills and concepts (Hooshyar et al., 2019). It uses
icons rather than syntax of computer programming
languages in order to exclude syntactical errors,
reducing the cognitive load of learners (see Figure 1).
AutoThinking, to the best of our knowledge, is the
first adaptive educational game developed for
promoting CT that includes adaptivity in both game-
play and learning process. It, in a novel way,
promotes four CT skills, namely problem
identification and decomposition (algorithmic
thinking), algorithm building (pattern recognition and
generalization), debugging, and simulation. What’s
more, it fosters three CT concepts, including
sequence, conditional, and loop.
Figure 1: AutoThinking’s interface.
In brief, AutoThinking includes three levels where
players should, in a role of a mouse, develop different
types of strategies or solutions to—collect as many
cheese and score as possible, and scape from two cats
in the maze—complete or win the level. Players are
given opportunity to develop up to 20 solutions for
CSEDU 2020 - 12th International Conference on Computer Supported Education
clearing all 76 cheeses on the maze. During the game-
play, players receive more score for solutions that
involve various CT concepts or skills compared to
traversing empty tiles, or only using simple solutions.
Note that players are provided with various options in
the game to develop different types of solutions, for
example, they can use the “function bar”, see Figure
1, to save various patterns, and if necessary apply or
generalize them in different situation of the game.
What’s more, before developing or running solutions,
players should thoughtfully and carefully observe the
movement of both cats and consider the risk of
running their solution for the current state of the
maze. Note that one cat moves randomly through the
maze according to the number of commands placed
by the player in the “solution bar” (e.g., a solution that
is appropriate for the current state of the maze might
be inappropriate for another situation), whereas the
other cat moves intelligently according to the number
of tiles traversed by the mouse and the quality of the
developed solutions (skill of players). According to
the suitability of solutions for the current state of the
maze, players are adaptively given various type of
feedback (textual, graphical, or video) and hints.
Several activities and features in AutoThinking
game are designed and embedded to target and
promote different CT skills and concepts. These
include “function bar” to encourage players to
construct generalizable patterns where they can be
used in different situations of the game (targeting
algorithmic thinking and pattern recognition skill);
“debug” button enabling players to monitor their
solution algorithm and possibly detect any potential
errors in their logic (practicing debugging skill);
“simulation” button to allow players to simulate their
solution before actually executing it to observe the
outcome of their solution regardless of intervention of
other variables in the game, such as cats movements
and cheeses (practicing run time mode or simulation
skill); “solution bar” to help players to develop
different solutions for different situations of the maze,
or different problems, using sequence of proper
actions (targeting both problem-solving and
sequence); “loop” button to run the same sequence of
actions multiple times (practicing loop concept); and
finally “conditional” button to enable player to make
decisions based on certain decisions that supports
expression of multiple outcomes (practicing
conditional concept).
3.2 Adaptivity in Game-play
During the game-play, one of the cats moves
intelligently according to the quality of the developed
solution by the player. To do so, it considers whether
the solution has the potential to gain enough score,
whether it is risky and the mouse might get caught by
cats, and whether players used proper CT skills or
concepts in their developed solution according to the
current state of the maze. Accordingly, a decision-
making technique used in the game—provided by a
probabilistic model, Bayesian Network, that
automatically assesses player’s skills—regulates the
movement of the cat by switching between the
following algorithms:
The cat decides to move randomly without
iteration through the maze.
The cat decides to move aggressively aimed
at catching the mouse (by finding the
shortest distance from the mouse).
The cat decides to move provocatively by
going close to the mouse (up to one tile
away), not to catch it, and come back.
The cat decides not to get closer than 6 tiles
away from the mouse.
Observe that the cat decides to choose a more
appropriate algorithm to use for its movements
according to both short term and long term solution
of the player. In other words, it considers both the
current solution developed and also previous
solutions developed by the player. However, another
cat still moves randomly with repetition according to
the number of commands used in the solution, making
AutoThinking an unpredictable and never-ending
game that always provides player with a new situation
that might have never happened for previous players.
3.3 Adaptivity in Learning
While playing the game, the automatic short and long
term assessment of the players enables the game to
provide them with timely feedback and hints.
According to the skill level of the players and
current status of the maze, the game offers textual,
graphical, or video feedback about CT concepts and
skills that are embedded in the game-play. It also
highlights some of the game features or buttons as a
hint, enabling players to improve their solutions
according to both the hint and feedback (see Figure
2). This phase of adaptivity takes place in two
different timings, before or after running the solution.
Regarding the former one, after players have
developed their solution they can use the “debug”
button—which activates the probabilistic model used
for decision-making—to see the estimation of the
suitability of their solution in a form of timely
adaptive feedback or hints. Doing so provides player
Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France
with a chance to, if necessary, change and improve
their solution so as to have a more optimum solution.
Alternatively, concerning the latter situation, player
can skip using the “debug” option and directly “run”
the game. This results in timely adaptive feedback or
hint, after running the game, which would help
players know about their shortcomings and mistakes
in previous solutions and possible ways to overcome
them. Such adaptivity—which aim to foster both
learners’ problem-solving (algorithmic thinking) and
pattern recognition skills—individually support
learners in developing the most optimum solution for
the problem in hand.
Figure 2: (a) A solution developed by a player, (b) Textual
feedback and hint generated by the game.
The goal of the research study was to investigate the
learning impact of the AutoThinking game in the
class to teach CT skills and concepts.
This section presents the evaluation methodology
applied, case study set-up, and results analysis of the
collected data.
4.1 Research Methodology
As this research study is focused on knowledge
acquisition aspect of game-based learning in the
AutoThinking game, pre- and post-test assessments
were run before and after the use of the game.
Table 1: The pre-test questions deployed before the game.
Question Answer
Q1. What is a sequence?- A piece of code that is written over
and over again
- The small shiny things sewn onto
clothes for a fancy effect
- An error in the coding language
- The order of events that the
computer will complete
Q2. _____ is about
analyzing and
identifying repeated
- Type answer: _____________
- I don’t know
Q3. The action of doing
something over and ove
again is “conditionals”.
- True
- False
- I don’t know
Q4. The if, elif, else
statement is used for
- Selection.
- Iteration.
- Indentation.
- Printing.
- I don’t know.
Q5. _____ is a named
group of programming
instructions. They are
reusable abstractions
that reduce the
complexity of writing
and maintaining
- Function
- Loop
- Repeat
- Algorithm
- I don’t know
Q6. Debugging is - What an exterminator does.
- Rewriting code to make it less
- Finding and fixing problems in an
algorithm or program.
- A girl named Dee annoying
- I don’t know.
Q7. Simulation is - A model that's used to see how a
specific process will work.
- A full-scale working model used to
test a design to see if it solves the
problem it was created to. address
- A graph that uses vertical or
horizontal bars to show comparisons
among two or more items.
- A graph that uses line segments to
show changes that occur over time.
- I don’t know.
CSEDU 2020 - 12th International Conference on Computer Supported Education
Table 2: The post-test questions deployed after the game.
Question Answer
Q1. A sequence is
the order in which
the commands are
- True
- False
- I don’t know
Q2. Define
- A sequence of instructions.
- Looking for similarities and
- Breaking a task into smaller
- Focusing on what is important
and ignoring what is unnecessary
- I don’t know
Q3. _____ is the
action of doing
something over
and over again
- Type answer: _____________
- I don’t know
Q4. Which of the
allows a program
to search a list of
options and make
a decision?
- If
- Select
- Function
- Choose
- I don’t know.
Q5. A piece of
code that includes
the steps
- Command
- Execute
- Function
- Iteration
- I don’t know
Q6. Finding and
fixing problems in
an algorithm or
- Sequencing
- Debugging
- Conditionals
- Behavior
- I don’t know
Q7. Simulation is,
essentially, a
program that
allows the user to
observe an
operation through
without actually
performing that
- True
- False
- I don’t know
The research methodology applied in this case
study involved 12 students from the Digital Learning
Management Master. Note that students in this
Master class are adults and they do not have any
course about CT. All students learned about the CT
by playing the educational game. The learning
process took place during the university study hours.
All the tests were implemented in the online survey
tool, Lime Survey, and provided to learners online via
Moodle. The case study consisted of several phases
which cover the collection of assent and consent
forms, description of the realised course, special pre-
questionnaires, knowledge pre-test, learning
experience, knowledge post-test, and other post-
questionnaires. In this paper, we are interested in the
knowledge pre- and post-tests.
Each learner played the game individually in the
computer room with a teacher present in the room, but
the teacher did not answer any question related to the
subject. In order to evaluate learners’ level of
knowledge on the subject prior the particular
pedagogical approach all students did the same pre-
test. Similarly, the same post-tests were provided to
all students to analyse and evaluate level of acquired
knowledge. Tables 1 and 2 show questions of pre- and
post-test applied during the experimentation. The pre-
and post-tests creation followed requirements such as
they should last max. 10 minutes, both tests should
have very similar content (Table 3) and identical
concept. These tests consist of a single choice and
simple answer questions.
Based on knowledge tests results an average score
can be calculated for students. By comparing average
pre-test and post-test scores a knowledge gain can be
Table 3: The addressed concept in each question.
Question (pre- and post-test) Concept
Q1 Sequence
Q2 Pattern recognition
Q3 Loop
Q4 Conditional
Q5 Function
Q6 Debugging
Q7 Simulation
4.2 Results Analysis
The research focuses on the knowledge acquisition
while students play the game. The evaluation was
based on the results of knowledge tests (pre- and post-
Final results showing the level of learner’
knowledge in percentage are depicted in Figure 3.
The AutoThinking game increases knowledge level
of learners by 21.4%. More specifically, we can
notice that the game improves the sequence concept
by 33%, the pattern recognition concept by 25%, the
conditional concept by 16%, the function concept by
42%, and the simulation concept by 50%. However,
the knowledge about the loop and debugging
concepts were slightly decreased by 8%. One possible
explanation could be that some students could not
properly read or understand the feedback and hints
Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France
provided by the game due to several reasons, e.g.,
language barrier. Additionally, some students may
have ignored using the “debug” button, which offers
chance to monitor solution algorithms and detect any
potential errors in their logic, as the game does not
enforce using this option and players can run their
solution even without debugging. This results in not
receiving some useful feedback or hints related to
different concepts or skills, among them loop logic.
The pre-test and post-test results are displayed in
Table 4, were the percentage of correct answers and
the corresponding number of learners are provided.
Regarding the pre-test, no learners answered correctly
all pre-test questions or 6 questions out of 7 questions.
17% of learners provided correct answers to 5
questions out of 7 and 4 questions out of 7 in the pre-
test. 50% of learners provided correct answers to 3
questions out of 7. 16% of learners provided correct
answers to either all questions or answered correctly
only 1 or 2 questions out of 7.
Figure 3: Average of pre- and post- test scores.
Table 4: Number of questions correctly answered by
Pre-test Post-test
7 out of 7
0% 17%
6 out of 7
0% 0%
5 out of 7
17% 33%
4 out of 7
17% 42%
3 out of 7
50% 8%
2 out of 7
8% 0%
1 out of 7
8% 0%
0% 0%
Regarding the post-test, 17% of learners answered
correctly all post-test questions and no learners
provided correct answers to 6 questions out of 7 in the
post-test. 33% of learners provided correct answers
to 5 questions out of 7. 42% of learners provided
correct answers to 4 questions out of 7. 8% of students
have answered at most 3 questions out of 7.
An analysis of the results shows that
AutoThinking game increases the learning outcomes
for the learners. 92% of learners have answered
correct at least 4 questions out of 7 in the post-test
versus only 34% of learners in the pre-test.
In general, students' answers revealed the positive
effect of the CT game and the fact that how an
adaptive educational game could successfully engage
learners in an interactive learning environment for
promoting their CT skills. Findings of this
preliminary study also unveiled that without highly
complex learning environments, it is still possible to
encourage students to produce some appropriate
computational problem-solving practices, thereby
fostering their CT concepts and skills. One possible
reason for this encouraging findings is the adaptivity
feature improvised in the game which enables the
game to treat each learner according to his/her skill
level. Such claim is in line with previous findings
reported by other researchers. For instance, both
Kickmeier-Rust et al. (2011) and Hooshyar, Yousefi,
and Lim. (2018c) concluded that a meaningful
personalization and adaptivity (individual support)
are among crucial factors leading to the success of
educational games which eventually result in
improving learning performance.
The paper presented a case study that investigated the
learning impact of an adaptive educational game
called AutoThinking on adults. The educational
game is about promoting CT skills and concepts
where players should, in a role of a mouse, collect
cheese and scape from two cats in the maze in order
to complete or win the level. The game offers
adaptivity in terms of game-play and learning. Pre-
and Post- tests results analysis has shown that the
game helped the adults to acquire knowledge on the
CT especially for the sequence, the pattern
recognition, the conditional, the function, and the
simulation concepts.
As a future work, we plan to design and carry out
a number of experimental studies with larger sample
size so as to more accurately measure the effect of
pre-test post-test
CSEDU 2020 - 12th International Conference on Computer Supported Education
AutoThinking game on learning gain of players. The
experimental studies will include interviews that can
be in the focus group mode. What’s more, we aim to
investigate the effect of adaptivity in the game by
running a study between two different versions of the
game, adaptive versus non-adaptive in different
European countries.
El Mawas, N., Gilliot, J.-M., Garlatti, S., Serrano Alvarado,
P., Skaf-Molli, H., Eneau, J., Lameul, G., Marchandise,
J. F. & Pentecouteau, H. (2017): Towards a Self-
Regulated Learning in a Lifelong Learning Perspective.
Proceedings of the 9th International Conference on
Computer Supported Education , CSEDU (1) pp. 661-
670, 2017.
El Mawas, N., Bradford, M., Andrews, J., Pathak, P. &
Hava Muntean, C. (2018). A Case Study on 21st
Century Skills Development Through a Computer
Based Maths Game. In Proceedings of EdMedia: World
Conference on Educational Media and Technology (pp.
1160-1169). Amsterdam, Netherlands.
Organisation for Economic Co-operation and Development
[OECD]. OECD skills outlook 2013. First results from
the survey of adult skills. Retrieved February 19, 2020
at site/piaac/publications.htm
Wing, J. M. (2006). Computational thinking.
Communications of the ACM, 49(3), 33-35.
Wang, P. S. (2016). From computing to computational
thinking. Chapman and Hall/CRC..
Wing, J. M. (2008). Computational thinking and thinking
about computing. Philosophical Transactions of the
Royal Society A: Mathematical, Physical and
Engineering Sciences, 366(1881), 3717-3725.
Haddad, R. J., & Kalaani, Y. (2015). Can computational
thinking predict academic performance?. In 2015 IEEE
Integrated STEM Education Conference (pp. 225-229).
Brown, N. C., Sentance, S., Crick, T. & Humphreys, S.
(2014). Restart: The resurgence of computer science in
UK schools. ACM Trans. Comput. Educ. TOCE, vol.
14, no. 2, p. 9, 2014.
Repenning, A., Webb, D. C., Koh, K. H., Nickerson, H.,
Miller, S. B., Brand, C., ... & Gutierrez, K (2015).
Scalable game design: A strategy to bring systemic
computer science education to schools through game
design and simulation creation. ACM Trans. Comput.
Educ. TOCE, vol. 15, no. 2, p. 11, 2015.
European School Network Homepage: Accessed 9 Jan 2020.
Yardi, S., & Bruckman, A. (2007). What is computing?
Bridging the gap between teenagers' perceptions and
graduate students' experiences. In Proceedings of the
third international workshop on Computing education
research (pp. 39-50).
El Mawas, N., Trúchly, P., Podhradský, P., Hava Muntean,
C. (2019). The Effect of Educational Game on Children
Learning Experience in a Slovakian School. In
Proceedings of the 12th International Conference on
Computer Supported Education. CSEDU (1) 2019:
Hooshyar, D., Yousefi, M., Wang, M., & Lim, H. (2018a).
A datadriven proceduralcontentgeneration
approach for educational games. Journal of Computer
Assisted Learning, 34(6), 731-739.
Kazimoglu, C., Kiernan, M., Bacon, L., & Mackinnon, L.
(2012). A serious game for developing computational
thinking and learning introductory computer
programming. Procedia-Social and Behavioral
, 47, 1991-1999.
Kickmeier-Rust, M. D., Mattheiss, E., Steiner, C., & Albert,
D. (2011). A psycho-pedagogical framework for multi-
adaptive educational games. International Journal of
Game-Based Learning (IJGBL), 1(1), 45-58.
Hooshyar, D., Yousefi, M., & Lim, H. (2018b). Data-driven
approaches to game player modeling: a systematic
literature review. ACM Computing Surveys (CSUR),
50(6), 1-19.
Hooshyar, D., Lim, H., Pedaste, M., Yang, K., Fathi, M., &
Yang, Y. (2019). AutoThinking: An Adaptive
Computational Thinking Game. In International
Conference on Innovative Technologies and Learning
(pp. 381-391). Springer, Cham.
Grover, S., & Pea, R. (2013). Computational thinking in K–
12: A review of the state of the field. Educational
researcher, 42(1), 38-43.
Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk,
N., Eastmond, E., Brennan, K., ... & Kafai, Y. (2009).
Scratch: programming for all. Communications of the
ACM, 52(11), 60-67.
Harvey, B., & Mönig, J. (2010). Bringing “no ceiling” to
Scratch: Can one language serve kids and computer
scientists. Proc. Constructionism, 1-10.
Fraser, N. (2013). Blockly: A visual programming editor.
URL: https://code. google. com/p/blockly.
Brennan, K., & Resnick, M. (2012). New frameworks for
studying and assessing the development of
computational thinking. In Proceedings of the 2012
annual meeting of the American educational research
association, Vancouver, Canada (Vol. 1, p. 25).
Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2011,
June). Habits of programming in scratch. In
Proceedings of the 16th annual joint conference on
Innovation and technology in computer science
education (pp. 168-172).
Weintrop, D., & Wilensky, U. (2012). RoboBuilder: A
program-to-play constructionist video game. In
Proceedings of the constructionism 2012 conference.
Athens, Greece.
Land, S. M. (2000). Cognitive requirements for learning
with open-ended learning environments. Educational
Technology Research & Development, 48(3), 61–78.
Eagle, M., & Barnes, T. (2009). Experimental evaluation of
an educational game for improved learning in
introductory computing. ACM SIGCSE Bulletin, 41(1),
Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France
Esper, S., Foster, S. R., Griswold, W. G., Herrera, C., &
Snyder, W. (2014). CodeSpells: bridging educational
language features with industry-standard languages. In
Proceedings of the 14th Koli calling international
conference on computing education research (pp. 05-
Ayman, R., Sharaf, N., Ahmed, G., & Abdennadher, S.
(2018). MiniColon; teaching kids computational
thinking using an interactive serious game. In Joint
International Conference on Serious Games (pp. 79-
90). Springer, Cham.
Zhao, W., & Shute, V. J. (2019). Can playing a video game
foster computational thinking skills?. Computers &
Education, 141, 103633.
Hooshyar, D., Yousefi, M., & Lim, H. (2018c). A
procedural content generation-based framework for
educational games: Toward a tailored data-driven game
for developing early English reading skills. Journal of
Educational Computing Research, 56(2), 293-310.
CSEDU 2020 - 12th International Conference on Computer Supported Education