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

Learning.

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.

1 INTRODUCTION

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

(2013).

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,

2016).

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

188

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

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

them.

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.

2 RELATED WORK

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

189

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 (http://www.autothinking.ut.ee/) 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

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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

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191

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.

(a)

(b)

Figure 2: (a) A solution developed by a player, (b) Textual

feedback and hint generated by the game.

4 CASE STUDY

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

sequences

- 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

programs.

- Function

- Loop

- Repeat

- Algorithm

- I don’t know

Q6. Debugging is - What an exterminator does.

- Rewriting code to make it less

complex.

- Finding and fixing problems in an

algorithm or program.

- A girl named Dee annoying

everyone.

- 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.

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Table 2: The post-test questions deployed after the game.

Question Answer

Q1. A sequence is

the order in which

the commands are

given.

- True

- False

- I don’t know

Q2. Define

Pattern

Recognition.

- A sequence of instructions.

- Looking for similarities and

trends.

- Breaking a task into smaller

tasks.

- 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

following

instructions

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

performed

- Command

- Execute

- Function

- Iteration

- I don’t know

Q6. Finding and

fixing problems in

an algorithm or

program.

- Sequencing

- Debugging

- Conditionals

- Behavior

- I don’t know

Q7. Simulation is,

essentially, a

program that

allows the user to

observe an

operation through

simulation

without actually

performing that

operation

- 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

calculated.

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-

tests).

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

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193

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

learners.

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%

none

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.

5 CONCLUSION

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

20%

40%

60%

80%

100%

120%

1234567

Q1-Q7

pre-test post-test

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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.

REFERENCES

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 http://www.oecd.org/ 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).

IEEE.

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:

https://www.esnetwork.eu/. 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:

465-472

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

Sciences

, 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),

321-325.

Investigating the Learning Impact of Autothinking Educational Game on Adults: A Case Study of France

195

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-

14).

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.

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