=Paper=
{{Paper
|id=Vol-2676/paper5
|storemode=property
|title=Computational Thinking and Acting: An Approach for Primary School Competency Development
|pdfUrl=https://ceur-ws.org/Vol-2676/paper5.pdf
|volume=Vol-2676
|authors=Jan Pawlowski,Kati Clements,Dimitra Dimitrakopoulou,Martin Idzik,Mikko Muilu,Mihkel Pilv,Sofoklis Sotiriou
|dblpUrl=https://dblp.org/rec/conf/ectel/PawlowskiCDIMPS20
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==Computational Thinking and Acting: An Approach for Primary School Competency Development==
https://ceur-ws.org/Vol-2676/paper5.pdf
Computational Thinking and Acting: An Approach for
Primary School Competency Development
Jan Pawlowski12, Kati Clements2, Dimitra Dimitrakopoulou3, Martin Idzik1, Mikko
Muilu2, Mihkel Pilv4 and Sofoklis Sotiriou3
1 Ruhr West University of Applied Sciences, Germany
2 University of Jyväskylä, Finland
3 Ellinogermaniki Agogi, Greece
4 Miksike OU, Estonia
jan.pawlowski@hs-ruhrwest.de
Abstract. Computational Thinking has become an important concept for almost
all age groups. It describes the purposeful utilization of Information and Com-
munication Technologies for solving problems. The approach of Computational
Thinking and Acting combines the computational thinking approach with physi-
cal computing, including physical activities to explore real-world problems and
tangible outcomes. In this paper, we present a competency framework, design
principles, and a sample learning scenario for Computational Thinking and Act-
ing. The evaluation has shown that the approach can be integrated across subjects
and is promising for both teachers and pupils.
Keywords: Computational Thinking, Primary School, Computational Thinking
and Acting, Physical Computing
1 Introduction
Computational Thinking (CT) comprises of competencies necessary to utilize infor-
mation and communication technologies to solve problems. In the current wave of Dig-
ital Transformation, CT competencies become relevant in many different subjects. In
this paper, we extend the concept of CT showing how CT can be developed across
subjects in primary schools using a physical computing approach.
The current trend of Digital Transformation leads to new requirements for organiza-
tions and individuals [1, 2]. New competencies are needed on all hierarchy levels and
in different contexts: basic digital skills for using digital technologies are often ex-
pressed within the concept of Digital Literacy [3, 4]. More specific competencies will
be required in the future work environments or the digital / digitized workplace [5, 6].
Individuals will need specific competencies for purposefully utilizing emerging or dis-
ruptive technologies, such as artificial intelligence, digital security or business analytics
[7]. It is common to different frameworks for future competencies / skills [8, 9] that
competencies on the use and utilization of information and communication technolo-
gies are of urgent importance. It is expected that those skills will improve different
aspects, in particular future employability [10, 11].
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2
Different pathways have been initiated to promote and improve Computational Think-
ing in different parts of education systems: on the policy level, the European Commis-
sion strongly promotes CT skills in the school system [12]. On the operational level,
many countries have integrated CT into their curricula [13] and teacher education stand-
ards [14]. One key issue is that CT is no longer focused on the disciplines of Computer
Science or Information systems: CT competencies will be needed in different subjects
and (industry) domains [15, 16, 17, 18]. Based on these initial findings, we aim at de-
veloping an approach for Computational Thinking integrating latest trends and devel-
opments of Digital Transformation. The main research questions are:
1. Which competencies are necessary to utilize information and communication tech-
nologies for problem solving across subjects?
2. How to design activity-oriented learning and teaching scenarios to be integrated into
curricula across subjects?
We focus on early education in primary schools, in particular for grades 3 to 6. In this
paper, we describe the approach of Computational Thinking and Acting which includes
a pedagogical framework and competencies as well as a proposal for cross-subject
learning scenarios.
2 Background
2.1 Computational Thinking
Currently, there is no common definition of Computational Thinking (CT). The cur-
rent understanding of the idea is based on the concept of Papert [19] and his idea of
teaching computing to children. Previous works [20] have also focused on the ways
computers solve problems, in particular algorithmic thinking [21]. The current use of
the term was strongly influenced by Wing [22] who defined Computational Thinking
as an “approach to solving problems, designing systems and understanding human be-
havior by drawing on the concepts fundamental to computer science”. Cuny et al [23]
define CT as “Computational thinking is the thought processes involved in formulating
problems and their solutions so that the solutions are represented in a form that can be
effectively carried out by an information-processing agent”. A shorter definition is
given by Berland & Wilensky [24] as “the ability to think with the computer-as-tool”.
While there is no consensus on the definition, we use the following definition:
“The ability to understand and utilize information and communication technologies
and their key concepts, methods and tools to purposefully solve real-world problems”
More important than the definition is the range of competencies included in this con-
cept. As an example, the International Society for Technology in Education (ISTE) and
Computer Science Teachers Association (CSTA) define key competencies as “Formu-
lating problems in a way that enables us to use a computer and other tools to help solve
them; Logically organizing and analyzing data; Representing data through abstractions
such as models and simulations; Automating solutions through algorithmic thinking (a
series of ordered steps); Identifying, analyzing, and implementing possible solutions
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3
with the goal of achieving the most; efficient and effective combination of steps and
resources; Generalizing and transferring this problem solving process to a wide variety
of problems.” [14]. Grover & Pea [18] describe the following competencies as common
for the purpose of curriculum development in the K-12 context: “Abstractions and pat-
tern generalizations (including models and simulations); Systematic processing of in-
formation; Symbol systems and representations; Algorithmic notions of flow of con-
trol; Structured problem decomposition (modularizing); Iterative, recursive, and paral-
lel thinking; Conditional logic; Efficiency and performance constraints; Debugging and
systematic error detection”
To further compare approaches, we have analyzed different approaches. We identi-
fied 15 national and international approaches [12, 13, 14, 18] which represent the broad
range of competencies which might be included in the concept Computational Think-
ing. Four key questions have emerged from the analysis. The first question is whether
programming should be an essential learning outcome in CT. Some approaches inte-
grate programming into CT, for example using visual programming languages [18]. At
least, the long-term intention of most approaches is that children learn programming
languages.
The second key question is the inclusion of emotional aspects, i.e., attitudes and
dispositions. As an example, ISTE CSTA [14] propose five dispositions: “Confidence
in dealing with complexity, persistence in working with difficult problems, tolerance
for ambiguity, the ability to deal with open ended problems, the ability to communicate
and work with others to achieve a common goal or solution”. Generally, there are still
many negative dispositions associated with Computer Science, e.g. as a nerd activity or
“not for girls”. [25, 26].
The third key question from the analysis regards specific (new) technologies. Almost
no approach includes emerging technologies. However, as currently many technologies
emerge which will dramatically change education and professional life as well as meth-
ods to solve problems (e.g. Machine Learning, Artificial Intelligence, 3D Printing), we
would suggest to incorporate those into competency frameworks.
Finally, it is essential to connect real world problems and computer solutions. As
Tissenbaum et al [27] criticize the “[...] initial focus on the concepts and processes of
computing, leaving real-world applications for "later" runs the risk of making learners
feel that computing is not important for them.”
2.2 Physical Computing
Computer activities are often seen as passive with even strong negative effects on
children’s physical condition [28]. However, many CT approaches include the use of
tangible, haptic devices such as robots [29, 30] to increase motivation, creativity, en-
gagement and physical activity [31]. Przybylla & Romeike [32] describe Physical Com-
puting as following: “Physical Computing covers the design and realization of interac-
tive objects and installations and allows students to develop concrete, tangible products
of the real world, which arise from the learners’ imagination”. A similar concept is
Tangible Computing [33]. Common to those approaches is that computer activities
should result in tangible (visual and haptic) experiences. Thus, most approaches focus
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4
on the physical output. Given the challenges that 1) computer activities are usually
seated and 2) that the transfer from real-world problems to the computer is often ne-
glected [27], it might be useful to extend the concept of Physical Computing. Physical
Computing in an educational context should incorporate physical activities to 1) enable
the problem identification in the real world and 2) to promote healthy activities and
exercise. The “physical” perspective can be seen in different ways:
• Physical Input: The problem to be solved in a learning activity is observed and
experienced in the real world. This means that children should physically go to the
place where the problem can be seen.
• Physical Transfer: Every computer activity should be connected with physical real-
life activities to avoid that computers are just perceived as a passive activity. The
problem identified in the real world should then be transferred to a computer activity
in which the solution is elaborated.
• Physical Output: Devices such as small robots are used to create haptic, tangible
experiences as the outcome of the computer activity.
By extending the concept of physical computing, we address the above mentioned
challenges and barriers to create more meaningful, active learning experiences.
3 Computational Thinking and Acting
3.1 Methodology
There is no common methodology for the development of competency frameworks
/ models or curricula due to the variety of disciplines involved in this field. Typically,
competence models are built using deductive approaches (e.g. analysis of existing mod-
els) or expert consultations, often in professional communities such as AIS / ACM (e.g.
[34]). Further methods include job advert analysis [35] or design based approaches [36].
We follow a Design Science Research method aiming at creating artefacts in a rigorous
way [37]. We see the main outcomes of our research (Learning / Teaching Model in-
corporating physical computing paradigm, associated learning scenarios) as meta-arte-
facts [38] as well as a method [39] for further system design – resulting systems could
be Human Resource Management systems as well as E-Learning courses as corre-
sponding interventions and IT-artefacts.
As guiding theories, we use the competence-based view of the firm [40] which ex-
plains the relation of individual competencies to the performance of firms. However,
as there is a time-lag of competence development (e.g. in school or university) and their
usage (in a later employment). The second guiding theory is the Human Capital Theory
which explains employability and competencies [41]. Especially the aspect of employ-
ability [42] needs to be extended regarding time and the specific impact of CT within
the learning biographies of people. This aspect is not addressed in this paper but it is an
overall goal of our research.
In our paper, we focus on the development of the meta-artefacts but also reflect our
results towards contributing to theory building [43]. As a contribution towards theory
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5
building, we contribute towards the identification of new competencies and related ac-
tivities as part of Computational Thinking. Regarding the practical contribution, we aim
at providing useful learning scenarios for teachers to be used across curricula and sub-
jects. The construction was based on a literature review and the main concept matrix
[44]. Overall, we used a naturalistic multi-method evaluation [45]. The initial version
of our artefacts was evaluated with 41 persons discussing the importance and target
group fit of each category. The version described in the next chapter represents the
result of this process.
3.2 Computational Thinking and Acting Framework
The key concept “Computational Thinking and Acting (COTA)” combines Compu-
tational Thinking and our extended understanding of Physical Computing. Computa-
tional Thinking and Acting (COTA) is defined as
the ability to understand and utilize information
and communication technologies and their key
concepts, methods and tools by identifying and
solving real world problems accompanied by
physical activities and experiences. The definition
describes the scope and specific characteristics of
COTA. To further refine the concept, it is neces-
sary to clarify competencies included. Our con-
cept is reflected in the COTA curriculum. It co-
vers seven main categories derived from the com-
petency / curriculum analysis.
Fig. 1. COTA Currriculum Categories
In contrast to previous curricula (see 2.1), we include 1) Attitudes and dispositions con-
taining emotional aspects towards computers and computing. This also contains aspects
focusing on the physical perspective, i.e., computer activities should not be seen as
solely seated indoor-activities. Furthermore, positive attitudes towards ICT as problem
solving tools are intended. 2) Disruptive Technologies focus on specific concepts and
technologies which (will) change practices in educational, private and professional life.
A typical example is Artificial Intelligence which will change the ways problems are
solved in business and leisure. Summarizing our approach, we see that Computational
Thinking curricula will be highly dynamic and change frequently. However, the core
curriculum needs to be mapped to and integrated into other subjects.
3.3 Pedagogical Approach and Learning Scenarios
As suggested by Kong [46], a curriculum needs to be extended describing clear ac-
tivities and scenarios to be implemented in practice. There are few frameworks for ped-
agogical / didactical aspects of CT. Chande [47] proposes the following phases: pro-
duction, recontextualization & problem solving, and reproduction & innovation. The
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6
model, however, is too prescriptive for our context. Kotsopoulos et al [48] propose four
main types of learning activities: unplugged, tinkering, making and remixing. This
model is well suited for physical activities but too strict for teaching CT across subjects.
Further models apply well established didactical concepts such as Problem-Based
Learning [49], Project-Based Learning [50] or Inquiry Based Learning [51].
Based on the review of existing models and our key concepts, we propose the fol-
lowing phases: 1) Context setting: In each scenario, the context needs to be clarified -
in particular in cross-subject activities, the overall idea should be clarified. 2) Problem
exploration and identification: A real world scenario should be explored by the learn-
ers - facilitated by a teacher, this should include a physical activity. 3) Transfer and
Elaboration: In this phase, the problem is transferred to a computer activity. Depend-
ing on the type of the problem, different competencies are included. 4) Production: In
this phase, the developed solution should be visualized / prototyped in the real world.
If possible, physical outputs should be produced. 5) Reflection: In the final phase, the
experience is reflected amongst participants. The phases are not necessarily sequential
but can be parallel or repeated multiple times. Secondly, we derived principles for the
creation and implementation of learning scenarios. Each learning scenario should be:
• Real World Problem-Oriented: Learners should experience and identify problems
from the real world. They should be allowed to explore and create own ideas for
problem solving / creating projects.
• Learner-Centric: Learners should be engaged and empowered to identify solution
strategies. The teachers should act as facilitators to moderate the learning process
(e.g. scaffolding)
• Cross-subject: Learning scenarios should not be restricted to computer science /
ICT. Projects across subjects and disciplines should be created to provide richer
learning experiences.
• Physical: each learning scenario should incorporate physical activities as input and
output.
• Transferable: Each solution should be reflected to understand the transfer to other
problems / subjects / domains.
Along those principles, initial learning scenarios have been designed for both teach-
ers and students. To bring curricula into the classroom, it is necessary to provide con-
crete learning scenarios related to the competencies [46]. The following scenario is a
short sample and outlines the main ideas of COTA.
Table 1. Sample Learning Scenario
Scenario Decomposition
Title
Main This learning scenario introduces algorithmic descriptions of problem
ideas and solutions as well as decomposition. The problem in this case is how to
descrip- estimate the rate of damaged / ill trees in a wide area. The scenario is
tion related to the biology and maths curriculum.
Context Primary school from grade 5
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Curricula Geography: Use of maps, scale
Biology: Forests and trees, tree diseases, nature protection
Mathematics: Surfaces, scales, units
Compe- Students can 1 Divide a problem into smaller sub-problems, 2) Use step-
tencies by-step instruction to describe a solution, 3) Use variables for calcula-
tions, 4) Use conditions within loops
Peda- Explorative learning
gogy
Learning Activities
LA1 The teacher introduces problems which cannot be solved as a whole –
Context examples are counting all animals in an area, sorting large amounts of
things. Students get the task to go out to a close-by forest. The question
is asked whether they can count all trees within one lesson.
Additionally, tree diseases are introduced. What kind of diseases exist
and how can they be observed (e.g. parasites such as birch moth, bark
beetle; acid rain, …). This introduction needs to be modified depending
on the geographical area.
LA2: Students get the task to estimate the number of trees in a given forest.
Explora- They g out to the area to get a visual impression of the problem. First,
tion they calculate the surface of the area. This can be done using a map and
estimating the total size. After this, students determine an adequate sam-
ple size to decompose the problem (a realistic sample is 50m*50m).
The students split up in groups and distribute tasks (one person for
measuring the length / width of the area, one person to count all trees,
some persons to find and count damaged trees). Students measure the
step length to calculate how many steps equal 50m. The worksheet pro-
vides step-by-step tasks to solve the problem.
LA3: Students go into the woods and find a place from where they can walk
Elabora- down the sample (50 by 50 metres). The corner points are marked (or a
tion student stops there). A counter should count the number of steps (e.g.
REPEAT walk_one_step UNTIL counter= number_of_steps)
When the square is marked, students start counting the number of trees
and agree on the number in case of differences. Afterwards students try
to count damaged trees (sick by parasites, breakage etc). Finally, the
students calculate the total number of trees and the rate of damaged
trees.
LA4: Finally the whole algorithm in the four solution steps (calculate area,
Reflec- walk / mark area, count trees, calculate trees) should be written down.
tion Students also can discuss in which other situations they could decom-
pose to find a quicker solutions.
Roles Students , Teacher / additional person for field trip
Tools Work sheet: Estimating trees
and Ma- Pen and paper (marking types of trees, conditions, amount
terials Area map
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8
The following figure shows sample tasks from the related guiding worksheets.
How many trees are in the forest below? How many are damaged?
Scale: 1:10000
How big ist he surface, use variables to calculate.
length_map = ____________
width_map = ____________
surface_map = ________ * ____________ = ___________
How long / side is the area in real?
length_real = __________
width_real = __________
surface_real = ________ * ____________ = ___________ m
Task: Go to the forest and mark a 50m * 50m square.
Step_length = __________ cm = _________ m
Number_of_steps = 50m / Step_length =
How many trees have you counted? How many are damaged
Counted_number_of_trees= ____________
Counted_damaged_trees = _____________
Now you can count the number of trees in the whole area. How many squares do you
need for the full area?
Our_square * ____X_____ = real_square; X = _____________
Overall_number_of_trees = ___________ * Counted_number_of_trees =
Overall_number_of_damaged_tree = ___ * Counted_number_of_damaged_trees =
Great, you have helped us a lot to understand the situation in the forest!
Task: Summarize all steps you went through to measure the number of damaged tress.
Use variables, conditions and loops, e.g.
IF step_counter == 100, THEN turn right by 90 degrees
IF tree = damaged, THEN counted_damaged_trees++1
REPEAT go_one_step UNTIL step_counter == Number_of_steps
Fig. 2. Sample work sheet
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9
The scenario is just one example which was designed together with teacher trainers.
It shows the key characteristics of the COTA approach:
• Problem identification in the real world: The physical input (forest excursion, deter-
mining damaged plants) and corresponding problem is discovered in the real world
without computer usage.
• Transferring the problem towards a CT solution: Different steps and task guide stu-
dents to the algorithmic description of the solution
• Using CT solutions, in particular pseudo code including object manipulation, condi-
tions, loops.
• Receiving tangible results such as photos and documentation of the solution.
The proposed solution was tested with a small group of students and improved based
on the students feedback. Then, a full evaluation of the approach was carried out.
4 Evaluation
To further understand teachers’ views on computational thinking and physical learning
scenarios, we interviewed individual experts and focus groups in Estonia (E), Finland
(F), Germany (DE) and Greece (GR). We involved 41 individuals: teachers (n2=30),
headmasters (n=5), university lecturers (n=4), teacher training specialist (n=1) and ed-
ucational technologist (n=1). Out of the teachers, 21 worked on primary level, 4 on
secondary and 7 on both.
The first part of the evaluation looked at physical computing as a pedagogical ap-
proach. The following solutions were discussed by the interviewees.
Fig. 3. Physical Computing elements
Real-life problems: Regarding the physical aspects, the most discussed issue was
the transfer of problems from the real world to computer solutions which was seen as
the key to success (F8, F9, E1, E2, E3, E7, GR2, GR9). “If students solve real-life
problems, they are more interested, motivated and focused on finding solutions.” (Es-
tonian primary school teacher, age 63)
Robots: The use of robots is also seen positively as it is already current practice in
many schools, especially in Finland, Greece and Estonia (F1, F2, F3, F4, F5, F8, F9,
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10
E3, E4, E6, E9, E10, GR4). “Students understand deeply physics and mathematics due
to robotics lessons.” (Greek primary school teacher, age 43)
Microcontrollers: Arduino and MicroBit controllers were popular in Finland and
Greece (F3, F4, F5, E10, GR1, GR5). “I used a couple of years ago the breadboard of
Arduino and for myself it was a fruitful experience because I got to know more about
physics. In addition, I realized that students were motivated, excited and understood
more easily due to physical computing.” (Greek primary school teacher, age 36)
Collaborative and Hands-on activities: Collaborative and “hands-on” activities
were described by most interviewees as liked and most engaging (F1, F2, F3, F6, F8,
E3). Physical computing has been used in making art and in games. “Hands on group
work seems to offer the easiest way to learn computational thinking.” (Finnish primary
school teacher, age 43)
Competitions: Different kinds of playful competitions were also mentioned several
times (F2, F9, E4, E10). Beaty contests or dance competitions of the lego-robots were
used. “We used to do only the ‘Hour of code’, but there’s hardly anything that really
grasps and motivates the students. Then again, giving them a task (sumo wrestling lego
robots) that includes programming, they can be motivated. Of course they concentrate
on the looks of the robot, the flag it carries and everything else ‘unimportant’ that
doesn’t have anything to do with the ICT goal. But when they are motivated, they will
do work at home and they will compare their work with other students. After the first
sumo championship games, the students wanted to have a couple of weeks to modify
their robots and their programming and have a rematch. You don’t hear that when the
kids are just coding with the computer.” (Finnish primary school teacher, age 46)
Overall, the idea of combining physical and computer activities was seen very posi-
tively to also change attitudes towards computer work.
The second part of the evaluation looked at the Competency Framework. Most
teachers recognised potential to achieve the competencies within the COTA framework
(Fig. 1). More specifically, The German teachers ranked different competences differ-
ently, the most important were: 1) Problem solving, 2) Algorithmic thinking, 3) Digital
/ media literacy, 4) Utilizing programs for problem solving, 5) ICT as tools for learning.
The view of the experts was rather different on the competencies of programming in a
specific language. While one expert found this a regular competency “(Scratch) Pro-
gramming is regularly taught from grade 2-3” (GR1), another expert clearly said that
“I don’t think that in primary school students learn programming.” (GR7) The category
of debugging was also controversial. As a consequence, we will continue to revise the
curriculum in further trials with school children and teachers. Overall, there was con-
sensus that CT is necessary in this age group or as one expert said: “It is the best age
to learn the basics”. Overall, the curriculum was perceived very positively as a starting
point for profile building for different requirements (in each country).
As a final aspect, we asked for support needs towards enabling teaching of compu-
tational thinking and physical computing. Key findings are quite obvious on resources
(time, funding, learning resources, competences, training) but also the teachers hope
they could have hands-on playing and testing time before introducing physical compu-
ting activities in their teaching. There are some differences between the European coun-
tries, but they all agree on the importance of increasing teachers’ competences and
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11
providing learning resources ready, easy-to-use, easy-to-find, age appropriately availa-
ble. Finnish teachers are not currently worried about schools' infrastructure. Their need
is more towards teachers’ time. Chart X shows the country differences in teachers’
needs to see whether our approach of providing Open Educational Resources (OER)
and Practices (OEP) is feasible. The following figure shows the teachers’ requests.
Fig. 4. Support needs of teachers
Needs for competences through training, guidance and support: Most popular
answer in this interview was that teachers agreed that they need guidance, training and
support to increase their competences. In Finland, teachers’ digital peer-mentoring pro-
jects have been well received and have provided a systematic way for teachers to learn
from each other. Teachers also need “Clear and simple goals to achieve to avoid over-
whelming them from the start.”(Finnish Maths teacher, Age 42). A lot of the answers
were focusing on teachers’ attitudes and motivation rather than technical skills in using
robots or computers. It was emphasised that the support mechanisms should make
teachers excited (German Primary Schoo teacher, Age 32) and get the teachers moti-
vated (Finnish Principal, age 44 and Greek Primary School teacher 39). Teachers
should be thought to have an open mind. (Estonian Seconary level teacher, age 45)
Needs for easy-to-use ready-made learning resources: Second most popular need
focused on learning resources in the forms of online resources as well as conventional
books and worksheets (German Headmaster, Age 50). The resources should be de-
signed to be age-appropriate and some mentioned playful, gamified elements. “Learn-
ing materials should be age appropriate. Playful materials for primary school and more
complex materials for the older kids.” (Estonian IT Specialist 51, Estonian secondary
level teacher, 60)
The following list shows the requirements which will be applied in the next steps
when designing further learning materials which should be: easy to use; easy to find;
age appropriate; simple goals clearly defined; structured; offline worksheets for those
who need them; playful, gamified materials.
Overall, the results of the evaluation have shown that the approach is useful for both
teachers and experts. As a next step, the COTA approach will be tried out in experi-
ments in schools with teachers and pupils.
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12
5 Conclusion
In this paper, we have outlined the concept of Computational Thinking and Acting
(COTA) which combines the concepts of Computational Thinking and Physical Com-
puting. The concept aims at providing new learning and teaching experiences with a
focus on contributing to competency development in the field of Computer Science as
well as future employability. We developed meta-artefacts as well as a method to im-
plement the competency framework, i.e., design principles and learning scenarios. The
initial evaluation has shown that our competency framework covers the most important
aspects of computer science and is feasible for schools and cross-subject teaching.
As the next steps, learning scenarios will be evaluated in different schools and sub-
jects. We aim at better understanding the impact of CT competencies and improve the
design guidelines and learning scenarios. In the long-term, it will be of high importance
to understand the relation of CT and employability over time.
6 Acknowledgement
This research has been co-funded by the European Commission within Erasmus+
programme, project COTA, grant no. 2019-FI01-KA203-060877.
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