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Computational Thinking is argued to be an essential skill for the workforce of the 21st century. As a skill, Computational Thinking should be taught in all schools, employing computational ideas integrated into other disciplines. Up until now, questions about how Computational Thinking can be effectively taught have been underexplored preventing efforts to cross the large gap between early adopters and the early majority, conceptualized as the Computer Science Education chasm. A promising strategy to cross the chasm is underway in Switzerland. Switzerland recently introduced a national curriculum, called Lehrplan 21, mandating Computer Science Education. This mandate requires the Computer Science education of elementary and middle school students. In 2017, the School of Education of Northwestern Switzerland (PH FHNW), introduced a mandatory pre-service teacher Computer Science Education course, to satisfy this mandate. All the PH FHNW students who study to become elementary school teachers must pass this two-semester course. The first part of this course was taught for the first time in fall of 2017. This paper presents the philosophy of this course and an initial analysis of both qualitative data capturing the students’ perceptions of Computational Thinking and quantitative data describing shifts in students’ skills and attitudes as effect sizes. The data suggest that it is possible to teach a basic understanding of programming to non-self-selected pre-service elementary school teachers.
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Teaching How to Teach Computational Thinking
Anna Lamprou
School of Education
Northwestern Switzerland (PH FHNW)
Windisch, Switzerland
anna.lamprou@fhnw.ch
Alexander Repenning
School of Education
Northwestern Switzerland (PH FHNW)
Windisch, Switzerland
alexander.repenning@fhnw.ch
ABSTRACT
Computational Thinking1 is argued to be an essential skill for the
workforce of the 21st century. As a skill, Computational
Thinking should be taught in all schools, employing
computational ideas integrated into other disciplines. Up until
now, questions about how Computational Thinking can be
effectively taught have been underexplored preventing efforts to
cross the large gap between early adopters and the early
majority, conceptualized as the Computer Science Education
chasm. A promising strategy to cross the chasm is underway in
Switzerland. Switzerland recently introduced a national
curriculum, called Lehrplan 21, mandating Computer Science
Education. This mandate requires the Computer Science
education of elementary and middle school students. In 2017, the
School of Education of Northwestern Switzerland (PH FHNW),
introduced a mandatory pre-service teacher Computer Science
Education course, to satisfy this mandate. All the PH FHNW
students who study to become elementary school teachers must
pass this two-semester course. The first part of this course was
taught for the first time in fall of 2017. This paper presents the
philosophy of this course and an initial analysis of both
qualitative data capturing the students’ perceptions of
Computational Thinking and quantitative data describing shifts
in students’ skills and attitudes as effect sizes. The data suggest
that it is possible to teach a basic understanding of programming
to non-self-selected pre-service elementary school teachers.
CCS CONCEPTS
Social and professional topics Computational
thinking Social and professional topics Computer
science education • Social and professional topics K-12
education
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from Permissions@acm.org. ITiCSE '18, Jul y 24, 2018, Larnac a, Cyprus
© 2018 Association for Computing Machinery.
ACM ISBN 978-1-4503-5707-4/18/07…$15.00
https://doi.org/10.1145/3197091.3197120
KEYWORDS
Computer Science Education, Computational Thinking, Pre-
service teacher, K-12 education, Primary school
ACM Reference format:
Anna Lamprou and Alexander Repenning. 2018. Teaching How
to Teach Computational Thinking. In Proceedings of 23rd Annual
ACM Conference on Innovation and Technology in Computer
Science Education (ITiCSE’18). ACM, New York, NY, USA, 6
pages. https://doi.org/10.1145/3197091.3197120
1 INTRODUCTION
Computational Thinking (CT) is described as a key skill for the
workforce of the 21st century [1]. Wing also argues, that CT is a
basic skill for all humans, not just computer scientists [1].
Following from that, CT should be taught everywhere, especially
at school level. Since its conception by Papert [22] and its
broader introduction by Wing [1], CT has been the subject of
intense and long discussions within the Computer Science
Education (CSEd) community. In the center of attention are
questions about CT’s importance and definition but also about
how this important skill can be effectively taught [2, 3].
Questions surrounding CT’s definition and importance even
though far from resolved, have been intensively discussed to
offer a consensus about its importance [28] and a basic
framework for a common definition. However, questions about
how CT can effectively be taught remain largely underexplored.
This is mainly because CT cannot be taught in a traditional
manner and has to overcome both a pedagogical and a systemic
challenge.
With regards to the pedagogical challenge, previous research
has shown that even though CT has close connections to
programming the first cannot be automatically learned through
teaching the latter. Duncan summarized a large pilot study with
primary school students in New Zealand with “We had hoped
that Computational Thinking skills would be taught indirectly
by teaching programming and other topics in computing, but
from our initial observations this may not be the case” [4]
Indeed, CT is more than just programming and teaching such a
skill may require the use of specific tools, so called
Computational Thinking (CT) Tools [16], that can assist the CT
process, without the introduction of complicated and difficult
programming. With regards to the systemic challenge, the
question is about how, with Computer Science (CS) being up
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Anna Lamprou and Alexander Repenning
until recently mainly an extracurricular activity, taken up by
interested in the subject (self-selected) teachers and students, we
can cross the chasm [5] of CSEd: shifting from self-selected
teachers and students to ALL teachers and students.
Switzerland, a highly affluent, but in terms of K-12 CSEd
somewhat conservative country, has made a bold movement
which may lead to the successful crossing of the CSEd chasm
(Fig. 1). With the introduction of Lehrplan 21, the new common
curriculum for compulsory education in the 21 German-speaking
states, CS will be introduced into the Swiss elementary schools
starting from the first grade. In anticipation of the changes
imposed by the new educational framework, Switzerland is
radically shifting its strategy by introducing mandatory pre-
service teacher CSEd starting at the elementary school level. The
term pre-service teacher refers to the undergraduate students
who study to become primary level teachers. Following from
that, since September of 2017, the School of Education of
Northwestern Switzerland (PH FHNW), requires its students
(pre-service teachers), to take a mandatory CSEd course in order
to be able to graduate and teach. In order to find out how
effective the course was we conducted a study collecting both
qualitative and quantitative data from more than 600 students
that took the course. This paper presents the philosophy of the
course and discusses initial findings from the study. Our results
show that even though pre-service teachers can easily learn
basic programming, the question about learning CT still remains
open.
2 RELATED WORK
There has been a long definition debate about CT. Jeannette
Wing defines CT as the thought process involved in formulating
a problem and expressing its solution(s) in such a way that a
computer—human or machinecan effectively carry out [6] (Fig.
2). Despite the fact th at many agree with this definition, to this
day there is no unanimous agreement on what CT exactly is [7].
In 2012, Aho defined CT as the thinking process of developing
solutions for problems using algorithms and computational steps
[3]. The same year, the Royal Society defined CT as the process
of recognition of “aspects of computation in the world that
surrounds us” and the application of “tools and techniques from
Computer Science to understand and reason about both natural
and artificial systems and processes” (p.29) [8]. In the context of
K-12 CT education, a similar definition was provided by Barr and
Stepheson summarizing that “CT is an approach to solving
problems in a way that can be implemented with a computer… .
It is a problem-solving methodology that can be automated and
transferred and applied across subjects” (p. 115) [9]. Denning,
made a distinction between traditional and new CT and argued
that to this day the CSEd community is struggling to answer
three questions: What is CT? How can it be assessed? And is it
good for everyone [2]?
Despite the ongoing debates surrounding CT’s meaning,
importance and way of teaching, commonly used definitions are
gradually emerging and there is broad acknowledgement that
our global economy is based and shaped by computing. There is
enough literature about teaching and learning programming and
CS but mostly for the undergraduate college level. In terms of
teaching CT in K-12, programming does not necessarily translate
in teaching CT [4]. Despite many countries introducing CSEd in
their K-12 curricula, such as: UK’s Computing at School
movement [24], the partnership of the University of Adelaide
with Google to successfully implement Australia’s Digital
Technologies Curriculum [25], and the CS for All mandate in the
US [26], and other initiatives aiming at introducing CS into
schools, such as the CS4HS and Computing in the Core, CSEd
has not been systematically introduced to K-12 education [7]
[27]. The crossing of the CSEd Chasm requires not only finding a
place for CSEd into K-12 curricula, but also finding a place for
CS in pre-service teachers’ educational curricula. Currently most
efforts are focused in providing professional development of in-
service teachers [10]. With the exception of pilot projects
involving selected pre-service teachers’ CS training [11, 31],
there is not much literature about CS pre-service teacher CSEd
[19]. Even though the need to educate pre-service teachers has
been previously addressed in literature [29, 30], most research
involving pre-service teachers and CS is focusing in questions
about attitudes towards the use of computers [12]. A momentum
crucial for the future of CSEd is building up in Switzerland,
which addresses both requirements. The new Lehrplan 21
introduces CSEd to the primary school level by way of the
module “Medien und Informatik.” The important next step is to
find ways to successfully implement the module descriptions of
Lehrplan 21 in practice and bring CSEd to primary schools in
Switzerland.
To meet the Lehrplan 21 requirements, the school of
education of northwestern Switzerland (PH FHNW) offers since
September 2017 an obligatory course in CSEd for its students:
pre-service teachers. We conducted research to investigate this
underexplored but very important area in order to fill in the
literature gap, contribute to the development of CSEd and inform
attempts of crossing the CSEd chasm.
3 APPROACH
To illustrate the fundamental challenges for a strategy to
cross the CSEd chasm, a brief history of about 30 years of
research may help.
3.1 Crossing the CSEd Chasm
“Hard and boring” two words describing CS, especially used by
young girls [13], became a strategic research map exploring the
challenges of CSEd. Over the many years, this work revealed
three distinct research stages relevant to crossing the CSEd
chasm (Fig. 1).
Stage I: The “Friday Afternoon Computer Club” Stage (Self-
Selected Students / Self-Selected Teachers): The first stage
focused on creating more accessible programming support tools
(AgentSheets) and more engaging creativity support tools for a
Friday afternoon computer club [14].
Stage II: The “Professional Development Movement” Stage
(All Students / Self-Selected Teachers): The transition from
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stage I to stage II required a shift in focus from tools to curricula.
Scalable Game Design (SGD), became a curriculum for teaching
CT [15], wh ile the 3D browser-based AgentCubes online CT tool
was developed [16].
Stage III: The “Mandatory Pre-Service Professional
Development” Stage (All Students / All Teachers): Through
networks of excited CS teachers, compelling curricula such as
SGD, can spread surprisingly quickly, but one cannot rely to
such networks to persuade more conservative teachers to
become part of the CSEd revolution, and cross the CSEd chasm.
If CT is considered to be an essential 21st century skill, then the
in-service teacher professional development must transform to
mandatory pre-service teacher education. This shift is now, since
September 2017, a reality for the PH FHNW, with the
introduction of the obligatory SGD Course.
Figure 1: Crossing the Computer Science Education Chasm
with mandatory pre-service teacher education
3.2 Scalable Game Design Course Philosophy
The Scalable Game Design (SGD) course is new and unique in
Switzerland. It addresses all undergraduate students (pre-service
primary teachers) of the PH FHNW, resulting in numerous
challenges but also great opportunities. Most challenging for this
course is the extraordinary heterogeneity regarding the
background knowledge and interest of students. This course has
to cope with a wide range of interests, ranging from intensive CS
enthusiasm to complete CS apathy. In contrast to other subject
areas such as mathematics, the course cannot assume that
students have even basic CS knowledge from their previous
education.
The goal of the SGD course is for pre-service teachers to
become computational thinkers themselves and to be able to
help their students become computational thinkers. The course
does not require any prior knowledge in CS. Through the course
the students are acquiring basic programming skills and CS
knowledge, but also, they understand what constitutes creative
processes and how these can be stimulated sustainably.
The definition of CT used for building this course was
heavily influenced by the particularities and needs of the
primary school teachers and their future students. In
Switzerland, primary level teachers teach all subjects to their
students who can be as young as 6 years old. An appropriate CT
definition should address and respond to this reality. To that
end, and for the purposes of this course, CT is a combination of
mathematical -analytical thinking with natural sciences,
engineering, and other disciplines. CT is conceptualized as
thinking with the computer (Denken mit dem Computer in
German) joining human abilities with computer affordances (Fig.
2). It is regarded and employed as a way of thinking that uses the
computer as an instrument to support the human thought
process. Based on Wing’s [1] definition the CT process can be
segmented into three stages:
1. Abstraction: problem formulation; 2. Automation:
representation of a solution; 3. Analysis: execution and
evaluation of the solution representation (Fig.2).
Figure 2: e AAA model: a three-stage process describing
Computational inking
The course was conceptualized upon three pillars to combine CT
with the Swiss Lehrplan 21.
1) Motivation and Learning Strategy: Scalable Game Design.
Motivation is at the center of this course. The SGD pedagogy is
based on the Zones of Proximal Flow theory and describes the
creation of games and simulations and their continuous
adaptation to new ideas [15, 23]. SGD starts with a project first
approach and has a uniquely low threshold, so students can
begin with easy activities (as described by Papert [17]), and a
high ceiling so students can advance towards the creation of
highly sophisticated games and simulations that model social
and scientific phenomena in STEM. Through a set of
increasingly sophisticated game and simulation projects the
students’ programming and computational thinking skills scale
up gradually.
2) Tools designed specifically to teach and support CT at the
elementary school level: Computational Thinking Tools. CT
is not the same thing as programming. Unlike CT, each
programming language is characterized by its own syntax, which
is not as important as the conceptual understanding of general
programming concepts. CT Tools are educational programming
environments that make the teaching of CT practical on every
school level. Good CT Tools address the two fundamental
challenges of CS being perceived as difficult and boring [13] by
having two key aspects. The first key aspect of CT Tools is that
they minimize avoidable complexity and thus address syntactic,
semantic and pragmatic challenges. The second aspect of CT
Tools is that they support creativity at a high level [14]. By
supporting all three stages of the CT process, minimizing
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Anna Lamprou and Alexander Repenning
accidental complexity, and including elements that support the
creative process, CT Tools render CT and programming
accessible and exciting [18].
3) The 7 big ideas of Computer Science. The mapping of the
SGD strategy onto the computer science part of the Lehrplan 21
was straightforward. The three main Lehrplan 21 CS topics (data,
algorithms, and systems) were identified as a subset of the 7 big
ideas found in the AP Computer Science Principles (CSP)
framework: creativity, abstraction, data, algorithms,
programming, the Internet and global impact [20].
The SGD course covered 14 weeks of classes (two-hour
sessions per week). Each week’s session was divided into two
parts: a theory part and an activity part. The theory part
provided knowledge and competencies regarding one of the
seven CSP ideas. Each CSP idea was mapped onto a two-week
block so that different aspects of each of the seven CSP ideas
could be covered and its relevance to the professional function of
primary school teachers could be demonstrated. For the activity
part the students focused on creating their own projects using
CT Tools. Students learned to program through a project first
approach, supported by scaffolding methods and using mainly
the CT Tool AgentCubes [18]. After being introduced to
AgentCubes in their first session, the students started using it to
program immediately. Initially they created simple Frogger like
games using step-by-step instructions, but as the course
progressed they gradually moved on to program more advanced
games and STEM simulations while experimenting without the
use of instructions. Other tools, such as Scratch and Processing
were also introduced. In the second half of the course the
activity part became even more self-guided since the students
were working on their own final projects, which they had to
develop in groups using CT Tools or object-oriented
programming languages. Through this hands-on work using CT
Tools, the students were able to learn how to write easy
programs and understand how to think computationally. The
instructors of the course had a combined background in
computing and education. programs and understand how to
think computationally.
4 METHODS
Since the SGD course is the first of its kind, the teaching was
accompanied by a research project, which documented and
collected data. The course was taken by approximately 650
students (pre-service teachers) mostly from the first and third
semester. On September 17, 2017, in 4 states, 7 instructors began
to teach 26 CS courses with approximately 25 pre-service
teachers each. During this time a variety of methods from
questionnaires to in-depth interviews were used to collect data.
During the first semester of the course, we ran three
questionnaires: one at the beginning, one in the middle and one
at the end of the course. The questionnaires were consisted by
background, self-efficacy and CS knowledge/skills questions and
used a five-point Likert scale (1 = strongly disagree --- 5 =
strongly agree). The middle questionnaire was focused more on
the assessment of the tools used in the class, while the end
questionnaire included a course evaluation part. In order to
assess the skills and knowledge gained by the students during
the course, we repeated a number of questions from the initial
questionnaire in the final and calculated the effect sizes for these
questions.
Additionally, for the purposes of teaching more than 650
students and coordinate seven instructors, the teaching team
used a teaching/learning platform where each instructor could
communicate with the students and the other instructors online.
The platform contained the teaching material and homework
assignments while it gave the possibility to develop wikis,
groups, discussion forums etc. This gave us access to a plethora
of qualitative data from projects to homework answers etc., and
the ability to ask specific open-ended questions.
5 RESULTS
In the following sections we present in detail some of the
findings from both the qualitative and quantitative data
collected.
5.1 antitative Data
We present here the effect size, calculated using the Cohen’s
d formula [21], of one question with a particular interest to the
teaching of CSEd. We asked the students to rate their ability to
program in both the beginning and the end of the course. We
received 539 and 471 answers respectively. In the beginning of
the course 77.9% of the students rated their ability to program as
practically non-existent (1 in Likert scale). This number drops to
5.3% at the end of the course. Cohen’s d for this particular
question was calculated to 2.05 which indicates a large effect
size. The figure below displays the percentages of the answers
before and after the course (Fig. 3).
Figure 3 Percentages of the answer to the question: I know
how to program, before and after the SGD course.
5.2 Qualitative Data
Given the central role of CT and the challenges connected to its
definition and teaching, in October, after about one and a half
months into the course we asked the students to answer the
open-ended question: What is CT for you? We received 447
replies which we grouped according to seven major
categories/themes. The themes were developed based on the
answers given, while the responses were categorized as correct
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ITiCSE’18, July 24, 2018, Larnaca, Cyprus
Table 1: Results from the analysis of the open-ended
question: What is CT for you? (N= 447)
Category &
Description
Examples of Responses %
a) Thinking like a
computer:
Answers suggesting
that the computer is a
thinking entity
“For me, computational thinking means
understanding how a computer thinks,
how to prog ram it, how to abstract and
simplify a problem, how to design
solution str ategies.”
“For me, "Computational Thinking" is
how yo u can think like a co mputer. That
you can think abstractly (way of
thinking like a PC)”
21%
b) Programming
CS/ Computer
work:
Answers strongly
connecting CT to the
ability to program or
to apply computer
science principles.
“Find solutions to a problem. Especially
in the field of programming.”
“I think Computational Thinking has
something to do with how the computer
works.”
“Computational thinking is for me to
deal with the world of computer science
and learn more about the methods and
models. Basically, it should help us to
cope with our everyday problems
easier.”
16.1%
c) Problem division:
Answers identifying
CT as a process that
involves breaking a
problem into smaller
less complicated
parts.
“Computational thinking is for me a
"system" for solving problems. It is
divided into smaller steps to find an
efficient solution. The problems are
abstracted and suitable algorithms are
searched for, so that the problem can be
solved as quickly as possible.”
“Identify a problem, break it into sub-
steps and formulate a solution str ategy.”
14.8%
d) Thinking with
the Computer:
Answers suggesting
synergistic
integration of human
abilities w ith
computer affordances
“Problem solving with the help of the
computer. The keywords abstraction,
automation and analysis come right to
my mind”
“Solution-oriented thinking and acting
with computer support”
14.3%
e) Problem Solving:
Answers identifying
CT as being a
problem-solving
process, without
much specif ication
“Computational thinking is for me the
individual mindset of a person to
answer or solve different, more or less
complex, q uestion s, problems and
tasks.”
“For me, computational thinking means
simply presenting a problem (abstractly)
and thus finding a solution.”
13.4%
f) CT Process/ AAA:
Answers describing
the CT process along
the lines of the CT
definition that was
taught in the class
“To formulate a problem in that way so
it can be solved by both a human and a
computer.”
“Interaction between human and
computer
1. problem formulation (abstraction)
2. Representation of a solution
(automation)
3. Execution & E valuation of the
Solution (Analysis)
11.4%
g) Other:
Answers not
matching any of the
other categories
“Logical systematic thinking that only
results in wrong or correct
“I do not remember exactly what the
term "computational thinking" covers.
When I hear it like this, it comes to my
mind spontaneously that it is a pro cess
that is e volving.”
9%
or wrong based on the definition of CT on which the course was
based on. Table 1 presents the results of the analysis while it
displays examples from answers from each theme.
25.7% (115 answers) of the students gave an answer that is
along the lines of the CT definition that was taught in the class.
From this, 14.3% (64 answers) were answers that identified CT as
“thinking with the computer” (denken mit dem Computer) while
11.4% (51 answers) described a process along the lines of Wing’s
definition or the AAA. process (Fig.2). 14.8% (66 answers)
described CT as a process of dividing a problem to smaller easier
parts in order to solve it, while 13.4% (60 answers) referred to CT
as a problem-solving process. Finally, 9% (40 answers) were
classified as “other” clustering the remaining responses (Fig. 4).
Figure 4 Categorized students’ answers to the question
“What is CT for you?”
6 DISCUSSION
The large effect size for the perception of programming skills
suggests that the students (pre-service teachers) think that they
successfully learned how to program but it is less clear what
they actually learnt with respect to CT. The disparity between
teaching programming and testing CT is consistent with
Duncan’s findings [4]. Even though the correctness of some of
these answers is debatable we are only considering categories c,
d, and f to be “right” in the sense that they were the intended
learning outcomes of the course. Looking through this lens, only
40.5 % of the answers correct. However, the large percentage of
false answers (59.5 %) does appear to suggest either instructional
difficulties or tenacious misconceptions that are hard to change.
The following discussion interprets some of the findings.
a) Thinking like a Computer (21%): We found this response
surprising as the course tried to explicitly stress the notion
of computational thinking as a synergistic combination of
human abilities with computer affordances (Fig. 2 center).
Do computers really think? Should people actually think the
way computers operate, and, if so, at what level, e.g.,
machine language, would this kind of thinking take place?
b) Programming/CS/Computer work (16.1%): This category
was perhaps too broad. It suggests that CT is an activity
connected to programming, or to the application of CS
principles. While this is true, it is not clear how inclusive
these kinds of statements are. Is any form of programming
or application of CS principles always necessarily a strong
manifestation of CT? For the most part, these kinds of
answers reflect our own blurred comprehensions of what
CT is or is not.
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c) Problem Division (14.8%): Divide and conquer is a well-
known general problem-solving strategy. Most CT will
include some kind of problem division but not every
problem division is necessarily CT.
d) Thinking with a Computer (14.3%): This was the answer
that we hoped to get but did not entirely succeed to convey.
One very small part of the course explored the creation of
STEM simulations. We can only speculate that the idea of
thinking with a computer would perhaps have made more
sense with simulation building than with game design.
e) Problem Solving (13.4%): While CT does include some
problem-solving components, it is more than just problem
solving. For instance, not every kind of problem solving
includes the expression of programs.
f) The AAA/ CT Process (11.4%): Fig. 2 was our main
learning prompt to capture the Abstraction /Automation
/Analysis process described by Wing [1]. The relatively low
frequency of this answer may suggest that the connection
between theory and praxis was not sufficiently explicit.
g) Other (9%): This category was very wide ranging from
actual admissions that pre-service primary level teachers
actually did not know what CT is, to partial credit
statements, e.g., mentioning the CT process model but with
an incorrect sequence.
The same group of 600+ pre-service primary level teachers
will have to take the mandatory Computer Science Didactics
course next semester. Our findings suggest being more explicit
in using and connecting CT with the practice of programming.
One shift from the Introduction to Programming to Computer
Science Didactics course will be the integration of CS with other
disciplines such as STEM, art, music and languages. Seeing CT
applied to other fields may help develop a better sense of
purpose of CT. It really is not the case that we want teachers, or
students, to think like computer scientists or programmers but to
conceptualize CT as general-purpose thinking tool relevant not
only to computer science but to most disciplines taught in public
schools.
7 CONCLUSIONS
Schools of education in Switzerland are starting to make CSEd
mandatory at the elementary school level. The data suggests that
using Computational Thinking Tools, to create games and STEM
simulations, it is possible to teach a basic understanding of
programming to non-self-selected pre-service elementary school
teachers. However, it is less clear, how much or what kind of CT
is conveyed. There appear to be preconceived notions of
computing that are difficult to overcome.
ACKNOWLEDGMENTS
This research was supported by the U.S. National Science
Foundation, the Swiss National Science Foundation, and the
Hasler Foundation.
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... Teachers' and prospective teachers' preconceptions of CT were mostly examined through their written responses to open-ended questions, such as "What do you think computational thinking is?" (Bower & Falkner, 2015;Corradini et al., 2017;Garvin et al., 2019;Hunsaker & West, 2020;Lamprou & Repenning, 2018;Lloyd & Chandra, 2020;Looi et al., 2020;Morreale et al., 2012;Mouza et al., 2017;Umutlu, 2021;Ung et al., 2022;Walton et al., 2020;Yadav et al., 2018), while in only one study, in-depth interviews with primary school teachers were utilized (Rich et al., 2019) and in another study, a predetermined list of skills was given to examine teachers' preexisting opinions about the skills included in CT (Sands et al., 2018). In a recent study including 369 primary and secondary school teachers in Malaysia, 90.80% of the participants reported no knowledge of CT (Ung et al., 2022). ...
... Results of the studies conducted with prospective teachers showed that they were more likely to conceive of CT as problem-solving (Lamprou & Repenning, 2018;Mouza et al., 2017;Yadav et al., 2011Yadav et al., , 2014, thinking like a computer (Lamprou & Repenning, 2018;Yadav et al., 2011), using algorithms (Lloyd & Chandra, 2020;Yadav et al., 2014), and problem-solving by using technology (Bower & Falkner, 2015;Yadav et al., 2011). In comparison, they less frequently described CT as collecting/organizing/processing and testing information, scientific thinking, mathematical thinking, and logical thinking (Bower & Falkner, 2015;Looi et al., 2020), programming and thinking with the computer (Lamprou & Repenning, 2018;Looi et al., 2020;Umutlu, 2021), using technology and computer (Looi et al., 2020;Mouza et al., 2017;Yadav et al., 2014), and data analysis (Lloyd & Chandra, 2020). ...
... Results of the studies conducted with prospective teachers showed that they were more likely to conceive of CT as problem-solving (Lamprou & Repenning, 2018;Mouza et al., 2017;Yadav et al., 2011Yadav et al., , 2014, thinking like a computer (Lamprou & Repenning, 2018;Yadav et al., 2011), using algorithms (Lloyd & Chandra, 2020;Yadav et al., 2014), and problem-solving by using technology (Bower & Falkner, 2015;Yadav et al., 2011). In comparison, they less frequently described CT as collecting/organizing/processing and testing information, scientific thinking, mathematical thinking, and logical thinking (Bower & Falkner, 2015;Looi et al., 2020), programming and thinking with the computer (Lamprou & Repenning, 2018;Looi et al., 2020;Umutlu, 2021), using technology and computer (Looi et al., 2020;Mouza et al., 2017;Yadav et al., 2014), and data analysis (Lloyd & Chandra, 2020). ...
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Computational thinking (CT) is considered a group of problem-solving skills that the next generations are expected to possess. The most efficient way to make them acquire these skills is to incorporate CT into K-12 education. To this end, various education programs have been designed to improve teachers’ and prospective teachers’ competence in CT. Such programs designing educational experiences based on teachers’ and prospective teachers’ preexisting opinions and self-efficacy perceptions about CT could achieve better results. Although the acquisition of CT skills has been suggested to start early on, these beliefs of early childhood teachers and prospective teachers have been underexplored. Therefore, this exploratory study aims to examine early childhood teachers’ and prospective teachers’ preconceptions and self-efficacy about CT. The study was conducted with 63 teachers and 78 prospective teachers in Turkey. Data were collected via an online survey in the spring of the 2020–2021 academic year. The preconceptions were assessed using a structured questionnaire, while the CT self-efficacy was measured with the Computational Thinking Scale. The findings showed similarities between teachers and prospective teachers in the preconceptions of CT. Both of them most strongly associated CT with logical thinking, problem-solving, using algorithms, coding/programming, doing mathematics, using technology in teaching, and using computers. Yet, teachers reported stronger associations between CT and logical thinking, using algorithms, and coding/programming. Furthermore, teachers’ self-efficacy perceptions in CT were significantly higher. The study findings provide some needed information to design professional development programs aiming to enhance CT practices in early education settings.
... Ωστόσο, παρόλη αυτή τη «βαβυλωνία των ορισμών», υπάρχει συνομολόγηση ειδικών (Lamprou & Repenning, 2018;Doleck et al., 2017), περί της θεμελιακότητας της υπολογιστικής σκέψης, ως μιας απαραίτητης δεξιότητας για τον άνθρωπο του 21ου αιώνα, την οποία όλοι οι μαθητές πρέπει να αποκτήσουν. Ο Li et al. (2020a), η Grover (2019) και οι Lamprou & Repenning (2018) αλλά και άλλοι προτείνουν, για την υπολογιστική σκέψη, να διδάσκεται αυτή στο μάθημα της Πληροφορικής ή να ενσωματώνεται σε άλλα γνωστικά αντικείμενα (ως μια σημαντική διεπιστημονική έννοια) παρά να διδάσκεται ως ξεχωριστή ικανότητα ή αντικείμενο. ...
... Ωστόσο, παρόλη αυτή τη «βαβυλωνία των ορισμών», υπάρχει συνομολόγηση ειδικών (Lamprou & Repenning, 2018;Doleck et al., 2017), περί της θεμελιακότητας της υπολογιστικής σκέψης, ως μιας απαραίτητης δεξιότητας για τον άνθρωπο του 21ου αιώνα, την οποία όλοι οι μαθητές πρέπει να αποκτήσουν. Ο Li et al. (2020a), η Grover (2019) και οι Lamprou & Repenning (2018) αλλά και άλλοι προτείνουν, για την υπολογιστική σκέψη, να διδάσκεται αυτή στο μάθημα της Πληροφορικής ή να ενσωματώνεται σε άλλα γνωστικά αντικείμενα (ως μια σημαντική διεπιστημονική έννοια) παρά να διδάσκεται ως ξεχωριστή ικανότητα ή αντικείμενο. ...
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Σκοπός της παρούσας μελέτης είναι να αναδείξει τη δυναμική της θεωρίας της Μετασχηματίζουσας Μάθησης σχετικά με την κοινωνική αλλαγή. Ο στοχαστικός διάλογος, η κριτική συνειδητοποίηση και η αλλαγή των δυσλειτουργικών αντιλήψεων, είναι δυνατόν να βοηθήσουν τα άτομα να αντιμετωπίσουν τις προκλήσεις μιας μεταβαλλόμενης κοινωνίας. Από την κριτική ανάλυση των ευρημάτων φαίνεται ότι η Μετασχηματίζουσα Μάθηση μπορεί να οδηγήσει -πέρα από την κριτική συνειδητοποίηση της πραγματικότητας- στη συλλογική μάθηση, στη διαμόρφωση κοινών στόχων και στην ανάληψη δράσης για κοινωνική αλλαγή.
... In Europe, several countries have focused on implementing CT into existing school curricula or developing new curricula for CT (Caspersen et al., 2019;Caspersen, 2018c;Heintz et al., 2015, Lamprou & Repenning et al., 2018. Slovakia was one of the first countries in Europe to implement CT into education by introducing 'educational programming' into primary schools in 2008 (Gujberova & Kalas, 2013). ...
... These environments have mainly been used to introduce programming through a game-based approach (Basawapatna et al., 2010) but are also now being used as an introduction to computational modeling in courses for teachers. In Switzerland, the future teacher workforce in primary schools are now being introduced to AgentCubes as part of a national curriculum for education (Lamprou & Repenning, 2018). ...
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Acknowledgements It is a privilege and a great pleasure to have worked with so many people who helped make this dissertation possible. A special thank you to: Ole Sejer Iversen, my supervisor, for being so patient with my background in biology. Also for introducing me to new research areas and to the academic life in an interdisciplinary research center. Michael E. Caspersen, my co-supervisor, for introducing me to computing education research and suggesting I pursued this work. He has tirelessly inspired, believed, and supported me and my work. Deborah Tatar, my co-supervisor, for sparking my interest in computational thinking by inviting me to participate in her own work, and for never failing to point me in the right research direction. Palle Nowack, my close friend and colleague, for inviting me on a winding (road-)trip to the land of computational modeling, and for never taking the easy way back. Keld Nielsen, my much appreciated colleague, for tireless discussions and for insisting on the relevance of our work. Peter Musaeus and the rest of my family, for being so kind as to bear with me throughout this process. Numerous people have contributed to my research.
... For example, Switzerland recently introduced a national curriculum, "Lehrplan 21", mandating Computer Science Education, the new common curriculum for compulsory education in the 21 German-speaking cantons. To discover how practical the CS Education course organised for student-teachers was, Lamprou and Repenning [8] conducted a study collecting data from more than 600 students who took the course. The research showed that even though pre-service teachers can quickly learn basic programming, the question about learning CT remains open. ...
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This paper describes an investigation of primary education student teachers’ perceptions of computational thinking (CT) who participated in a course ‘Digital Technologies in Primary Education’, and explores what these students consider difficult when developing primary school pupils’ CT. In the academic year 2021/22, a revised curriculum will be introduced into Czech primary school education. Instead of ‘ICT’, ‘Informatics’ is to be introduced into the curriculum as a new subject at all school levels. Pupils’ digital literacy will be formed and developed across all subjects, so all faculties of education in the Czech Republic have paid great attention to the development of primary education student teachers, to prepare them for the planned changes in school practice. Using qualitative methods, the study findings of 66 primary education student teachers (who analysed the Bebras contest for primary school pupils) were that (1) for better understanding of CT, student teachers are required to have sufficient Informatics’ knowledge to be able to think computationally, (2) student teachers reported CT is close to mathematical thinking, but these two concepts are not the same, and (3) CT development in primary education requires logical thinking, reading literacy and counting abilities.
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Addressing unresolved questions concerning computational thinking.
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