Computational Thinking in K-12: In-service Teacher Perceptions of Computational Thinking: Foundations and Research Highlights

Abstract

Computational thinking (CT) has been described as a problem-solving approach that draws from the practices of computer science (CS). Computer science ideas and practices influence multiple domains, from simplifying complex tasks and problems through problem decomposition to using automation to increase the speed and efficiency of solving those problems. Computational thinking is, thus, described as a set of mental skills, a disposition common to most fields, and computer science concepts that can impact those fields decontextualized from programming and hardware. Researchers and educators have worked to integrate CT into multiple subjects in K-12. This takes the form of both identifying instances of CT already being used in existing teacher practices and identifying areas where disciplinary practices can be changed through the latest application of computational tools. This chapter reports the results from a study to examine practicing teachers’ views of CT and how those views compare to how computer science education researchers define CT. Results from this study suggest that teachers’ conceptions of CT include important aspects of the CT literature, yet there are several common misconceptions about CT. We discuss implications of our findings on how to engage non-computing K-12 teachers in computational thinking and develop their competencies to incorporate CT within the context of their subject area. The goal of this discussion is to inform in-service and preservice teacher development efforts and clarify how CT applies to disciplinary knowledge within K-12 education.

Full text

Chapter 8
Computational Thinking in K-12: In-service
Teacher Perceptions of Computational
Thinking
Phil Sands, Aman Yadav, and Jon Good
8.1 Introduction
Much of what we know about computational thinking comes from early research in
educational practices using computers (Papert 1980; Pea and Kurland 1984) and
from common conceptions of how computer scientists think about problems
designed to be solved by computers (Denning 2009). Wing (2006) formalized
computational thinking in an influential article discussing the ways computer scien-
tists think about problems and how skills associated with computing are broadly
applicable in other disciplines. Wing sparked a discussion about how educators
should prepare students for careers influenced by computing and where core com-
putational thinking concepts could be integrated into K-12 curricula (Barr and
Stephenson 2011; Grover and Pea 2013; Yadav et al. 2014). Almost a decade
later, teaching computational thinking skills to students has permeated at all levels
of elementary and secondary schools. This integration is being done through the
generation of new curricula within computer science education programs –the AP
computer science principles course is one notable example –as well as in other
content areas, such as mathematics and science (Weintrop et al. 2016). With this
increased interest, however, comes key questions about how in-service teachers
conceptualize computational thinking, especially teachers who are not trained in
computer science. Namely, how do these teachers understand computational con-
cepts as they work to apply them in their classrooms? Further, what steps do we need
to take to help in-service teachers integrate computational thinking into their
curriculum?
Most of the attention on embedding computational thinking during the past
decade has focused on preservice teachers (Yadav et al. 2011,2014). While this
P. Sands · A. Yadav (*) · J. Good
College of Education, Michigan State University, East Lansing, MI, USA
e-mail: ayadav@msu.edu
©Springer International Publishing AG, part of Springer Nature 2018
M. S. Khine (ed.), Computational Thinking in the STEM Disciplines,
https://doi.org/10.1007/978-3-319-93566-9_8
151

information can help guide in-service teachers’professional development, we have
yet to identify the unique challenges that exist in introducing computational thinking
to non-computing teachers. A better understanding of in-service teachers’concep-
tions of computational thinking can guide design of teacher professional develop-
ment programs. In a recent survey, we examined how K-12 in-service teachers
perceive computational thinking within elementary and secondary classrooms. We
present results from the survey and provide recommendations for developing pro-
fessional development programs around computational thinking practices. We also
discuss specific areas within the computational thinking model that lend themselves
to the nature of applied problem-solving in K-12 classrooms.
8.2 Background
In considering computational thinking and its application to student preparation,
Wing (2008) pointed to the links between CT and the wide variety of disciplinary
skills traditionally taught in K-12 classrooms. These connections focus on the
ubiquitous nature of computing and the nature of abstraction as it pertains to
STEM career pathways. In addition, Wing stressed that computational thinking
was not the same as the practice of programming; rather, she argued that the skills
used in programming are useful for problem-solving in multiple contexts. Denning
(2009) argued for the use of computational thinking ideas as the “third leg of
science,”a component of the inquiry process as much as it is a separate and distinct
discipline. While Wing and Denning differed in how computational thinking was
framed, they both agreed on the benefits for students from learning computer
science. Regardless of which perspective one takes, it is apparent that the connec-
tions between computing and K-12 curricula are deep enough to justify the interest
in further embedding these ideas in classrooms.
Since Wing (2006) introduced computational thinking, there have been several
attempts to expand on what ideas encapsulate CT. Wing proposed that computa-
tional skills include abstraction, problem decomposition, pattern recognition, algo-
rithmic thinking, and logical thinking. In attempting to draw connections between
these skills and an educational model in Bloom’s taxonomy, Selby (2015) organized
a variation of these ideas by perceived difficulty: evaluation, algorithm design,
generalization, abstraction of functionality, abstraction of data, and decomposition.
Barr and Stephenson (2011) proposed nine major computational thinking concepts
and abilities to be used within K-12 classrooms across core content areas. These
include data collection, data analysis, data representation, problem decomposition,
abstraction, algorithms and procedures, automation, parallelization, and simulation.
This set is echoed in the work of Grover and Pea (2013), who offered that CT was
comprised of abstractions and pattern generalizations, systematic processing of
information, symbol systems and representations, algorithmic notions of flow of
control, structured problem decomposition, iterative, recursive, and parallel
152 P. Sands et al.

thinking, conditional logic, efficiency and performance constraints, and debugging
and systematic error detection. A more complex set of skills were described by the
National Research Council (2010) including:
reformulation of difficult problems by reduction and transformation; approximate solutions;
parallel processing; checking and model checking as generalizations of dimensional analy-
sis; problem abstraction and decomposition; problem representation; modularization; error
prevention, testing, debugging, recovery and correction; damage containment; simulation;
heuristic reasoning; planning, learning, and scheduling in the presence of uncertainty; search
strategies; analysis of the computational complexity of algorithms and processes; and
balancing computational costs against other design criteria. (p. 3)
Given the wide variety of skills that can be connected to computational thinking, the
lack of a clearly defined subset of skills may confuse educators trying to implement
these practices.
Computational thinking skills have also appeared in recent updates to K-12
curriculum frameworks, such as Next Generation Science Standards (NGSS) as
well as other curricula designed to teach introductory computing skills. The Next
Generation Science Standards (NGSS) include the use of CT as an important practice
to develop scientific understanding (NGSS Lead States 2013). The College Board
created a new Advanced Placement computing course focusing on six key compu-
tational thinking practices, with the goal of attracting a more diverse group of
students to computer science (2014). Similarly, Google introduced the CS First
initiative to provide traditional computer science activities and lessons focused on
computational thinking primarily for use by out-of-school organizations.
Considering that the onus for implementing these programs is on educators with
limited experience in computing, a concern is the risk of conflating computational
thinking with computer science or mathematics. There is also a potential for those
implementing computational thinking ideas to imply that both CT and CS require the
use of programming in all contexts (Fletcher and Lu 2009). In order to address this
issue, it has been suggested that educators encourage the use of computational
thinking skills at an early age, concentrating more on the innate thought processes
that are associated with computing as opposed to specific computing tools. By doing
so, educators can reduce the barriers for entry for students taking computing courses
later in their academic careers (Margolis et al. 2010). This group includes not just
students that develop further interest in computer science but also students interested
in other fields engaging with computing in some form.
In spite of the potentially overwhelming set of skills that can be included in
definitions of computational thinking, it is possible to implement most of the core
ideas in primary and secondary classrooms without overemphasizing technical
abilities. Examples can include digital storytelling, simple data collection, and the
encouragement of scientific investigation (Lee et al. 2014). Considering that teachers
may be using these skills in primary school classrooms already (Mannila et al. 2014),
this suggests a need to help move teachers from implicit to explicit practices
grounded in an understanding of why computational practices are relevant to student
development.
8 Computational Thinking in K-12: In-service Teacher Perceptions of... 153

8.3 Need
Computational thinking practices have the potential to develop student interest in
how computing plays a role in other disciplines, specifically STEM. In order to see
the benefits of student exposure to these computing concepts, we need to train both
preservice and in-service teachers in computational thinking practices regardless of
academic discipline. Across the United States, academic standards have been rewrit-
ten to include computational thinking as a core principle of curriculum implemen-
tation. Examples of this include the Next Generation Science Standards which
include computational thinking concepts (NGSS 2013), Indiana’s K-8 science
standards (Indiana Department of Education 2017), and Texas’Essential Knowledge
and Skills for elementary education (Texas State Board of Education 2012). Design-
ing teacher professional development program should focus on augmenting teachers
existing competencies while relying on established best practices, in order to align
courses with the major components of computational thinking. As an important step
in this process, we need to understand in-service teachers’current perceptions of
computational thinking (Prieto-Rodriguez and Berretta 2014). In identifying areas of
need, the transition can then be made to connecting professional development with
classroom integration of CT. This study examined in-service teachers’conceptions
of computational thinking and was guided by the following research questions:
1. How do in-service teachers conceptualize computational thinking as it would
manifest in classroom practice?
2. How does teachers’subject area influence their computational thinking
conceptualizations?
3. How does teachers’grade level taught influence their computational thinking
conceptualizations?
8.4 Methods
Participants Seventy-four elementary and secondary teachers from a Midwestern
state participated in the study. Of these teachers, 65 were female and 9 were male.
Teachers taught at a variety of levels in the K-12 spectrum but could be divided
roughly into primary school (N ¼45) and secondary school (N ¼29) levels. For the
purposes of this study, we included grades K-6 as primary school teachers and
grades 7–12 as secondary school teachers. Lastly, we considered those teachers that
taught primarily STEM subjects (N ¼29) versus those that were in non-STEM
subjects (N ¼55). STEM subjects included mathematics, science, computers, or
technology.
154 P. Sands et al.

Survey The survey included ten Likert scale questions based on prior work exam-
ining preservice teachers’perceptions of computational thinking (Yadav et al. 2011,
2014). The survey items began with the phrase “Computational thinking
involves...”followed by a short stem that either belonged or did not belong to the
broader perception of computational thinking. Teachers responded to the items on a
Likert scale with five potential response values. These included “strongly agree,”
“agree,”“disagree,”“strongly disagree,”and “don’t know.”Table 8.1a includes the
list of survey items, and Table 8.1b includes how we characterized whether the item
aligned with literature’s conceptions of computational thinking. It should be noted in
this table that the concept of “coding/programming”was not categorized due to
disagreement over whether programming is an essential element of teaching CT in
classrooms (Denning 2009; Wing 2006; Brennan and Resnick 2012). The internal
reliability of these items was assessed using Cronbach’s alpha (α¼0.92). In
addition, the survey included items to collect demographic information regarding
teachers’gender, grade level taught, and subjects taught.
The survey was distributed at the Michigan Association for Computer Users in
Learning (MACUL) conference. Participants were recruited at an exhibition booth
for university K-12 outreach programming.
Table 8.1a Items included
in the teacher survey Computational thinking involves...
... solving problems
... using heuristics/algorithms
... logical thinking
... thinking like a computer
... coding/programming
... doing mathematics
... using computers (e.g., office tools)
... knowing how to use a computer
... using technology in your teaching
... playing online games
Table 8.1b How researchers categorized items from the teacher survey
Computational thinking involves... Computational thinking does not involve...
... solving problems ... doing mathematics
... using heuristics/algorithms ... using computers (e.g., office tools)
... logical thinking ... knowing how to use a computer
... thinking like a computer ... using technology in your teaching
... playing online games
It is unclear whether or not computational thinking involves...
... coding/programming
8 Computational Thinking in K-12: In-service Teacher Perceptions of... 155

8.5 Data Analysis
Likert response was given a numerical value from 1 to 4 (“strongly agree,”1;
“agree,”2; “disagree,”3; “strongly disagree,”4), and missing responses and those
marked as “don’t know”were excluded from these calculations. We used descriptive
analysis for each of the survey items to view patterns in teachers’conceptions of
computational thinking. In addition, Mann-Whitney U test was used to analyze the
influence of teachers’subject area and grade level taught on their conceptions of
computational thinking. Mann-Whitney U test, a nonparametric alternative test to
the independent t-test, was used due to the ordinal nature of the data. The data was
analyzed using the R statistical package.
8.6 Results
Majority of the teachers in our study were most confident that computational
thinking involved logical thinking (100%), doing mathematics (100%), and solving
problems (99%). To a lesser degree, majority of the teachers also agreed that
computational thinking involved using heuristics or algorithms (93%), using com-
puters (86%), using technology in teaching (82%), and knowing how to use a
computer (76%). Teachers’conceptions of computational thinking are shown in
Fig. 8.1, and the descriptive statistics are presented in Table 8.2.
8.6.1 STEM vs Non-STEM Teachers
STEM refers to teaching and learning in the fields of science, mathematics, engi-
neering, and technology (Gonzalez and Kuenzi 2012). For the purpose of this study,
teachers that specified their primary area as one of the natural sciences or engineering
(e.g., computer science, physics, chemistry, etc.) were included within STEM. This
group was categorized as “STEM”teachers, and those outside of these disciplines
was categorized as “non-STEM”teachers. For this study, most of the primary school
teachers were removed from the STEM analysis because these educators commonly
teach all domains. Only those primary educators that specified a domain specializa-
tion were considered in this analysis. Table 8.3 shows the breakdown by grade level
and STEM specialization.
As shown in Fig. 8.2, results showed that STEM teachers had the greatest
confidence that computational thinking involved doing mathematics (100%), logical
thinking (100%), solving problems (100%), using computers (96%), and using
heuristics or algorithms (96%). The non-STEM teachers showed similar beliefs
that computational thinking involved doing mathematics (100%), logical thinking
(100%), solving problems (100%), and using heuristics or algorithms (93%). While
156 P. Sands et al.

27%
25%
24%
18%
16%
14%
7%
1%
Playing online games
Computational Thinking involves...
Percentage
thinking like a computer
knowing how to use a computer
using technology in teaching
coding or programming
using computers
using heuristics or algorithms
solving problems
logical thinking
doing mathematics
0%
0%
73%
75%
76%
82%
84%
86%
93%
99%
100%
100%
100
Response Strongly Agree Agree Disagree Strongly Disagree
10050 500
Fig. 8.1 Teachers’conceptions of computational thinking
Table 8.2 Descriptive statistics on teachers’conceptions of computational thinking
Computational thinking involves... Mean Standard deviation
... doing mathematics 1.31 0.46
... using computers (e.g., office tools) 1.67 0.90
... solving problems 1.28 0.48
... using heuristics/algorithms 1.5 0.76
... logical thinking 1.23 0.42
... thinking like a computer 1.70 0.92
... knowing how to use a computer 1.84 0.99
... using technology in your teaching 1.65 0.88
... playing online games 1.83 0.97
... coding/programming 1.64 0.83
Note: The scale was from 1 (strongly agree) to 4 (strongly disagrees)
8 Computational Thinking in K-12: In-service Teacher Perceptions of... 157

there were similar responses between the STEM and non-STEM teachers on almost
all of the items, two notable exceptions were “thinking like a computer”and “using
computers.”This showed that non-STEM teachers were less likely to view those as
computational thinking. It should be noted that “using computers”was described on
the survey instrument as being akin to using office tools and other applications.
Table 8.3 Primary and secondary teachers considering STEM vs non-STEM teaching credentials
Primary Secondary
STEM 14 15 29
Non-STEM 31 14 45
45 29
85%
coding or programming
doing mathematics
knowing how to use a computer
logical thinking
playing online games
solving problems
thinking like a computer
using computers
using heuristics or algorithms
using technology in teaching
100 10050 500
86%
85%
71%
85%
86%
96%
83%
96%
93%
77%
79%
76%
77%
100%
100%
100%
100%
100%
100%
15%
14%
15%
29%
15%
14%
4%
17%
4%
7%
23%
21%
24%
23%
0%
0%
0%
0%
0%
0%
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
STEM
Non-STEM
Computational Thinking involves...
Percentage
Response Strongly Agree Agree Disagree Strongly Disagree
Fig. 8.2 STEM vs. non-STEM teachers and perceptions of computational thinking
158 P. Sands et al.

Mann-Whitney U results exhibited there was no significant difference between
STEM and non-STEM teachers on how they conceptualized computational thinking
(see Table 8.4 for the Mann-Whitney U statistics for each of the computational
thinking items).
8.6.2 Primary vs Secondary School Teachers
Over the last decade, the high awareness of STEM curricula has led to more
elementary teachers exploring ways to engage their students in technology
(DeJarnette 2012); hence, we examined whether there were differences in how
they conceptualized computational thinking when compared to secondary teachers.
As shown in Fig. 8.3, results demonstrated that secondary teachers believed that
computational thinking involved doing mathematics (100%), logical thinking
(100%), solving problems (100%), and using heuristics or algorithms (100%).
Similarly, primary teachers also viewed computational thinking as involving doing
mathematics (100%), logical thinking (100%), and solving problems (98%). How-
ever, there were some differences between the two groups as secondary teachers
disagreed at a higher rate whether computational thinking involved “knowing how to
use a computer,”“playing online games,”and “using technology in teaching.”In
addition, they had uniform sentiment that “using heuristics or algorithms”belonged
to computational thinking, while primary teachers showed some disagreement.
Other items showed some differences, but none that were visually significant enough
to note.
Mann-Whitney U results suggested no significant difference between primary
and secondary teachers on how they conceptualized computational thinking (see
Table 8.5 for the Mann-Whitney U statistics for each of the computational thinking
items).
Table 8.4 Mann-Whitney U test comparing STEM vs Non-STEM teachers
Computational thinking involves... U statistic p-value
... doing mathematics 526 0.06557
... using computers 437.5 0.5706
... solving problems 473.5 0.3973
... using heuristics or algorithms 423.5 0.7236
... logical thinking 396 0.8475
... thinking like a computer 420 0.2644
... knowing how to use a computer 389 0.8276
... using technology in teaching 385 0.8967
... playing online games 349 0.6169
... coding or programming 333 0.5387
8 Computational Thinking in K-12: In-service Teacher Perceptions of... 159

77%
coding or programming
doing mathematics
knowing how to use a computer
logical thinking
playing online games
solving problems
thinking like a computer
using computers
using heuristics or algorithms
using technology in teaching
100 10050 500
88%
72%
76%
77%
86%
89%
84%
100%
88%
73%
79%
68%
77%
100%
100%
100%
100%
100%
98%
23%
12%
28%
24%
23%
14%
11%
16%
0%
12%
27%
21%
32%
23%
0%
0%
0%
0%
0%
2%
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Secondary
Primary
Computational Thinking involves...
Percentage
Response Strongly Agree Agree Disagree Strongly Disagree
Fig. 8.3 Primary vs. secondary teachers and perceptions of computational thinking
Table 8.5 Mann-Whitney U test comparing primary and secondary teachers’perceptions
Computational thinking involves... U statistic p-value
... doing mathematics 688.5 0.24
... using computers 607 0.72
... solving problems 651 0.51
... using heuristics or algorithms 673.5 0.24
... logical thinking 621.5 0.50
... thinking like a computer 550.5 0.71
... knowing how to use a computer 571.5 0.73
... using technology in teaching 565 0.79
... playing online games 508.5 0.76
... coding or programming 525.5 0.92
160 P. Sands et al.

8.7 Discussion
Overall, results suggested that while teachers conceptualized computational thinking
in alignment with the literature, they also had some incorrect ideas about what
computational thinking entailed. We also found that there were no differences on
teachers’conceptions of computational thinking based upon either the content area
(STEM vs. non-STEM) or grade level (primary vs. secondary). Computational
thinking involves a set of skills that describe many of the same abilities inherent to
programming and problem-solving with computers (Denning 2009). The responses
given by the teachers in our study suggested that many educators have very little
knowledge about what these skills are and lack awareness of how these skills can be
implemented in their classrooms. The results suggest that there is much work to be
done before in-service teachers are able to implement computational thinking in their
classrooms.
Based on the literature, we classified what computational thinking entails (see
Table 8.1b). Our results exhibited that teachers had the greatest confidence that CT
involved “logical thinking”and “solving problems,”which align with how compu-
tational thinking has been conceptualized recently (Denning 2017). On the other
hand, teachers also viewed CT as “doing mathematics,”which does not align with
the common conception of computational thinking. Overall, we found that majority
of the teachers strongly agreed with all the components of computational thinking
outlined in the survey items and in many cases that teachers incorrectly agreed with
concepts that we did not view as computational thinking. With these conceptions of
computational thinking, a teacher simply using digital tools, such as Microsoft
Office, might think that he/she is engaging his/her students in computational think-
ing. On the other hand, it is also possible that teachers might think that CT involves
too many conceptual tasks to integrate.
Our results support the need to develop non-computing teachers’understanding
of computational thinking if it is to permeate within K-12. Teachers, regardless of
whether they taught a STEM subject or not, have similar ideas about computational
thinking and sometimes hold incorrect conceptions. Given the prevalence of incor-
rect views related to computational thinking suggests that while CT maybe a
buzzword in computing education, many teachers are not being introduced to the
core components of computational thinking. While researchers have argued for the
need to embed computational thinking within teacher education (Yadav et al. 2017),
our results suggest the need to also train in-service teachers. This training needs to be
content-specific on how to integrate computational thinking ideas into existing
curriculum. Specifically, teachers need to be introduced to computational thinking
in a way that meets their existing learning goals and fits within their pedagogical
practices. Rather than adapting approaches designed for preservice teachers, we
instead propose implementing a distinct strategy for integrating CT ideas aimed at
teachers already working in K-12 classrooms.
In-service teacher professional programs need to provide support for content
integration, allowing educators to utilize their existing body of knowledge while
8 Computational Thinking in K-12: In-service Teacher Perceptions of... 161

also meeting their needs with regard to time constraints and availability. Existing
research into teacher professional development has found the difficulties of provid-
ing long-term gains in the classroom based on limited exposure to applied concepts
through isolated workshop sessions (Harris and Sass 2011; Desimone 2009). Thus,
in order to successfully train teachers to integrate computational thinking into K-12
classrooms, we need to develop ongoing and continuous professional development
programs that help teachers develop a thorough understanding about what it means
to think computationally and then engage their students in computing ideas (Yadav
et al. 2017).
Professional development needs to draw upon teachers’expertise in their content
knowledge, pedagogical knowledge, and pedagogical content knowledge. The
Reading Apprenticeship model (Greenleaf et al. 2011) provides a framework to
support teachers’learning of computational thinking concepts and develop students’
understanding of how computation can be applied in specific subject areas. Specif-
ically, professional development should point out clear connections and how com-
putational thinking can meet subject area learning goals rather than just being an
instructional add-on in the K-12 curriculum (Greenleaf et al.). Given the large
number of demands teachers face and the time constraints of the classroom, we
also need to address how to deliver the content to teachers. Schools of education
should collaborate with departments of computer science to lead state-approved
professional development certification programs in computing education. These
low-cost flexible programs could be delivered online, to allow teachers to learn
virtually and be a member of an online community of practice to discuss how
computational thinking can be embedded to meet their subject-specific learning
goals. As suggested by Yadav et al. (2017), we believe that an online community
of practice would allow teachers to effectively integrate computational thinking to
meet their curriculum needs.
Our findings have important implications for how professional development
programs should be structured to ensure that teachers effectively integrate compu-
tational thinking in their classrooms. Results suggest that professional development
needs to differentiate between the use of computing tools and the concepts and
practices inherent to computational thinking. It might be beneficial to expose
teachers to computational thinking without the use of computers, such as using the
CS Unplugged curriculum (Bell et al. 2009). Focusing on unplugged activities might
help teachers grasp how computational thinking and the use of computers in the
classroom differ from one another. We believe that given Wing’s(2006) description
of computational thinking overlapped with aspects of problem-solving components,
such as abstraction, problem decomposition, pattern recognition, and algorithmic
thinking, a focus on problem-solving skills offers a low floor to get teachers
interested in computational thinking. By using problem-solving as the focus, we
feel that more teachers will be motivated to embed subcomponents of computational
thinking in their regular academic subjects (Yadav et al. 2016).
This study had a few limitations, which has implications for generalizability of
the findings. First, we acknowledge that the survey was based on a small number of
teachers and may not have accurately represented teacher knowledge of
162 P. Sands et al.

computational thinking across the United States. The impact of this small group is
also enhanced due to the large number of elementary teachers in our sample that
were not included in our evaluation of STEM and non-STEM teachers. Additionally,
given that participants in our study were volunteers might lead to self-selection bias,
which limits generalizability of the results. It is also possible that the since teachers
completed the survey at a conference focused on technology in education, they were
more focused on computational thinking as involving use of technology/digital
tools. At the same time, given that teachers interested in technology struggled with
identifying computational thinking ideas suggests we have an uphill climb before
CT becomes another core subject similar to reading, writing, and arithmetic as called
for by Wing (2006).
In summary, we recognize the need to prepare students for twenty-first-century
careers makes it essential for K-12 teachers to be prepared to integrate computational
thinking concepts. This requires a multipronged approach to prepare teachers at the
preservice and in-service level to become computationally literate.
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