ChapterPDF Available

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


Abstract and Figures

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.
Content may be subject to copyright.
Chapter 8
Computational Thinking in K-12: In-service
Teacher Perceptions of Computational
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 inuential 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 inuenced 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
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
©Springer International Publishing AG, part of Springer Nature 2018
M. S. Khine (ed.), Computational Thinking in the STEM Disciplines,
information can help guide in-service teachersprofessional 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 teachersconcep-
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 specic 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 benets 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 Blooms taxonomy, Selby (2015) organized
a variation of these ideas by perceived difculty: 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 ow of
control, structured problem decomposition, iterative, recursive, and parallel
152 P. Sands et al.
thinking, conditional logic, efciency 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 difcult 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 dened 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 scientic 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 conating 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 specic 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 elds engaging with computing in some form.
In spite of the potentially overwhelming set of skills that can be included in
denitions 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 scientic 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
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, specically STEM. In order to see
the benets 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), Indianas K-8 science
standards (Indiana Department of Education 2017), and TexasEssential 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 teacherscurrent 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 teachersconceptions
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 teacherssubject area inuence their computational thinking
3. How does teachersgrade level taught inuence their computational thinking
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 712 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
154 P. Sands et al.
Survey The survey included ten Likert scale questions based on prior work exam-
ining preservice teachersperceptions 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 ve potential response values. These included strongly agree,
agree,”“disagree,”“strongly disagree,and dont know.Table 8.1a includes the
list of survey items, and Table 8.1b includes how we characterized whether the item
aligned with literatures conceptions of computational thinking. It should be noted in
this table that the concept of coding/programmingwas 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 Cronbachs alpha (α¼0.92). In
addition, the survey included items to collect demographic information regarding
teachersgender, 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., ofce 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., ofce 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 dont knowwere excluded from these calculations. We used descriptive
analysis for each of the survey items to view patterns in teachersconceptions of
computational thinking. In addition, Mann-Whitney U test was used to analyze the
inuence of teacherssubject 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 condent 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%). Teachersconceptions 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 elds of science, mathematics, engi-
neering, and technology (Gonzalez and Kuenzi 2012). For the purpose of this study,
teachers that specied 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 STEMteachers, and those outside of these disciplines
was categorized as non-STEMteachers. 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 specied 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
condence 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.
Playing online games
Computational Thinking involves...
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
Response Strongly Agree Agree Disagree Strongly Disagree
10050 500
Fig. 8.1 Teachersconceptions of computational thinking
Table 8.2 Descriptive statistics on teachersconceptions of computational thinking
Computational thinking involves... Mean Standard deviation
... doing mathematics 1.31 0.46
... using computers (e.g., ofce 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 computerand using
computers.This showed that non-STEM teachers were less likely to view those as
computational thinking. It should be noted that using computerswas described on
the survey instrument as being akin to using ofce 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
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
Computational Thinking involves...
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 signicant 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 algorithmsbelonged
to computational thinking, while primary teachers showed some disagreement.
Other items showed some differences, but none that were visually signicant enough
to note.
Mann-Whitney U results suggested no signicant 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
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
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
Computational Thinking involves...
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 teachersperceptions
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
teachersconceptions 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
Based on the literature, we classied what computational thinking entails (see
Table 8.1b). Our results exhibited that teachers had the greatest condence that CT
involved logical thinkingand 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
Ofce, 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 teachersunderstanding
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-specic on how to integrate computational thinking ideas into existing
curriculum. Specically, teachers need to be introduced to computational thinking
in a way that meets their existing learning goals and ts 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 difculties 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 teachersexpertise in their content
knowledge, pedagogical knowledge, and pedagogical content knowledge. The
Reading Apprenticeship model (Greenleaf et al. 2011) provides a framework to
support teacherslearning of computational thinking concepts and develop students
understanding of how computation can be applied in specic 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 certication programs in computing education. These
low-cost exible 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-specic 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 ndings 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 benecial 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 Wings(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 oor 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 ndings. 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-rst-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.
Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and
what is the role of the computer science education community? ACM Inroads, 2(1), 4854.
Bell, T., Alexander, J., Freeman, I., & Grimley, M. (2009). Computer science unplugged: School
students doing real computing without computers. The New Zealand Journal of Applied
Computing and Information Technology, 13(1), 2029.
Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development
of computational thinking. In Proceedings of the 2012 annual meeting of the american
educational research association, Vancouver, Canada (pp. 125).
The College Board. (2014). AP Computer Science Principles: 20162017. Retrieved from https://
DeJarnette, N. (2012). America's children: Providing early exposure to STEM (science, technology,
engineering and math) initiatives. Education, 133(1), 7784.
Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of
the ACM, 60(6), 3339.
Denning, P. J. (2009). Beyond computational thinking. Communications of the ACM, 52(6), 2830.
Desimone, L. M. (2009). Improving impact studies of teachersprofessional development: Toward
better conceptualizations and measures. Educational Researcher, 38(3), 181199.
Fletcher, G. H., & Lu, J. J. (2009). Education: Human computing skills: Rethinking the K-12
experience. Association for computing machinery. Communications of the ACM, 52(2), 23.
Gonzalez, H. B., & Kuenzi, J. J. (2012). Science, technology, engineering, and mathematics
(STEM) education: A primer. Congressional research service.Retrieved from
Greenleaf, C. L., Litman, C., Hanson, T. L., Rosen, R., Boscardin, C. K., Herman, J., Schneider,
S. A., Madden, S., & Jones, B. (2011). Integrating literacy and science in biology: Teaching and
learning impacts of reading apprenticeship professional development. American Educational
Research Journal, 48(3), 647717.
Grover, S., & Pea, R. (2013). Computational thinking in K12 a review of the state of the eld.
Educational Researcher, 42(1), 3843.
Harris, D. N., & Sass, T. R. (2011). Teacher training, teacher quality and student achievement.
Journal of Public Economics, 95(7), 798812.
8 Computational Thinking in K-12: In-service Teacher Perceptions of... 163
Indiana Department of Education (2017). Indiana academic standards: Science & computer
science. Retrieved from
Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K8 curric-
ulum. ACM Inroads, 5(4), 6471.
Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014).
Computational thinking in K-9 education. In Proceedings of the working group reports of the
2014 on innovation & technology in computer science education conference (pp. 129).
New York: ACM.
Margolis, J., Estrella, R., Goode, J., Holme, J. J., & Nao, K. (2010). Stuck in the shallow end:
Education, race, and computing. Cambridge: MIT Press.
National Research Council. (2010). Report of a workshop on the scope and nature of computational
thinking. Washington, DC: National Academies Press.
NGSS Lead States (Ed.). (2013). Next generation science standards: for states, by states.
Washington, DC: National Academies Press. Retrieved from
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books,
Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming.
New Ideas in Psychology, 2(2), 137168.
Prieto-Rodriguez, E., & Berretta, R. (2014). Digital technology teachersperceptions of computer
science: It is not all about programming. In 2014 I.E. Frontiers in Education Conference (FIE)
Proceedings (pp. 15).
Selby, C. C. (2015). Relationships: Computational thinking, pedagogy of programming, and
blooms taxonomy. In Proceedings of the workshop in primary and secondary computing
education (pp. 8087). New York: ACM.
Texas State Board of Education (2012). Chapter 111. Texas essential knowledge and skills for
mathematics subchapter a. Elementary. Retrieved from
Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016).
Dening computational thinking for mathematics and science classrooms. Journal of Science
Education and Technology, 25(1), 127147.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 3335.
Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Trans-
actions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881),
Yadav, A., Gretter, S., Hambrusch, S., & Sands, P. (2017). Expanding computer science education
in schools: Understanding teacher experiences and challenges. Computer Science Education,
26, 235254.
Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical
approaches to embedding a 21st century problem solving in K-12 classrooms. TechTrends,
60, 565568.
Yadav, A., Mayeld, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in
elementary and secondary teacher education. ACM Transactions on Computing Education
(TOCE), 14(1), 5.
Yadav, A., Zhou, N., Mayeld, C., Hambrusch, S., & Korb, J. T. (2011, March). Introducing
computational thinking in education courses. In Proceedings of the 42nd ACM technical
symposium on Computer science education (pp. 465470). New York: ACM.
164 P. Sands et al.
... From pre-service to in-service teachers, there is a lack of understanding of CT, typically viewed simply as mathematics or rudimentary uses of the computer (Sands et al., 2018;Yadav et al., 2018). Most studies apply either plugged or unplugged approaches in teacher education. ...
Full-text available
Computational thinking (CT) is one of the skills that are critical for problem-solving in a technology-driven society. Although the importance of CT as a goal in education is increasingly acknowledged, there is scant research on developing pre-service teachers’ CT competencies so that they can integrate CT in their lesson design. In this study, drawing from the experiential learning framework, we discuss the design of a module using a novel approach that is a hybridisation of plugged and unplugged CT approaches. The aim is to facilitate pre-service teachers in making connections between CT and their teaching contexts. Thirty-eight pre-service teachers attended the CT module for twelve weeks. The results indicated that the participants developed better CT competencies by integrating, justifying and reflecting CT in their lesson design. This study demonstrates the importance of providing a practical CT module to conduct unplugged activities for pre-service teachers, especially for those without prior computing knowledge, before introducing CT in the context of programming.
... The measurement of selfefficacy showed marginal differences based on the comparison of the preliminary and post-tests. Peel et al (2022) summarize that many teachers, due to the lack of IT and programming experience, are not adequately prepared to effectively integrate algorithmic thinking into technical knowledge (Aljowaed & Alebaikan, 2018;Sands et al., 2018;Wu et al., 2018). This inexperience can lead to low self-efficacy and low self-confidence (Aljowaed and Alebaikan, 2018;Rich et al. 2021). ...
Full-text available
Negative attitudes and perceptions on programming impair the effectiveness of learning programming skills. In this study the attitude related to programming, problem solving, and self-views on importance of IT/programming knowledge were assessed by pre- and post-test completed at the beginning and at the end of a software development course. The study was conducted using an online questionnaire and four different dimensions were measured by a survey consisting 23 items. The results show positive moderate associations between self-commitment in problem solving and algorithmic and problem solving ability and negative weak relationship with lack of self-confidence in programming. K-means algorithm showed that the students could be classified into two main groups stronger and weaker self-confidence in programming. In the case of both clusters, it was possible to achieve a positive change in attitudes related to programming. In the case of weaker self-confidence in programming, a greater change can be observed in the attitudes, which can be considered an important result from the point of view of the effectiveness of the software development course. The research presented in the article proves that attitudes related to programming can be influenced in a positive direction both in the case of those with stronger, but even more so in the case of those with weaker attitudes.
... In a similar study, Sands et al. (2018) surveyed primary and secondary school teachers in STEM and non-STEM related fields to understand how they conceptualized and embedded CT into lessons. They found teachers' generally believed CT involved using algorithms, problem-solving, and logical thinking. ...
Data science and computational thinking (CT) skills are important STEM literacies necessary to make informed daily decisions. In elementary schools, particularly in rural areas, there is little instruction and limited research towards understanding and developing these literacies. Using a Research-Practice Partnership model (RPP; Coburn & Penuel, 2016) we conducted multimethod research investigating nine elementary teachers’ perceptions of data science and related curriculum design during professional development (PD). Connected Learning theory, enhanced with Universal Design for Learning, guided ways we assisted teachers in designing the data science curriculum. Findings suggest teachers maintained high levels of interest in data science instruction and CT before and after the PD and increased their self-efficacy towards teaching data science. A thematic analysis revealed how a data science framework guided curriculum design and assisted teachers in defining, understanding, and co-creating the curriculum. During curriculum design, teachers shared the workload among partners, made collaborative design choices, integrated differentiation strategies, and felt confidence towards teaching data science. Identified challenges included locating data sets and the complexity of understanding data science and related software. This study addresses the research gap in data science education for elementary teachers and assists with successful strategies for data science PD and curricular design
... Teachers, after relevant training, positively changed their perceptions and skills about CT [27], [29], [30]. The study, concerning Computer Science teachers in Greece, revealed that teachers did not have a deep understanding of CT's meaning and their attitude was not positive regarding its inclusion in education [32]. In Turkey, there was a significant difference between in-service and pre-service teachers in their CT skills [33]. ...
Conference Paper
Full-text available
Computational thinking is considered an important skill set for 21st-century learners and became a subject of focus in K-12 education in recent years. It cultivates problem-solving and algorithmic thinking and can be helpful in wider aspects of everyday life, besides programming and computer science. In this paper, we investigated what is the Greek Primary and Secondary School Teachers’ understanding and awareness as far as Computational Thinking is concerned. Since teachers are the agents of change, it is critical to find out how familiar and/or skilled they are with the Computational Thinking notion. Thus, we applied a qualitative questionnaire all over the whole Greek State where 406 teachers answered. The study led to a number of interesting conclusions regarding the teacher’s readiness, as well as more generic aspects according to their profile and faculty.
... According to Sands et al. (2018), computational thinking is a problem-solving approach that leverages computer science applications. This approach relates to ideas and applications in the field of learning, from parsing complex tasks and problems to solving them, to using automation to increase solution speed and efficiency. ...
Full-text available
In this study, the role of science and computational thinking (CT) in teaching self-efficacy and design thinking variables were examined to explain the technological pedagogical content knowledge (TPACK) knowledge forms needed by science teachers for integrated Science, Technology, Engineering and Mathematics (STEM) within the framework of the TPACK framework. 216 teachers working as science teachers in Turkey participated in the research. In the study, data were collected in an electronic form consisting of five parts. The model proposed in the research was tested with the partial least squares-structural equation modeling (PLS-SEM) method. The research showed that the self-efficacy of science teachers was related to technological pedagogical engineering knowledge (TPEK), T- integrated (I) STEM, and technological pedagogical science knowledge (TPSK). In addition, the self-efficacy of science teachers is also effective in design thinking. CT teaching self-efficacy has a positive effect on design thinking and the development of technological pedagogical mathematics knowledge (TPMK), TPEK, and TPSK structures. Design thinking skill is also related to TPMK, TPEK, and TPSK structures. These results can be a guide to ensure the effectiveness of professional development programs that will be prepared to improve science teachers’ integrated STEM competencies.
... An important challenge in delivering CT in K-12 is the shortage of teachers with sufficient knowledge of the subject (Mason & Rich, 2019). Most teachers have no background in CS (Yadav et al., 2016), and many do not know how to embed CT into their teaching (Sands et al., 2018). Several PD programmes in CT have been implemented and have been found to enhance teachers' knowledge, beliefs, and attitudes (e.g., Bower et al., 2017;Nugent et al., 2020). ...
Recent studies have revealed that the existing measurement methods related to computational thinking (CT) pivot on gauging thinking skills, recommending an extended understanding of CT as disposition. Disposition reflects inclination towards learning CT and indicates the interest to think intelligently about issues confronting them. Hence, the aim of this chapter is to assess students' affection towards learning CT as problem solving tool that can transform knowledge more productively. In the context of the affective domain, attitudes and beliefs can be regarded of as generic responses to something, the core quality of an emotion, feeling, mood, or temperament, and hence as affective mental activities. The framework of the CT disposition proposed in this chapter was developed based on tripartite classification of mental activities known as of trilogy of mind: cognitive, affective, and conative. The basic tenet of this chapter is aligned with the theoretical underpinnings of thinking dispositions which is expected to suit different contexts and needs.
Conference Paper
Computational thinking is one of the skills that gained attention in the 21st century. Several countries have included computational thinking in the curriculum at almost every level, from pre-school to university. There are two types of activities found when practicing computational skills in the classroom: unplugged and plugged. The most common activity found when unplugged and plugged is using a game learning environment or game-based learning. Despite many articles on the adoption of game-based learning in computational thinking skills, there may still be a deficiency of explanations for implementation at the middle school level. Therefore, the study used a literature review method that analyzed journal articles and books from trusted databases using keywords related to recent periods. This study obtained three main topics: learner as player vs. designers, teachers’ skills in a learning environment of the game, and assessment of learning. The study's findings revealed that the game activities and learning assessments used determine the computational thinking ability. Future teacher competency training, particularly computational thinking in a game-based learning environment, must be well planned.
Full-text available
The increased push for teaching computer science (CS) in schools in the United States requires training a large number of new K-12 teachers. The current efforts to increase the number of CS teachers have predominantly focused on training teachers from other content areas. In order to support these beginning CS teachers, we need to better understand their experiences and challenges encountered in the classroom. This study investigated U.S. CS teachers' perspectives on the demands of teaching computer science and support needed to ensure quality teaching. Results suggested that teachers face a number of challenges, including isolation, lack of adequate computer science background, and limited professional development resources.
Full-text available
The recent focus on computational thinking as a key 21st century skill for all students has led to a number of curriculum initiatives to embed it in K-12 classrooms. In this paper, we discuss the key computational thinking constructs, including algorithms, abstraction, and automation. We further discuss how these ideas are related to current educational re- forms, such as Common Core and Next Generation Science Standards and provide specific means that would allow teachers to embed these ideas in their K-12 classrooms, in- cluding recommendations for instructional technologists and professional development experts for infusing computational thinking into other subjects. In conclusion, we suggest that computational thinking ideas outlined in this paper are key to moving students from merely being technology-literate to using computational tools to solve problems.
Full-text available
Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include “computational thinking” as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.
Full-text available
Recent attention has been brought to light in the United States regarding low numbers of students pursing STEM (Science, Technology, Engineering and Math) disciplines and degree programs (National Science Board, 2010). There is a great need in America for talented scientists and engineers. Numerous programs abound for high school and middle school students in regard to STEM initiatives; however, fewer opportunities exist for elementary students and their teachers. Research has shown that early exposure to STEM initiatives and activities positively impacts elementary students' perceptions and dispositions (Bagiati, Yoon, Evangelou, & Ngambeki, 2010; Bybee, & Fuchs, 2006). By capturing students' interest in STEM content at an earlier age, a proactive approach can ensure that students are on track through middle and high school to complete the needed coursework for adequate preparation to enter STEM degree programs at institutions of higher learning. As a result, programs focusing on STEM initiatives and content are a growing priority in American schools with aims to provide early exposure for elementary students.
Full-text available
Various aspects of computational thinking, which builds on the power and limits of computing processes, whether they are executed by a human or by a machine, are discussed. Computational methods and models are helping to solve problems, design systems, and understand human behavior, by drawing on concepts fundamental to computer science (CS). Computational thinking (CT) is using abstraction and decomposition when attacking a large complex task or designing a large complex systems. CT is the way of thinking in terms of prevention, protection, and recovery from worst-case scenarios through redundancy, damage containment, and error correction. CT is using heuristic reasoning to discover a solution and using massive amount of data to speed up computation. CT is a futuristic vision to guide computer science educators, researchers, and practitioners to change society's image of the computer science field.
Addressing unresolved questions concerning computational thinking.
Conference Paper
This study explores the relationship between computational thinking, teaching programming, and Bloom's Taxonomy. Data is collected from teachers, academics, and professionals, purposively selected because of their knowledge of the topics of problem solving, computational thinking, or the teaching of programming. This data is analysed following a grounded theory approach. A computational thinking taxonomy is developed. The relationships between cognitive processes, the pedagogy of programming, and the perceived levels of difficulty of computational thinking skills are illustrated by a model. Specifically, a definition for computational thinking is presented. The skills identified are mapped to Bloom's Taxonomy: Cognitive Domain. This mapping concentrates computational skills at the application, analysis, synthesis, and evaluation levels. Analysis of the data indicates that abstraction of functionality is less difficult than abstraction of data, but both are perceived as difficult. The most difficult computational thinking skill is reported as decomposition. This ordering of difficulty for learners is a reversal of the cognitive complexity predicted by Bloom's model. The plausibility of this inconsistency is explored. The taxonomy, model, and the other results of this study may be used by educators to focus learning onto the computational thinking skills acquired by the learners, while using programming as a tool. They may also be employed in the design of curriculum subjects, such as ICT, computing, or computer science.
The term "STEM education" refers to teaching and learning in the fields of science, technology, engineering, and mathematics. It typically includes educational activities across all grade levels- from pre-school to post-doctorate-in both formal (e.g., classrooms) and informal (e.g., afterschool programs) settings. Federal policymakers have an active and enduring interest in STEM education and the topic is frequently raised in federal science, education, workforce, national security, and immigration policy debates. For example, more than 225 bills containing the term "science education" were introduced between the 102th and 112th congresses. The United States is widely believed to perform poorly in STEM education. However, the data paint a complicated picture. By some measures, U.S. students appear to be doing quite well. For example, overall graduate enrollments in science and engineering (S&E) grew 35% over the last decade. Further, S&E enrollments for Hispanic/Latino, American Indian/Alaska Native, and African American students (all of whom are generally underrepresented in S&E) grew by 65%, 55%, and 50%, respectively. On the other hand, concerns remain about persistent academic achievement gaps between various demographic groups, STEM teacher quality, the rankings of U.S. students on international STEM assessments, foreign student enrollments and increased education attainment in other countries, and the ability of the U.S. STEM education system to meet domestic demand for STEM labor. Various attempts to assess the federal STEM education effort have produced different estimates of its scope and scale. Analysts have identified between 105 and 252 STEM education programs or activities at 13 to 15 federal agencies. Annual federal appropriations for STEM education are typically in the range of $2.8 billion to $3.4 billion. All published inventories identify the Department of Education, National Science Foundation, and Health and Human Services as key agencies in the federal effort. Over half of federal STEM education funding is intended to serve the needs of postsecondary schools and students; the remainder goes to efforts at the kindergarten-through-Grade 12 level. Much of the funding for post-secondary students is in the form of financial aid. Federal STEM education policy concerns center on issues that relate to STEM education as a whole-such as governance of the federal effort and broadening participation of underrepresented populations-as well as those that are specific to STEM education at the elementary, secondary, and postsecondary levels. Governance concerns focus on perceived duplication and lack of coordination in the federal effort; broadening participation concerns tend to highlight achievement gaps between various demographic groups. Analysts suggest a variety of policy proposals in elementary, secondary, and postsecondary STEM education. At the K-12 level, these include proposals to address teacher quality, accountability, and standards. At the post-secondary level, proposals center on efforts to remediate and retain students in STEM majors. This report is intended to serve as a primer for outlining existing STEM education policy issues and programs. It includes assessments of the federal STEM education effort and the condition of STEM education in the United States, as well as an analysis of several of the policy issues central to the contemporary federal conversation about STEM education. Appendix A contains frequently cited data and sources and Appendix B includes a selection of major STEM-related acts.