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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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