Teaching Computational Thinking Through a Cross-Curricular

Approach Supported by Programming Patterns

Deller Ferreira

, Cássio Martins

, Samuel Costa

and Dirson Campos

Institute of Informatics, Feredal University of Goiás, Campus Samambaia, Goiania, Brazil

Keywords: Computational Thinking, Cross-Curricular, Programming Patterns.

Abstract: Computational thinking means thinking or solving problems like computer scientists. It refers to the thought

processes needed to understand problems and formulate solutions, making it a crucial skill for success in

today’s world. Therefore, it is essential that schools provide students with the necessary skills to think

logically and solve problems. However, there is little knowledge among teachers about computational

thinking, and some misconceptions about it suggest a demand for the term to be better explored in the context

of initial teacher training. In this research, design-based research was used to develop teaching strategies and

tasks for elementary students, involving programming patterns to develop computational thinking skills cross-

curricularly. Six teachers positively evaluated a questionnaire analysing the strategies and tasks regarding

clarity, compatibility, productivity, technological role, scope, and student focus. The set of cross-curricular

teaching strategies involving programming patterns to develop thinking skills presented in this research

constitutes an innovative and effective approach to teaching computational thinking in a contextualized,

integrated, and systematic way.

1 INTRODUCTION

According to Hsu et al. (2018), research on

computational thinking (CT) has increased over the

last ten years. Teaching CT is a way to train students

to be more than just consumers of technology. CT can

be seen as a gathering of concepts and tools from

computer science that are applicable in solving real-

world problems. Integrating computational thinking

into the curriculum can help students develop 21st-

century skills such as creativity, critical thinking, and

problem-solving.

CT (Computational Thinking) means thinking or

solving problems like computer scientists. CT refers

to the thought processes required to understand

problems and formulate solutions. CT involves logic,

evaluation, decomposition, automation, and

generalization. According to Wing (2008),

computational thinking is a type of analytical

thinking. CT involves skills necessary to participate

in the digital world and can be applied in various

https://orcid.org/0000-0002-4314-494X

https://orcid.org/0009-0005-8967-7134

https://orcid.org/0009-0009-4356-6710

https://orcid.org/0000-0002-0878-8336

disciplines and contexts, including computer science,

mathematics, sciences, and the humanities (Wing,

2006). Overall, computational thinking is a problem-

solving process that emphasizes breaking down

complex problems into smaller parts, recognizing

patterns, developing algorithms, and using

automation to solve problems across multiple

domains.

CT is an interconnected set of skills and practices

for solving complex problems, a way to learn topics in

many disciplines, and a necessity for full participation

in a computational world (Yadav et al., 2017). CT is

seen as an important competency necessary for

adapting to the future. However, educators, especially

elementary school teachers and researchers, have not

clearly identified how to teach it (HSU et al., 2018).

Yadav et al. (2017) revealed that pre-service teachers

without prior exposure to CT have a superficial

understanding of computational thinking.

Despite several resources and tools being

available to help educators integrate computational

Ferreira, D., Martins, C., Costa, S. and Campos, D.

Teaching Computational Thinking Through a Cross-Curricular Approach Supported by Programming Patterns.

DOI: 10.5220/0013137700003932

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 17th International Conference on Computer Supported Education (CSEDU 2025) - Volume 2, pages 641-648

ISBN: 978-989-758-746-7; ISSN: 2184-5026

641

thinking into an interdisciplinary approach, there are

challenges and opportunities to be uncovered in

integrating it throughout elementary education, with

promising practices and strategies to be discovered

for moving computational thinking from concept to

deep integration across different disciplines.

As seen, the importance of CT extends beyond

computing, and it should be integrated into cross-

curricular approaches. Due to the importance of CT

in disciplines such as mathematics, social studies,

language, and sciences (Wing, 2006), involving its

underlying concepts, benefits, surrounding issues,

forms of assessment of students’ understanding of

these concepts, and approaches for applying this

concept in elementary education, as well as the great

interest of researchers and educators on the subject,

the motivation arose to develop a cross-curricular

teaching approach.

A promising approach is the use of programming

patterns as a cross-curricular strategy for teaching

computational thinking. In this research, we consider

a cross-curricular approach to be a teaching approach

that spans different areas of knowledge present in the

school curriculum. A programming pattern, simply

put, is a way of solving a recurring problem, that is, a

common solution for a particular problem (Proulx

2000).

In this context, the objective of the present

research is to create teaching strategies for elementary

students, involving programming patterns to develop

computational thinking skills cross-curricularly.

2 LITERATURE REVIEW

Recent developments highlight the importance of

developing interdisciplinary work skills, where

students learn to meaningfully relate computational

concepts across different disciplines (Celepkolu et al.,

2020). The existing literature supports the inclusion

of computational thinking (CT) in elementary school

curricula across various subjects, starting from early

childhood education. This approach requires students

to learn how to use CT in ways that allow them to

apply what they have learned to different domains

(Chakarov et al., 2019).

From the student's perspective, despite its overall

effectiveness, the transfer of skills between different

subjects can be challenging, especially for younger

students. An open challenge for computer science

education researchers is to develop a deep

understanding of the student experience in integrating

CT across disciplines (Celepkolu et al., 2020). From

the teacher's perspective, teaching CT in a cross-

curricular manner can also be challenging. In Yadav

et al.'s (2017) study, 134 pre-service teachers were

asked about their views on computational thinking

and its role in teaching CT in elementary classrooms.

The goal of the research was to understand pre-

service teachers' perceptions of CT in their specific

areas and to assess how they would implement it in

their future classrooms. The results indicated that

elementary school teachers have only a superficial

understanding of computational thinking.

Carvalho and Braga (2022) corroborate this result

by noting that there is still little knowledge among

teachers about CT, and some inadequate

understandings suggest a need for the term to be

better explored in the context of initial teacher

education. Falcão and França (2021) also point out

the lack of training in CT, tied to the low level of

digital literacy among Brazilian teachers.

To meet these demands, the computer science

education community has long been investigating

best practices to prepare students for the essential

skills needed in a computer-dependent world. Barr

and Stephenson (2011) related computational

thinking (CT) to various fields, identifying and

exemplifying core CT concepts and strategies applied

across different disciplines. For example, problem

decomposition was mapped to science through

species classification and to language instruction

through outlining. The concept of abstraction was

applied to language by writing a branching story,

mapped to social studies by summarizing facts and

drawing conclusions, and contextualized in physics

by constructing a model of a physical entity.

In the research of Goldberg et al. (2012),

elementary and middle school students were

introduced to computational thinking and computer

science concepts, including algorithms, graph theory,

and simulations in interdisciplinary contexts,

reflecting how computing technologies are used in

research and industry. Computing was embedded into

courses students were already taking, including art,

biology, health education, mathematics, and social

studies.

Souza and Menezes (2023) developed

computational thinking strategies through a cross-

curricular pedagogical framework for fifth-grade

students, countering the skills of the National

Common Core Curriculum in the areas of languages,

natural sciences, and mathematics. For example, to

explore everyday phenomena that demonstrate

physical properties of materials, such as density,

thermal and electrical conductivity, responses to

magnetic forces, solubility, responses to mechanical

forces, among others, they proposed the creation of

CSEDU 2025 - 17th International Conference on Computer Supported Education

642

an algorithm to inquire about the material by asking:

What is the product's consistency? Is it malleable or

not? What is its resistance (is it degradable in a few

days, weeks, months)? Where is it used?

Güven and Gulbahar (2020) provided assistance

to social studies teachers regarding how CT can be

integrated into elementary school classrooms. For

example, to encourage students to think abstractly,

they suggest assigning tasks such as creating a model

of cultural changes, urbanization, and population

growth. Regarding decomposition, teachers can ask

students to identify the main reasons and

consequences for differences in population

distribution within and between countries. For

algorithmic thinking, teachers can ask students how a

certain bill became law. Students can study the steps

necessary for a proposed bill to become law and then

draw an algorithmic flowchart.

Ragonis and Shilo (2018) conducted a study

where the results showed that students' understanding

of argumentative texts improved after learning

programming logic and the teacher applied an

interdisciplinary learning facilitation technique

through analogies between the structure components

of an argument and commands in an algorithm.

A different way of thinking about CT is to move

beyond its abstract definitions toward a more

pragmatic conceptualization. Basawapatna et al.

(2011) applied CT to science teaching through

analogies between programming patterns and science

simulations. Students and teachers who participated

in a summer game development course were given a

CT questionnaire. This questionnaire tested the

participants' ability to recognize and understand

patterns in a science context. They found that most

participants were able to comprehend and recognize

the patterns in various contexts.

Yadav et al. (2017) argue that there is a set of CT

concepts that allow introducing CT concepts into

other areas of knowledge; these concepts include data

collection, data analysis, and data representation.

According to the authors, a social studies teacher can

use data, such as the most used words in presidential

inaugural speeches over a period of time, and students

can analyze the differences between speeches across

time periods or between presidents of different

parties. The ability to make sense of a dataset to solve

a problem is a fundamental CT skill.

Lee et al. (2011) argued that CT shares elements

with several other types of thinking, and one of these,

according to the authors, is mathematical thinking. In

their mapping, Barr and Stephenson (2011)

associated CT with processes used in solving

mathematical problems, such as performing long

division or factorization, where each step can be

guided by a logical and well-defined reasoning. The

authors also associated certain skills that are practiced

and developed through CT education and can be

applied in mathematics, such as problem

decomposition. For example, applying an order of

operations in solving an expression.

CT has been offered as an interdisciplinary set of

mental skills derived from the discipline of computer

science. However, the approaches found in the

literature lack an explicit correlation of other areas

with computing and an interconnection between the

different domains. The use of programming patterns

allows for an integrated view of CT across different

disciplines, thus facilitating the transfer of CT skills

to distinct contexts.

Using patterns during the teaching and learning

process allows students to accelerate the development

of skills such as abstraction, problem decomposition,

and identifying a recurring problem (Proulx, 2000),

which are fundamental skills related to computational

thinking. Besides these concepts, the research

addresses cross-curricular integration, relating CT not

only to computing but in an integrated manner.

A cross-curricular approach seeks to go beyond

the existing space of each discipline in order to

produce knowledge and learning, connecting learning

to people's lives. Programming patterns can be

instantiated in different disciplines, from the most

common ones like mathematics to even physical

education (Leal & Ferreira, 2016), thus allowing a

single computational solution to be viewed from

different perspectives. In other words, students can

apply the same solution and think computationally

across different disciplines in a uniform way.

There is little work on the teaching of

programming patterns, and in the specialized

literature, as far as we know, no work addresses the

teaching of programming patterns combined with the

teaching of computational thinking in a cross-

curricular manner. Thus, this research represents a

relevant and original contribution to CT education.

3 METHODOLOGY

This research used a qualitative research

methodology based on the design-based research

(DBR) method. The basic process of DBR involves

developing solutions to problems. There are different

ways to describe DBR found in the literature. In this

work, the model chosen was that of Romero-Ariza

(2014). The approach proposed by Romero-Ariza

involves three main phases: a preliminary

Teaching Computational Thinking Through a Cross-Curricular Approach Supported by Programming Patterns

643

investigation phase where the needs and the problem

are clarified, a development and implementation

phase, involving progressive improvement in

iterative cycles of prototypes aimed at achieving the

research objective, and a final evaluation phase to

validate whether the result obtained is consistent with

the defined objective.

3.1 Methodological Steps

For this research, DBR was applied to develop a set

of strategies for teaching computational thinking in a

cross-curricular manner in elementary education. The

structure of the phases and steps of the research,

aiming to meet the characteristics of the DBR

approach according to the model proposed by

Romero-Ariza, are described as follows:

Phase 1: Preliminary Investigation

Literature review on cross-curricular approaches to

CT. Application of questionnaires to analyze

teachers' familiarity with CT and cross-curricular

approaches.

Phase 2: Development and Implementation

Initial development of a set of strategies using cross-

curricular programming patterns. Definition of a

context for cross-curricular application of the

strategies. Initial development of tasks to implement

the developed strategies. Application of a

questionnaire to evaluate the tasks by teachers

regarding efficiency and effectiveness.

Reformulation of tasks in a participatory manner with

teachers.

Phase 3: Final Evaluation

Application of a questionnaire to analyze the

strategies and tasks by teachers in terms of their

clarity, compatibility, productivity, technological

role, scope, and student focus (Kimmons et al., 2020).

4 RESULTS

4.1 Strategies for Using Programming

Patterns in a Cross-Curricular

Manner

The main contribution of the research is the

development, with the participation of teachers, of a

set of strategies and tasks for teaching computational

thinking (CT). From these strategies, a set of

activities is created using a systematized and

integrated approach to teach CT in a cross-curricular

way. These activities consist of tasks developed for

different subjects beyond computer science. The

patterns used in this work can be found on the

Elementary Patterns Home Page (Wallingford, 2001).

As an example of a programming pattern, we have

sequential choice. Sequential choice addresses a

situation where exactly one of several possible

actions must be chosen, but the action does not

depend on the value of a single expression. Instead,

suppose each action depends on a separate testable

condition. All the subjects involved are addressed in

a cross-curricular, integrated, and systematized

manner. This integration occurs by contextualizing a

real-world theme and its problematization in the

subjects, as well as by using a common approach

across subjects through programming patterns.

Problematization is systematically presented through

the application of strategies that have a common

denominator, which is the programming pattern. This

systematized integration allows students to visualize

a cross-curricular application of CT, making the

learning process more meaningful. The strategies are

presented below.

4.1.1 Understanding Programming Patterns

In the understanding strategy, the activity begins in

the computer lab and later continues in other subjects.

In this work, the Scratch programming language was

used. Understanding tasks are divided into two

subtypes: tasks involving the use of patterns and tasks

linked to anti-patterns. Here, anti-patterns are

considered as common erroneous solutions that have

a correct part.

By applying patterns in other subjects, students

expand their understanding of a concept through

analogies with other constructs. This strategy is

related to overcoming and visualizing concepts and

ideas in a broader way. Seeing an idea in different

contexts and also seeing ideas in a larger scenario is a

way to overcome conceptual barriers. Considering

ideas in new contexts is a way of perceiving other

possible uses and meanings. This type of task is

related to the flexibility of divergent thinking.

The use of anti-patterns takes advantage of the

way bad ideas become beneficial deviations for good

ideas. Students do not only reflect on positive

impacts, relevant implications, or good

characteristics but also reflect on why a failure

occurred, on the impacts, characteristics, and negative

implications. They do not just eliminate the wrong

paths but reflect and take advantage of them. Students

transform ideas and concepts into new interpretations,

also thinking about mistakes. Furthermore, anti-

patterns can reflect partially correct solutions that are

CSEDU 2025 - 17th International Conference on Computer Supported Education

644

associated with more simplistic thinking, making

them easier for students to understand. From the

student's understanding of the anti-pattern, the

teacher moves on to a second explanation of how to

correct it.

4.1.2 Recognizing Programming Patterns

The recognition strategy can be adopted in any

subject and involves tasks in which students must

identify one or more previously addressed patterns

within a presented solution. The goal is for students

to exercise the ability to identify situations where a

pattern can be applied to solve a problem more

quickly or improve a solution. In this type of task,

analogical reasoning is applied to problem-solving.

Analogical reasoning is one of the most important

problem-solving heuristics. It is related to transferring

solutions from previously known problems to new

ones and the ability to abstract similarities and apply

productive past experiences to new situations. When

students examine problems similar to familiar

structures, they gain more robust conceptual

knowledge about the problems, building a stronger

problem schema.

4.1.3 Adapting Programming Patterns

The goal of the adaptation strategy is for students,

having been introduced to content in previous classes,

to deepen or enhance their knowledge of that content.

In these classes, students will be challenged to adapt

some activity, creating something different and new

from what they have already seen and discussed. The

adaptations can be minor or major. The adaptation

strategy can be used in any subject.

This strategy is related to the divergent thinking

skills of elaboration and fluency. Elaboration and

fluency are two fundamental components of the

creative process. The teacher can encourage students

to improve these skills by making explicit what is

already there but hidden, as well as dealing with the

elements of who, what, why, and how of solution

ideas. Students uncover opportunities by searching

for attributes and relationships between concepts and

new ideas, and they try to organize and reorganize the

information.

4.1.4 Combining Programming Patterns

The combination strategy can be applied in any

subject. In the combination strategy, the goal is for

students to apply more than one pattern within a

single solution. The way students can organize these

patterns can be done sequentially or with one pattern

as part of another.

This strategy is also related to the fluency skill,

just like the adaptation strategy. Additionally, it is

related to the problem-solving processes of

decomposition and the "divide and conquer"

paradigm. Simply put, problem decomposition aims

to separate or divide a complex problem into smaller

problems, making each problem's solution easier.

Hence, the idea of "divide and conquer" comes into

play. It is a paradigm that breaks a complex problem

into small subproblems, and after solving each

subproblem, the solutions are combined to solve the

initial problem.

4.2 Tasks Based on Strategies for Using

Programming Patterns in a

Cross-Curricular Manner

The chosen context for cross-curricular application of

the strategies was COVID-19. The theme of COVID-

19 was used to outline various problems for students

to solve in different subjects. 24 tasks were developed

as practices for applying patterns in a cross-curricular

way, four for each involved subject, which were

computer science, science, physical education, social

studies, languages, and mathematics. Below are six

sample tasks.

The moving average is a tool that helps to

understand how COVID is behaving. It is calculated

by summing the number of cases from the last 7 days,

and after summing, it is necessary to divide this

amount by 7. As an example, see Table 1.

Table 1: Evolution of COVID-19 Case Numbers.

Day of

the

Week

Mond

Tuesday Wednesday Thursday Friday Saturday Sunday

Number

of cases

10 20 25 30 38 45 56

In this presented scenario, the moving average for

Sunday is the sum of the number of cases from the

last 7 days:

S = 10 + 20 + 25 + 30 + 38 + 45 + 56

S = 224

Division of the sum by 7:

M=224/7

M = 32

The teacher should clarify what the moving average

is by discussing questions such as:

• Why should we sum all the values?

• Why should we divide by 7?

Teaching Computational Thinking Through a Cross-Curricular Approach Supported by Programming Patterns

645

The teacher should discuss questions that make

sense of the formula used to calculate the moving

average.

After introducing and discussing the concept and

formula of the moving average, ask the students to

write a sequence of instructions to calculate the

moving average of the number of COVID cases over

the last 7 days. The accumulator pattern should be

adapted and combined with the sequential pattern

when writing the instructions.

After constructing the sequence, students should

execute it and discuss its operation.

4.3 Interaction with Teachers

Six elementary school teachers from the subjects of

informatics, mathematics, sciences, social studies,

physical education, and languages participated in the

research.

4.3.1 Precedents and Context Analysis

41.7% of teachers know or have used programming

or computational thinking (CT) terms. 41.7% know

or have used these terms to a limited extent. 16.6% do

not know or have not used programming or CT terms.

41.7% were familiar with CT or programming

terms, 41.7% had little knowledge of them, and

16.6% were unfamiliar with the terms.

Even though 58.3% had at least some knowledge

of the terms, only 41.7% had experience with

programming and CT in class. Of these, only those in

the informatics discipline had more solid and

significant experiences with CT and programming in

teaching, using educational software to teach basic

computer principles. Still, all teachers, even briefly

introduced to CT, showed interest in working with the

concept in their subjects.

For CT integration to occur effectively, the

teacher must be motivated and engaged in using

computational thinking in their elementary school

subject. The results presented a predisposition to seek

new pedagogical strategies using CT that can enrich

student learning.

In the set of practices proposed for this research,

one concept used to make CT understanding and

learning more meaningful is transversality. 60.8% of

teachers had experiences with transversal teaching

approaches, while 33.2% had not. 60.8% of teachers

were interested in applying transversal approaches,

and 16.6% were not.

The teachers reported what they knew and their

experiences with transversal teaching approaches. Of

the total participants, 60.8% had experiences with

transversal teaching approaches, but not all had

positive experiences. Some reported that in their

experiences, they participated in groups with students

from different grade levels, and the disparity between

knowledge levels limited teamwork.

Regarding interest in working with transversal

approaches, 83.4% declared interest. 16.6% did not

state whether they were interested or not, as they were

unfamiliar and had no experience with transversal

approaches. The results showed that teachers are

motivated to promote more integrated learning that

connects different areas of knowledge and allows

students to have a broader and more complex

understanding of the content.

4.3.2 Formative Evaluation of Activities

A formative evaluation of the set of tasks for teaching

CT was conducted. Based on the data collected from

the precedents and context analysis, researchers and

teachers proposed a set of requirements to guide the

evaluation of the activities. This set of requirements

is presented in Table 2.

Table 2: Transversality Precedents.

Regarding the set of tasks

Regarding individual

tasks

- Does it teach and

encourage the practice of

CT?

- Does it address CT?

- Is it transversal?

- Does it address patterns

and/or anti-

atterns?

- Were the types of tasks

explored?

- Does it explain the

concepts it will cover?

- Does it track the

continuous and cumulative

evolution of student

erformance?

- Does it engage with the

cognitive capacity of

elementary school

students?

After the teachers studied and analyzed the set of

activities, they identified which requirements were

met and which still needed to be achieved. After this

evaluation, the researchers improved the set to meet

the unmet requirements, and then a new evaluation

was conducted. This cycle was repeated twice until

all the requirements were fulfilled for the set of

activities.

4.3.3 Final Evaluation of the Strategies and

Activities

Once the refinement cycles of the proposed set of

practices were completed, all participating teachers

were invited to answer a questionnaire for the final

evaluation of the activities. 83.4% of the teachers

considered the strategies for applying computational

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646

thinking (CT) in their discipline sufficiently simple.

100% considered the strategies for applying CT in

their discipline clear. 83.4% did not consider that the

strategies for applying CT in their discipline had

hidden complexities. 100% considered that the

strategies for applying CT in their discipline

complement valuable existing educational practices.

100% considered that the strategies for applying CT

in their discipline support valuable existing

educational practices. 100% considered that the

strategies for applying CT in their discipline foster

productive thoughts as teachers struggle with

technology integration issues. 83.4% considered that

the strategies for transversal application of CT reduce

technology integration issues in their discipline.

83.4% did not consider that the strategies for

transversal application of CT in their discipline were

an end in themselves. 83.4% considered the strategies

for transversal application of CT sufficiently

parsimonious to ignore irrelevant aspects of

technology integration. 83.4% considered the

strategies for transversal application of CT in their

discipline sufficiently comprehensive to guide their

practice. 100% considered that the strategies for

transversal application of CT in their discipline

emphasize active student participation. 100%

considered that the strategies for transversal

application of CT could improve student outcomes in

their discipline. 100% could visualize the patterns in

other problems and contextualized situations in their

discipline beyond those presented.

Based on the data presented, it can be concluded

that the evaluation was positive in terms of clarity,

compatibility, productivity, technological role, scope,

student focus, and replicability.

5 CONCLUSIONS

Due to the importance of CT in subjects such as

mathematics, social studies, language, and physical

education in primary education, as well as the great

interest of researchers and educators in the topic, it

is essential to integrate transversal approaches to

teach CT.

In this research, teaching strategies were

developed for primary school students, involving

programming patterns to develop computational

thinking skills in a transversal way. This contribution

is a relevant and original one in teaching CT, as it

involves strategies and tasks that allow its application

in an integrated and systematic way across different

subjects.

The methodology used for developing the

research was Design-Based Research (DBR). DBR

offers a set of methods and methodological steps

when building educational artifacts.

Questionnaires were used to evaluate the

strategies by six teachers, one from each discipline

covered: computer science, mathematics, languages,

social studies, science, and physical education.

The results of the questionnaires showed that most

participating teachers considered the strategies for

applying CT in their discipline sufficiently simple,

clear, and complementary to existing educational

practices. Additionally, most believe that the

strategies for transversal application of CT foster

productive thoughts and can improve student

outcomes in their discipline. Most also consider the

strategies sufficiently parsimonious and

comprehensive to guide their practice, emphasizing

active student participation. However, a minority

believes that the strategies have hidden complexities

and are not an end in themselves. Furthermore, some

indicated that the strategies for transversal application

of CT may not reduce all technology integration

issues in their discipline. Finally, all teachers can

visualize the patterns in other problems and

contextualized situations in their discipline beyond

those presented.

The set of transversal teaching strategies

involving programming patterns to develop CT skills

presented in this research represents an innovative

and effective approach to teaching CT skills in a

practical and contextualized manner.

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