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Veerasamy A.K., Larsson P., Apiola MV., D’Souza D., Laakso MJ. (2021) Pedagogical 
Approaches in Computational Thinking-Integrated STEAM Learning Settings: A Literature 
Review. In: Arai K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 
285. Springer, Cham. https://doi.org/10.1007/978-3-030-80129-8_27 
The final authenticated version is available online at 
https://link.springer.com/chapter/10.1007/978-3-030-80129-8_27 
 
 
 
 
Pedagogical Approaches in Computational
Thinking-Integrated STEAM Learning Settings:
A Literature Review
Ashok Kumar Veerasamy1(B), Peter Larsson1, Mikko-Ville Apiola1, Daryl D’Souza2,
and Mikko-Jussi Laakso1
1 University of Turku, Turku, Finland
askuve@utu.fi
2 RMIT University, Melbourne, Australia
Abstract. This paper aims to provide a comprehensive analysis of pedagogical
approaches deployed in computational thinking (CT)-based STEAM curricula
during the period 2015–early 2020. Based on a set of suitable search keys for
querying the Scopus database we found 46 studies on CT-integrated STEAM
learning settings in K-12 schools and universities. Nearly 46% of the studies were
in K-12 science learning. Seven different pedagogies were used to introduce CT in
STEAM (science, technology, engineering, arts and mathematics) environments.
Collaborative learning, hands-on and learning by modelling activities, were found
to be the main approaches in CT-integrated STEAM learning research settings. In
addition, most of these studies used computing principles to teach CT + STEAM
topics. However, the roles of pedagogies used in these studies were not clearly
stated. Furthermore, CT principles in STEAM learning were not well-defined.
Hence, our study provides evidence that it is critical to develop a possible inven-
tory of successful pedagogies and supporting learning activities for CT-integrated
learning environments.
Keywords: Computational thinking based STEAM learning · Collaborative
pedagogy · Learning by modelling
1 Introduction
Computational thinking (CT) involves skills or techniques that include defining prob-
lems, collecting, analysing, identifying, evaluating and providing possible solutions in
computational form for complex problems [1]. CT is considered as one of the required
fundamental skill sets of the twenty-first century and relevant to all disciplines, including
mathematics, science and humanities [2]. Integrating CT in school curricula has received
much attention in educational research and practice. Many universities offer CT based
courses for their STEAM students [3]. Furthermore, schools have already started intro-
ducing CT-integrated learning for STEAM subjects such as science, maths and crafts
into their K-12 curricula [4]. CT-integrated STEAM learning environments have proved
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
K. Arai (Ed.): Intelligent Computing, LNNS 285, pp. 1–21, 2021.
https://doi.org/10.1007/978-3-030-80129-8_27
2 A. K. Veerasamy et al.
to be an effective method for learning and understanding STEAM domains and help-
ing students to develop their key CT concepts and practices [5]. There has been much
research on CT in STEAM learning for over the past decade [6]. Significantly, various
pedagogical approaches and learning activities have been used to couple CT concepts
to STEAM learning environments. In addition, several reviews of literature have been
conducted on CT in education [7–9]. However, no previous studies have examined or
listed a concrete inventory for use as a possible pedagogical approach for CT-integrated
STEAM learning environments. Moreover, the influence of pedagogical approaches used
in CT-integrated STEAM learning is not clearly defined in the literature. As such, this
paper presents a more current review of pedagogical approaches; including learning
activities used in CT-integrated STEAM learning in school and university contexts. The
objective of this review is to provide a bibliographic evidence for researchers and educa-
tors to gain a better understanding of existing CT-integrated pedagogical approaches in
STEAM learning. Furthermore, the scope of this research is to provide a possible guide
for educators to develop effective teaching strategies for CT-integrated STEAM learn-
ing environments. Consequently, we pose and address the following research questions
(RQs).
RQ1. What are the pedagogical approaches used in CT-integrated STEAM learning?
RQ2. What are the teaching and learning activities implemented in CT-integrated
STEAM learning environments?
Towards addressing these questions, this paper is organised as follows. Section 2
presents some prior studies that have attempted systematic literature reviews of STEAM
learning through CT activities. Section 3 presents the review method our study has
adopted. Section 4 presents the results and discussion of our literature review, to address
our research questions. Finally, Sect. 5 presents the conclusions and limitations in terms
of how well the foregoing research questions are answered.
2 Previous Studies
Previous studies have attempted to provide comprehensive analyse of CT practices in
STEAM learning over a decade [7, 9–12]. For example, a recent systematic review of
CT examined the publication trends and research typology (2006–2018) and reported
that game and peer collaboration were found to be the main pedagogies in CT-integrated
classrooms [7]. Similarly, Barcelos et al. reviewed the articles published from 2006 to
2017 to evaluate the learning outcomes of mathematics learning through CT activities [9].
Ching et al. provided a list of educational technologies for young learners to develop CT
skills in STEM learning [13]. Li et al. conducted a systematic review of STEM education
journal articles published (n = 798) between 2000 and 2018 inclusive. They identified
that of these only six articles focused on the connection between CT and STEM education
[11]. Tang et al. reviewed the CT assessments in education and identified that more
CT assessments are needed for school, college and vocational education development
programs [6]. However, none of these studies focused on pedagogical approaches or
educational theories to facilitate learning and understanding of STEAM domains which
in turn, helps students to develop both STEAM and CT key concepts and practices. Our
study aims to offer a list of pedagogical activities around CT that have been used in
STEAM learning environments.
Pedagogical Approaches in Computational Thinking 3
3 Methodology
The literature review process was carried out by Erasmus + research team members and
authors of this article. The working group reviewed the articles published and indexed
by Scopus [14] in the period start of 2015 to February 2020. The literature review
process was conducted on the basis of guidelines proposed by [15, 16]. The working
group followed the highly structured process steps described in Sects. 3.1 through to
3.5, below.
3.1 Defining and Implementing the Search Query
The search period was set in between January 2015 and February 2020. The database
chosen for the automatic search was The Scopus. Scopus database contains a large
volume of abstracts and citations of peer reviewed literature for papers that examine
CT concepts in STEAM learning settings [14]. The search term for CT in the STEAM
learning environment was defined by combining suitable key phrases to extract the
relevant articles for this study. Figure 1 shows the search string used for paper retrieval
via the Scopus database:
Fig. 1. Screenshot of search string used in the Scopus database
3.2 Defining Inclusion and Exclusion Criteria for the Review
The search phrase yielded 358 English articles relevant to CT practices in STEAM learn-
ing. As noted, the objective of this review was to identify and discuss the pedagogical
approaches used in CT-integrated STEAM learning environments for students to inte-
grate their CT skills in STEAM topics. As such, we defined the following inclusion and
exclusion criteria to select the final set of articles for full-text review.
Papers were excluded if they:
i. did not address educational theory or pedagogical approaches in the context of
CT-based STEAM learning;
ii. did not use the CT concepts/practices in the context of STEAM topics (one or more)
learning;
iii. were works in progress or project work research papers;
iv. were CT and or STEAM related literature review articles (but used to address gaps
in literature); and
v. were theoretical with no explorative studies.
4 A. K. Veerasamy et al.
3.3 Selection of Articles for Full-Text Review
The working group reviewed the abstracts of all articles retrieved (n = 358) individually,
based on the selection criteria and procedures established for this review. Three mem-
bers from the research group selected articles for full-text review. They independently
reviewed each paper and recorded their responses in a pre-formatted excel spreadsheet
with clear remarks (yes/no), and answered the question “Why did you select this article
for full text review?” The reviewers were invited for subsequent discussion meetings.
In total, three consecutive weekly review meetings were conducted with all members
of the working group, including reviewers, to select the final set of articles for full-text
review.
3.4 Extracting Data Based on Research Questions
As previously mentioned, in total 358 articles were initially selected for evaluation. Of
these, 312 articles were excluded based on reviewers’ feedback, including votes, remarks
registered in the excel spreadsheet, and agreed in the discussion meetings. A total of 46
articles were finally selected for full-text review.
3.5 Synthesizing and Classifying the Results
Following the selection of articles, the selected articles (n1 = 46) were divided among the
authors for full text-review. The reviewers classified the articles by STEAM categories
for full evaluation. The data extracted by each reviewer were synthesised for preparation
of review report to discuss further. In addition, the selected papers were classified based
on pedagogical approaches used in STEAM learning environments including teaching
and learning activities.
4 Data Analysis, Results and Discussion
The objective of this research was to list and identify the pedagogies adopted for CT-
integrated STEAM learning environments, for teaching STEAM concepts with CT.
Nearly 65% of studies were conducted in K-12 STEAM + CT learning settings. Table 1
presents the list of articles classified by STEAM categories for full-text review. Of the
46 articles classified nearly 46% examined the role of CT in science, and followed by
arts (17%), mathematics (15%) and STEAM (11%) courses. On the other hand, only
11% of articles addressed CT in technology and engineering topics.
We were interested in identifying the type of pedagogical approaches that were used
for C-integrated STEAM learning environments for the development of STEAM and
CT skills. As such, we classified our results based on pedagogies including teaching and
learning activities used in these studies to answer RQs 1 and 2. Table 2 presents the list
of pedagogies and type of learning activities used in CT-integrated STEAM learning
environment at school and universities.
Pedagogical Approaches in Computational Thinking 5
Table 1. List of articles (classified by STEAM category) selected for full-text review
STEAM category K-12 University References
Science
(basic science, physics, chemistry,
biology, early computer science
education, programming)
15 6 [5, 17–24] &
[25–30]*
[31–36]**
Technology 2 0 [37, 38]*
Engineering 1 2 [39]* [40, 41]**
Arts 6 2 [42–47]*
[48, 49]**
Mathematics 4 3 [50–53]* [4, 54, 55]**
STEAM*** 2 3 [56, 57]* [58–60]**
Total 30 16 46
* Research conducted at K-12 curricula.
** Research conducted at university (including vocational education).
*** Studies conducted on STEM or STEAM in general.
Table 2. Pedagogies and learning activities used in CT-integrated STEAM learning environments:
2015–February 2020
Reference Subject/topic K-12 HE Pedagogy | learning
activity
Science
[17] Animal learning
(Thematic unit)
1–2 Collaborative | task
based learning (puzzles,
matching the picture)
[33, 36] Elementary
Science
Primary (grades
3–5) school
teachers
Constructivist and
Collaborative |
Hands-on learning
[5] Physics 7–9 Collaborative | learning
by modelling
(synergistic learning)
[61] Physics 7–9 Collaborative | concept
maps, learning by
modelling and model
simulations as group
and scaffolding
(continued)
6 A. K. Veerasamy et al.
Table 2. (continued)
Reference Subject/topic K-12 HE Pedagogy | learning
activity
[21] Physics 7–9 Constructivist | learning
by modelling-tasks and
rubrics
[32] Physics Sophomore
students
Collaborative |
Synergistic learning
– learning by modelling
[23] Chemistry 7–9 Constructivist | Learn
by modelling and
simulation (Computer
based learning)
[20] Biology 9–12 Cognitive-behaviourist
pedagogy:
Restructuration theory |
Learn by modelling and
simulation
[35] Data science Non-CS students Situated learning theory
and Collaborative |
Project based learning
[28] Programming 7–9 Reflective | (game)
Project based learning
[22] Programming 7–9 Reflective | Portfolio
based learning and
assessment
[31] Programming Non-CS students Constructivist and
Collaborative | Task
based learning
[27] Programming 7–9 Constructivist and
Inquiry | Game based
learning and simulation
design
[34] Programming Primary and
secondary
education teachers
Constructivist and
Inquiry | Blended
learning
[30] Virtual reality
programming
7–9 Inquiry | Experiential
learning (Hands on
learning)
(continued)
Pedagogical Approaches in Computational Thinking 7
Table 2. (continued)
Reference Subject/topic K-12 HE Pedagogy | learning
activity
[18] Physics and
Computing
7–9 Constructivist and
Collaborative |
constructionist learning
(video game making)
[26] Physics &
Biology
6 Constructivist |
Learning by modelling
[29] Physics &
Biology
5 Constructivist |
Learning by modelling
(synergistic learning)
[25] Physics &
Biology
5–8 Constructivist |
Learning by modelling
and using simulation
(agent based modelling)
[24] Biology &
Chemistry
7–9 Collaborative |
Simulation and Problem
based learning
Technology & Engineering
[37] Robotics
technology
3–6 Collaborative | Hands
on learning
[38] Digital tools 3–6 Collaborative |
Challenge based
learning
[39] Programming and
Circuit
foundations
10–12 Collaborative | Hands
on learning
[40] Robotics class for
engineers
Both undergraduate
and graduate
engineering and IT
students
Collaborative | Project
based learning (active
learning) / computer
aided engineering
activities
[41] Programming and
CT for electronic
engineering
Electronic
engineering
students
Collaborative | Active
learning includes
project based learning
Arts
[42] Learning English 2 Inquiry | Active learning
includes visual learning
(continued)
8 A. K. Veerasamy et al.
Table 2. (continued)
Reference Subject/topic K-12 HE Pedagogy | learning
activity
[43] Design 7–9 Collaborative and
Inquiry | Inquiry-based
teaching activities
[44] Literature 9 (7–9) Collaborative | Project
based learning
[45] English 4–5 Constructivist: Papert’s
theory of
constructionism |
learning by making or
Hands on learning
[46] Music + CT 3–10 Collaborative | Hands
on learning includes
collaborative music
composition based
learning
[47] Smart textiles 3–6 Constructivist | Hands
on learning
[48] Social work 9–12 College students Collaborative and
Inquiry | Problem based
learning
[49] Music +
programming
Non-CS college
students
Constructivist |
Continuous assessment
based activities
Mathematics
[50] Preschool maths K Constructivist and
Collaborative | Hands
on activities based
learning
[51] Fractions and
shapes
3–6 Collaborative | Web and
game based learning
[52] Mathematical grid
diagrams
5–6 Collaborative and
Inquiry | Scaffolding
includes inquiry-based
learning
[53] Geometry 7–9 Collaborative | problem
based learning (probe a
set of mathematical
questions)
(continued)
Pedagogical Approaches in Computational Thinking 9
Table 2. (continued)
Reference Subject/topic K-12 HE Pedagogy | learning
activity
[54] surface area and
Volume- basic
maths
Vocational college
students
Micro learning / mobile
learning
[4] Arithmetic,
Geometry, algebra
K-12 math teachers Constructivist | Online /
e-learning | Pedagogical
essays and developing
animated exercises for
K-12 maths
[55] Geometry School teachers Collaborative |
e-learning and
e-assessments
STEAM
[56] Water
conservation and
environmental
sustainability
7–9 Culturally relevant
pedagogy | game based
learning
[57] STEM Robot 10–12 Collaborative | Hands
on learning based
exercises
[58, 60] Educational
robotics – STEM
pedagogy
School teachers Collaborative | Hands
on learning
[59] Teacher training
for STEAM and
CT
Pre-service K-8
teachers
Collaborative| Team
based learning, flipped
classroom and pair
learning
We identified seven pedagogical approaches (see Fig. 2) were used in CT-integrated
STEAM learning settings and they are: constructivist (20%), collaborative (44%), reflec-
tive (4%), inquiry (4%), cognitive behaviourist (2%), micro learning (2%), and cultur-
ally relevant pedagogy (2%) and combination of constructivist, collaborative and or
inquiry pedagogy (22%). Figure 2 presents the list of pedagogical approaches used in
CT-integrated STEAM studies.
10 A. K. Veerasamy et al.
Fig. 2. Pedagogical approaches used in the CT-integrated STEAM learning environments
4.1 Constructivist Pedagogy in CT-Integrated STEAM Learning
Constructivist pedagogical approach focuses on critical thinking and problem solv-
ing. It promotes student-centred learning and can be used to improve student CT and
STEM skills [62]. This pedagogy lets student interacts autonomously and enhance self-
awareness of the knowledge construction process which is one of the key elements of
CT. Figure 3 shows percentages of studies that used constructivist pedagogy in STEAM
learning environments (Fig. 3). Nearly 20% of studies used constructive pedagogy to
develop and enhance CT and STEAM skills.
Fig. 3. Constructivist pedagogy in CT-integrated STEAM learning
Pedagogical Approaches in Computational Thinking 11
4.2 Collaborative Learning Pedagogy in CT-Integrated STEAM Learning
Collaborative learning is an instruction method in which students work together as
pair or group for the purpose of achieving a common academic goals and outcomes.
Collaborative learning enables students to engage in analytical thinking and promotes
student-student and student-instructor interaction and interest. In turn, it enhances critical
thinking skills which are one of the key elements of CT and STEAM domains [63, 64]. In
addition, it is found that collaborative learning promotes student CT and programming
learning [65]. As noted, nearly 44% of the studies implemented a collaborative learning
pedagogy for enhanced synergistic learning and understanding of STEAM domains via
CT key concepts (Fig. 4).
Fig. 4. Collaborative pedagogy in CT-integrated STEAM learning
4.3 Reflective Learning Pedagogy in CT-Integrated STEAM Learning
Reflective pedagogy lets learners reflect on their learning experiences to increase their
self-awareness and support student-centered and experiential learning [66]. Two studies
(4%) implemented a reflective learning pedagogy to capture school students’ experiences
on using educational tools and self-reflected programming tasks for the improvement
of student programming and CT skills. However, this pedagogy was not used in other
STEAM topics. This should be analysed further (Fig. 5).
4.4 Inquiry-Based Learning STEAM in CT-Integrated STEAM Learning
Inquiry-based learning approach focuses on investigation and problem-solving which is
part of the key elements of CT and STEAM domains. Inquiry-based learning pedagogy
enables students to develop their analytical and reasoning skills. It encourages learners
to explore, interpret and reflect their findings and improve critical thinking and under-
standing in STEAM topics. In addition, Inquiry based learning approach improves inter-
est, self-determination, self-efficacy and student learning outcomes [67]. We identified
inquired based approach used in arts and science studies only (Fig. 6).
12 A. K. Veerasamy et al.
Fig. 5. Reflective pedagogy in CT-integrated STEAM learning
Fig. 6. Inquiry pedagogy in CT-integrated STEAM learning
4.5 Mixed and Other Pedagogies in CT-Integrated STEAM Learning Setting
Combining multiple pedagogies may boost student learning and interest [68] which in
turn, enhance student STEAM and CT skills. There were studies that used the combi-
nation of constructivist, collaborative and or inquiry-based educational approaches for
integration of CT into STEAM learning (Fig. 7). Notably, CT + science based studies
used the combination of constructive, collaborative and inquiry pedagogical approaches
to integrate CT in science topics. On the other hand, arts and mathematics + CT based
studies used the combination of collaborative + inquiry-based pedagogies.
We also identified that a few other studies used cognitive behaviourist pedagogy,
micro-learning and culturally relevant pedagogy (Fig. 8). Reportedly, culturally relevant
pedagogy based learning improves student ability to learn, seek evidence and challenge
beliefs [56]. Moreover, their results suggested that STEM + CT related activities that are
Pedagogical Approaches in Computational Thinking 13
Fig. 7. Combined pedagogies in CT integrated STEAM learning
tied with familiar issues or problems known by student may improve student engagement
in learning STEAM topics and fosters CT skills.
Fig. 8. Other pedagogies in CT-integrated STEAM learning
4.6 Mixed and Other Pedagogies in CT-Integrated STEAM Learning Setting
Our findings reveal that most of the studies used familiar pedagogical approaches
such as constructive, collaborative or combinations thereof. The aforementioned results
(Fig. 2 and 9) also imply that collaborative learning approaches blended well to use
CT-integrated STEAM lessons within K-12 and HE curricula. In particular, these stud-
ies implemented technology enhanced collaborative learning methods to teach STEAM
14 A. K. Veerasamy et al.
Fig. 9. Comparison framework: pedagogical approaches in CT enabled STEAM
Pedagogical Approaches in Computational Thinking 15
and CT concepts in K-12 and HE learning settings for the improvement of algorithmic
thinking, abstraction, characterizing problems and designing solutions, analysing and
interpreting data skills. Studies conducted on CT-integrated technology and engineering
learning settings used mainly collaborative learning and programming. For example, one
study on computing with diverse-real-world dataset using situated learning theory with
collaborative pedagogy reported introducing introductory computing to non-CS students
[35]. On the other hand, studies on CT-integrated science learning attempted using vari-
ous pedagogical approaches. For example, several studies used various combinations of
pedagogies for introducing CT and elementary science, physics and fundamental pro-
gramming skills in K-12 and HE learning settings [18, 27, 31, 34, 36]. However, most of
these studies dodged from providing background study of pedagogical approaches used
or why those were chosen over other approaches.
4.7 Teaching and Learning Activities Used in CT-Integrated STEAM Learning
Settings
To answer RQ2 we analysed the most common teaching and learning activities used
for integration of CT and STEAM. We identified hands-on, game based learning and
learning by modelling as being often used to introduce CT-integrated STEAM learning
in K-12 curricula. Similarly, flipped classroom, blended and online based learning &
training were used to train K-12 school teachers in order to conceptualise CT in STEAM
education (Fig. 9). On the other hand, project based learning and task based learning
were used to learn STEAM domains and key CT concepts in university context. Many
of these studies used programming language or coding-based activities to teach STEAM
topics but loosely integrated with CT concepts (Table 2 and Fig. 10).
From these results the following points emerged. First, CT-integrated STEAM learn-
ing are designed to improve both STEAM and CT constructs. Therefore, attention should
be paid to delivery methods and pedagogical approaches that influence the quality of
learning outcomes. However, none of these examined the role of pedagogy in conduct-
ing CT-integrated STEAM lessons or the association between instructional setup and
its influence on learning outcomes in the context of CT-integrated STEAM education
with deep engagement. Moreover, the pedagogies used in these studies varied based
on student demography and course contents. Hence, it is critical to develop a possible
inventory of successful pedagogies and supporting learning activities for CT-integrated
learning environments. Third, none of these studies used integrative pedagogy. More-
over, CT principles in STEAM learning are not well defined in these studies. These
should be explored further.
16 A. K. Veerasamy et al.
Fig. 10. Teaching and learning activities used in CT-integrated STEAM learning settings
Pedagogical Approaches in Computational Thinking 17
5 Conclusions and Limitations
Our study presented a comprehensive review of pedagogical approaches deployed in
CT-based STEAM curricula during the period 2015–early 2020. The results show that
STEAM with CT education has become an emerging topic of discussion over the past
decade. Collaborative learning pedagogy and hand-on learning based activities improves
student STEAM and CT skills. Coding activities were mainly used as an outreach tool to
understand the role of CT key elements in learning STEAM topics. However, the kinds
of pedagogical approaches used in these articles were not clearly stated. This implies
that well-grounded learning trajectories must be determined in CT-integrated STEAM
curricula to obtain consistent progress in developing STEAM and CT skills. In addition,
our review suggests that collaborative and hands-on learning methods may improve
student creativity and problem-solving skills in STEAM disciplines.
This research has a few limitations. First, the articles examined in this review were
extracted from a single database (Scopus), published in between January 2015 and
February 2020 only. This may have been limiting in terms of the results reported in
our study. However, we have planned to extend our research period with more key
words and search of a broader category, for a larger data set. Second, our study did
not conduct the deep understanding of qualitative study on pedagogies used in these
articles, in the context of learning outcomes. Third, our study did not examine the role
of educational technologies used. This will be addressed in future work. Despite these
limitations, our findings provide some suggestions and evidence for instructors and
educational institutions to research and develop pedagogical models for CT-integrated
STEAM education.
Acknowledgments. The authors wish to thank all members of ViLLE research team group and
Department of Future technologies, University of Turku, for their comments and support that
greatly improved the manuscript. This research was supported fully by a University of Turku,
Turku, Finland.
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