Trends and Issues in STEM + C Research: A Bibliometric
Hanxiang Du
, Wanli Xing
, Bo Pei
, Yifang Zeng
, Jie Lu
and Yuanlin Zhang
College of Education, University of Florida, Gainesville, Florida, U.S.A.
College of Education, Texas Tech University, Lubbock, Texas, U.S.A.
Department of Computer Science, Texas Tech University, Lubbock, Texas, U.S.A.
Keywords: STEM + C, Computational Thinking, Computing Education.
Abstract: The integration of computing education or computational thinking with STEM majors has gained substantial
research interests. A number of research papers of the topic were published. This work is to provide a
comprehensive overview of literature in the STEM + C field through both bibliometric and content analysis.
We conducted a systematic search to identify articles and utilized machine-learning-based techniques to
analyze these articles. Common bibliometric indicators were used for bibliometric analysis. Machine-
learning-based text mining techniques such as LDA topic modelling and flow analysis were used for content
analysis. Our analysis spotted STEM + C publication trends, popular topics and their dynamics over time.
This work also pinpointed commonly used methodologies for integration study for both PK, K–12 and higher
education. Meanwhile, several future research directions were identified. This work contributes to the
literature by systematically examining the existing literature and bringing machine-learning-based data
mining techniques to the analysis.
STEM + C, defined as a field ofscience, technology,
engineering, mathematics (STEM) and computing”,
is also interpreted as an integration of computational
thinking (CT) to STEM disciplines by National
Science Foundation (NSF, 2020). The integration
practice of CT into STEM disciplines has drawn
increasing research interests over the years. Despite
the discrepancies in the definition of and elements of
CT among various research (NRC, 2010), STEM + C
has remained an active research field in the past
decade. However, to our best knowledge, no research
has explored the existing literature on the topic using
quantitative methods. Bibliometric analysis is used
for the study of qualitative features and research
performance, especially for large quantities of
publications (Wallin, 2005). By conducting a
bibliometric and content analysis on the field of
STEM + C, this paper aims to provide valuable
references on existing literature to researchers and
potential topics for future work.
1.1 What Is STEM + C?
There is much discussion on what STEM, computing
and CT are for. Although no common agreement has
been achieved on their definition, we provide our
perceptions and rationales before diving into the field.
The goal here is not to exhaust various definitions of
the terms, but to clarify the scope of our work. What
the acronym STEM stands for is quite clear: Science,
Technology, Engineering and Math, as are its
alternative versions STEAM and STREAM, which
include the Arts and Reading respectively. However,
agreement has not yet been reached about what the
four letters mean when strung together. Among many
perspectives, adopted definitions assume STEM to be
one or more of the four isolated subjects, or an
integrated continuum of multidisciplinary elements
(Bybee, 2010; Kelley & Knowles, 2016). Even within
the integrated STEM field, the discussion on the
relationships and conceptual frameworks for learning
among science, technology, engineering and
Du, H., Xing, W., Pei, B., Zeng, Y., Lu, J. and Zhang, Y.
Trends and Issues in STEM + C Research: A Bibliometric Perspective.
DOI: 10.5220/0010998800003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 1, pages 69-80
ISBN: 978-989-758-562-3; ISSN: 2184-5026
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
mathematics remains unresolved (English, 2016;
Kelley & Knowles, 2016).
The NSF’s definition on STEM + C injects C
with two components: computing education and CT
education. This definition explicitly indicates that
computing is a concept different from, but related to
CT. Computing represents an integrated field of
computer science, information science, computer
engineering and information technology (Wing,
2008). According to a report that explores how
computing is taught worldwide, computer science and
information technology are categorized as disciplines
of the field of computing (Jones, 2011). Similarly,
computing education is considered to be a broad term
that may include one or more of the following areas:
computer science, technology literacy or fluency, and
information technology (Krakovsky, 2010).
Meanwhile, the term “computing” is often used
interchangeably with “computation” and
“computational” in the context of programming or
calculations (Psycharis, 2018), as are “computing
education” and “computer science education”
(Garneli et al., 2015). The goal of this work is to
investigate literature of the entire STEM + C field
using bibliometric analysis methods. Hence, we will
take computing education as a broad term which
includes, but is not limited to, computing education.
Jeannette Wing (2006) argued that CT
represents “a universally applicable attitude and skill
set” that involves problem solving, system design and
understanding human behaviour, and that CT will
benefit everyone rather than solely computer
scientists. Conceptualizing CT with a focus on
problem solving has both advocates and critics (Barr
& Stephenson, 2011; Bundy, 2007; Glass, 2006; The
Royal Society, 2012). Despite the discrepancy on
CT’s definition, conceptual framework, and key
elements, most literature does not question that CT is
a skill related to computing or programming practice.
Evidently, CT does not equal program writing. In
particular, CT is generally considered to be a different
skill from programming, while programming is
commonly used to teach CT (Lye & Koh, 2014). As
discussions on CT’s definition and components
continue, this work keeps an open mind on related
publications and their adopted conceptual
This work considers multiple perspectives on
STEM and CT to be legitimate providing that they are
in the context of the aforementioned core disciplines.
Similarly, this work takes computing education in its
broad form which includes computing education,
information technology, information literacy and
their variations.
1.2 Research on STEM + C
Reviews of literature on CT, computing education,
and their integration with one or more isolated STEM
subjects have been conducted. Grover & Pea (2013)
went through various definitions of CT and illustrated
how they were related to the idea of “computational
literacy” or “procedural literacy” in past decades.
They also examined research and educational practice
in CT or computing education, however, STEM did
not appear as a requirement to the integration
practice. Many studies they mentioned aimed to
prompt CT through computing education, rather than
STEM. Garneli et al. (2015) examined 47 peer-
reviewed articles on K–12 computer science
education with a focus on educational contexts and
efficient instructional tools and practices. Upadhyaya
et al. (2020) collected over 500 publications on K–12
CT research in the USA and conducted a longitudinal
analysis of publications from 2012 to 2018. Their
focus was to provide a general description of the
current status of computing education, including
curriculum content, grade levels, and the way in
which computing education was delivered. Similarly,
several other studies investigated related publications
focusing on either computing education (Robins et
al., 2003), or CT from multiple perspectives like
instructional tools and practice, conceptual
frameworks, and assessments (Hsu et al., 2018;
Kalelioglu et al., 2016; Tang et al., 2020; Zhang &
Nouri, 2019).
Several review studies were conducted to
explore the relationship between CT and mathematics
and/or science. Weintrop et al. (2016) framed CT in a
science and mathematics context by identifying a
taxonomy of four categories. They reviewed
discussions on CT’s definition and its crucial
connection to science and mathematics learning, as
well as CT-promoting practices at K–12 schools. In
addition to a practical taxonomy, their work provided
a solid conceptual framework for future research.
Barcelos et al. (2018) collected 42 publications that
had an experimental design specifically aimed at
developing CT skills through mathematics learning
activities. They reported a systematic analysis on
instructional tools and materials, experimental
designs, assessments, and reported achievements.
Hickmott et al. (2018) searched 6 databases and
identified 393 peer-reviewed articles on CT in K–12
education, then classified results into five categories
based on mathematical concepts like algebra or
geometry. They found that most studies were from the
domain of computer science and focused more on
CSEDU 2022 - 14th International Conference on Computer Supported Education
programming skills rather than mathematics
Existing works linking STEM to CT mainly
focus on conceptual frameworks or pedagogical
strategies, and most of them are empirical studies.
Jona et al. (2014) suggested an alternative strategy to
improve students’ engagement and sustention in
computer science by embedding CT activities within
their ongoing STEM coursework. Similarly, Swaid
(2015) proposed a comprehensive project to integrate
CT into STEM by enforcing CT elements in STEM
gate-keeping courses like the introductory level
courses of STEM and computer science. Leonard et
al. (2016) designed learning activities to integrate
technology with CT utilizing robotics and game
design. Psycharis (2018) outlined various research
and practices for STEAM integration. Although the
role of CT, computing education, and their integration
was discussed, the goal was to support their proposed
model: Computational STEAM Pedagogy. Their
research was more qualitative than quantitative.
1.3 Research Goals
Bibliometric analysis uses statistical analysis to
systematically extract measurable features from
publications within a field (Agarwal et al., 2016). By
utilizing various bibliometric indicators and different
methodologies, bibliometric analysis can help assess
the impact of research, measure the importance of
publications, as well as decompose the evolution of a
research topic, and identify potential research topics
(Agarwal et al., 2016; Song et al., 2019). It has been
shown as a reliable and useful tool to overview the
existing literature of a research field (Campbell et al.,
2010). However, bibliometric analysis has also been
criticized for its exclusion of content (Hung, 2012).
To address this issue, we extend this work by
enabling data-based content analysis to provide a
more comprehensive and systematic overview of
STEM + C literature. Our work addresses the
following research questions (RQ):
RQ 1. What are the current trends, popular topics
and their dynamics in STEM + C research?
RQ 2. What is the role of CT or computing
education in STEM + C research?
RQ 3. What potential research directions shall be
addressed based on current literature?
For the purpose of investigating the whole STEM + C
field of work, we take STEM, CT, and computing
education in their broad terms and do not exclude
articles based on discrepancies with one specific
definition. The search terms are defined as a
combination of x AND y AND z, as shown in Table
1. Both x and y are used to search article titles,
representing key terms of STEM and C, respectively.
Meanwhile, z is used to search within abstracts for
educational articles where applicable. To search
efficiently, “computer” was excluded from y: if
“computer” was included in y, then it would form a
combination “computer science” with “science” from
x. Even with restrictions on abstracts, “computer
science” would result in a dramatically large and
unnecessary number of results. The term “computer
science” or “CS” was excluded for this same reason.
Table 1: Search terms.
Field Key Terms
x Title STEM OR science OR technology
OR engineering OR math OR
biology OR chemistry OR physics
y Title computational thinking OR
programming OR computing
z Abstract learn OR course
Figure 1 presents the data retrieval that consists of
a two-round search. In the first round, we
systematically searched three academic databases:
Web of Science, IEEE Xplore, and ACM Digital
Library. Publications were considered if they were:
(1). Articles written in English, including journal
articles and conference proceedings, excluding
dissertations, books, or book chapters. (2). Included
content and topics falling into the STEM + C field
with a focus on integration practice, whether they
were empirical studies or not. Based on search terms
and restrictions, 2855 records were retrieved and
manually filtered by checking the content of titles and
abstracts. As a result, 56 records were kept after the
first-round search.
Figure 1: Data retrieval process.
Trends and Issues in STEM + C Research: A Bibliometric Perspective
Web of Science is a well-known database of high-
quality academic work and widely used to search
articles for bibliometric analysis. However, the
journals it links to are selected by humans. Google
Scholar is a scholarly search engine that connects to
the entire Internet, covering more valuable records
that cannot be found on Web of Science (Kiduk &
Meho, 2006). However, due to the computational
settings of the search engine, the search results can be
different even one uses the same search term. In
addition, search results from Google Scholar cannot
be exported systematically for further processing. To
maintain the systematic nature and consistency of the
searching process, Google Scholar was only involved
in the second-round search.
The goal of the second-round search, or
supplement search, is to maximize the retrieval of
related work which may have been neglected in the
first-round search. First, in addition to search terms,
we also usedSTEM + C in Google Scholar.
Different from prior practice, both searching and
filtering were conducted at the same time. Second,
forward and backward tracing was conducted to
supplement the search. We checked articles that cited
the filtered results, as well as those cited by them. As
a matter of fact, more records were identified at this
stage. Our speculation is that those article titles do not
necessarily meet the identified key terms
combinations. Instead, more common terms were
used like school, students, or education. However, if
we used isolated search terms, including the ones
mentioned, searched results may be too general to be
an efficient search. As a result, 202 publications were
finalized for this study.
3.1 Bibliometric Indicators
Several popular bibliometric indicators were used in
this work to measure the impact of collected articles.
Publication and citation count are commonly used
indicators to assess productivity and influence. Two
thresholds of total citation count were used to
measure the influence of an author: 100 and 300
(Merigó et al., 2015). Meanwhile, the h-index which
measures the level of scientific achievement was also
included. The h-index used in this work was collected
from author’s personal page on Google Scholar.
3.2 Sleeping Beauties in Science
Citations are commonly used to evaluate scholarly
articles’ impact and research performance. Citation
dynamics and quoted papers describe the
dissemination trajectories of research articles.
Sleeping Beauties analysis is one way to examine the
citation history of papers. Sleeping Beauties in
science refer to articles that were not recognized until
years later after publication (van Raan, 2004). Li
(2014) proposed a parameter-free criterion to assess
the imbalance of citation distribution and later was
used to identify Sleeping Beauties in science. Let C
be the total number of citations, and c
(i {1, 2,
, n}) be the number of citations received in the ith
year. Gs is an adjustment of Gini coefficient and
defined as:
𝐺𝑠 = 1 −
,𝐶 > 0 (1)
, where Gs (-1, 1]. When the article receives a total
citation of 0, Gs is 1. Otherwise, the higher Gs is, the
more citations one article receives in its later years.
3.3 Textual Data Pre-Processing
Data processing is necessary as it systematically and
automatically helps trim and clean the textual data by
eliminating redundant information. As a result, the
textual data will be presented as more structured and
relevant, and its meaningful structures can be
captured. Several commonly used textual data
processing techniques are involved in this work.
Special characters and punctuation are removed.
Commonly used words across fields that carry little
information like “a”, “the”, and “of” are removed as
well using a stopwords package. Tokenization divides
a string into several substrings for future pre-
processing. Lemmatization and stemming are
commonly used techniques to reduce inflectional
forms of terms. For example, “books”, “book”,
“book’s” and “books’” will be mapped to “book”.
The lemmatizer and Porter stemmer package are used
in this process.
3.4 Keywords Flow Analysis
Using textual data mining techniques, word flow
analysis is employed to present the keyword
dynamics over time (Du et al., 2019). We define the
keywords as terms used repeatedly in abstract. Term
frequency of keywords over time are then calculated.
The results are presented in a flow chart that provides
a general overview of keyword dynamics over time.
CSEDU 2022 - 14th International Conference on Computer Supported Education
The keywords flow will help identify popular
research directions.
3.5 Latent Dirichlet Allocation (LDA)
Latent Dirichlet Allocation (LDA) is a widely used
unsupervised machine learning algorithm for finding
the relationship between documents and words in
textual data (Blei et al., 2003). LDA is able to
generalize a number of topics from given documents
with each topic represented by a few words. The
summarization capability makes LDA powerful and
convenient for key feature extraction from large-size
textual datasets. It has been broadly applied to various
research scenarios, including social media content
classification, bibliometric analysis, and scientific
article recommendation (Blei et al., 2003; Iqbal et al.,
2019). Coherence score, which measures how well a
topic model fits the data set, is used to decide the
number of topics. A higher coherence score means
results of such a model represent the documents better
than a lower one. We will also use abstracts of
collected articles to train an LDA topic model. All
analysis for this work is implemented in Python.
4.1 Trend & Prolific Analysis
We identified 202 articles with 512 distinctive
authors of 187 institutions from 29 countries/regions
worldwide. By March 2020, these 202 articles have
5590 total citations.
The annual publication and citation count are
presented in Figure 2, where each data point stands
for the number of publications or citations within a
specific year. Beginning in 2008, there was an
increasing trend for both the publication and citation
count despite some fluctuation, indicating growing
research interest towards the field. Since we only
collected work that was available by February, 2020,
it leads to a significant drop in year 2020 on both
lines. In addition, the USA is the most prolific country
with 156 publications, which includes more than 2/3
of the total publications.
In order to view the background distribution,
authors’ affiliated majors were categorized into four
categories: Education, Computer Science, STEM and
Other. The distribution is presented in Figure 3.
Authors with an education background rank first,
closely followed by those with a computer science
background. Meanwhile, 8.8% of authors come from
STEM majors. This indicates that more researchers in
the field are from computer science or STEM majors
rather than education.
4.2 Highly-cited Publications and
Sleeping Beauties
We calculated the adjusted Gini coefficient (Gs) to
measure the imbalance of citation history of identified
highly-cited articles. Table 3 lists articles whose total
citation count is one standard deviation higher the
mean (M = 27.98, SD = 83.60) and their Gs values.
Considering the highest Gs is less than 0.50, it is fair
to say that most highly-cited publications of STEM +
C receive immediate recognition.
Figure 2: Annual publication and citation trend.
Trends and Issues in STEM + C Research: A Bibliometric Perspective
Figure 3: Author affiliated majors’ distribution.
4.3 Keywords Flow Analysis
Figure 4 presents the identified keywords by term
frequency and Figure 5 shows keywords flow over
time. Terms of high frequency yet low information to
our analysis like “education”, “use”, “school”,
“learn” and “integrate” were removed. The term
Table 3: Highly-cited articles and their Gs value.
Articles Citation
The learning effects of computer simulations in
science education
Thinking like a wolf, a sheep, or a firefly:
Learning biology through constructing and
testing computational theories—an embodied
modelling approach.
Defining computational thinking for
mathematics and science classrooms
Computational thinking and tinkering:
Exploration of an early childhood robotics
Development of system thinking skills in the
context of earth s
stem education
Integrating computational thinking with K
science education using agent-based
utation: A theoretical framewor
Computational thinking in K
9 education
Visual programming languages integrated
across the curriculum in elementary school: A
two-year case study using “Scratch” in five
Computational thinking in compulsory
education: Towards an agenda for research and
A multidisciplinary approach towards
computational thinking for science majors
Designing for deeper learning in a blended
computer science course for middle school
Supporting all learners in school-wide
computational thinking: A cross-case
ualitative anal
*Gs >.40
“CT” was calculated under the term “computational
thinking”. “Computational thinkingattracts the most
research interest over time. Computing-related terms
like “programming”, “modelling”, “simulation”,
“concept”, and “data” are also listed, indicating
various aspects of CT or computing education were
explored and discussed. Individual disciplines like
science and math, as well as the acronym STEM, all
received increasing attention over the years.
However, research interest in engineering is rather
limited in comparison with others. Meanwhile,
“teachers” is mentioned far less than “students” in the
field, indicating most research focuses on students.
Figure 4: 20 Keywords identified by term frequency.
4.4 LDA Topic Modelling
We first evaluate the performance of our model by
calculating the coherence score with topic numbers
ranging from 1 to 15, setting the model parameter
passes at 50 and random_state at 1. Suggested by c-
oherence score, model has the highest interpretability
when the topic number equals 4. Figure 6 shows the
results of our trained LDA topic model and the topic
distribution. The numbers in Figure 6(a) are the
probability distribution over the topic words. The
topic distribution result is displayed in Figure 6(b).
Some terms are common across topics with
different probabilities, like “student”, “ct”, “comput”,
“scienc” and “school”. This is common as topics are
not mutually exclusive (DiMaggio, 2013). Several
terms are unique to only one or two topics, like
“mathemat”, “program”, “model”, “teacher”, “effect”
and “transfer”. Based on the topic words, we can
summarize Topic 1 as mathematics/STEM research,
Topic 2 as programming learning, Topic 3 as teacher
and integration development, and Topic 4 as learning
outcomes. As shown in Figure 6(b), more than half of
the articles focused on a programming learning
perspective of STEM + C research, around one third
of articles discuss mathematics and STEM learning
CSEDU 2022 - 14th International Conference on Computer Supported Education
and research, while only a few studies address on
teachers’ perspectives and others.
5.1 What Are the Current Trends,
Popular Topics and Their
Dynamics in STEM + C Research?
The number of publications and citations in STEM +
C consistently grow since 2007, and have increased
rapidly for the last 5 years. The USA has contributed
dramatically more publications and received more
citations than any other country/region. Almost half
of the authors are from CS or STEM majors, while
around 40% are from education, indicating STEM +
C has drawn interest from a wide range of majors.
Sleep Beauty analysis suggests work of great
importance has received immediate recognition.
LDA topic modelling was used to identify topics:
mathematics/STEM research, programming learning,
teacher and integration development, and learning
outcomes. Most articles in the field focus on the first
two topics, while the other two are less discussed. In
particular, more than half of the studies focus on the
programming learning, indicating that the most
popular way to conduct STEM + C research is to
integrate programming practice into STEM learning.
Prompting integration of CT through mathematics or
STEM learning has also gained popularity. More than
one third of the collected articles are assigned with
the topic of mathematics/STEM research. The
percentage explicitly suggests the importance of
mathematics in the field.
While the LDA topic modelling sees data on the
document level, a term level analysis, keywords flow,
is conducted to provide a dynamic and
comprehensive view. The term “computational
thinking” (including “CT”) is the single most frequent
keyword and its frequency is about 3 times that of the
second term, “student”. The frequency of “studentis
about 3 times that of “teacher”, indicating that many
studies focus on students’ learning perspectives.
Researchers seem to prefer “science” and “math”
over “engineering” or “technology”. In particular,
both “science” and “math” are more frequently
mentioned than “STEM”. “Engineering” ranks 19th
Figure 5: A general keywords flow of all keywords over time: 1995 – 2020.
Trends and Issues in STEM + C Research: A Bibliometric Perspective
Figure 6: LDA results (a) and the topic distribution (b).
mentioned than “STEM”. “Engineering” ranks 19th
and “technology” is not listed at all. This indicates
that attention is not equally distributed within STEM
disciplines. Meanwhile, several terms are explicitly
computing education related: “programming”,
“modeling”, “simulation” and “data”. These can be
viewed as popular computing education components
in STEM + C research. Among these four, scholars
favour “programming” the most. “Data” seems to
have received some attention only in the past 5 years.
5.2 What is the Role of CT or
Computing Education in
STEM + C Research?
CT has been commonly accepted as an effective
strategy to benefit and advance STEM learning in the
research community (Assaraf & Orion, 2005;
Hambrusch et al., 2009; Jona et al., 2014; Perković et
al., 2010; Swaid, 2015; Weintrop et al., 2016). It is
intuitive to develop CT through programming.
Meanwhile, programming is the most commonly
used way to teach CT (Lye & Koh, 2014). An
alternative way to integrate CT is through
computation concepts, modelling and simulations,
especially for young children (Assaraf & Orion,
2005; Bers et al., 2014; Sáez-López et al., 2016;
Sengupta et al., 2013; Wilensky & Reisman, 2006).
To summarize, programming practice, computation
concepts, modelling and simulation, and data
manipulation are commonly involved in STEM + C
One shared interest in STEM + C research is the
development of a CT framework that can be widely
applied across disciplines in K–12 or higher
education (Hambrusch et al., 2009; Jona et al., 2014;
Perković et al., 2010; Sengupta et al., 2013; Swaid,
2015; Weintrop et al., 2016). Several goals are
extensively addressed by these works, one is to
advance STEM learning with help of CT and prepare
the next generation to be modern citizens (Hambrusch
et al., 2009; Jona et al., 2014; Perković et al., 2010;
Sengupta et al., 2013; Weintrop et al., 2016).
Meanwhile, embedding CT with current K-12 STEM
courses is considered as an alternative solution to K-
12 schools’ inability to offer computer science or
programming classes (Jona et al., 2014; Weintrop et
al., 2016). Works focusing on framework
development can be roughly classified into 2
categories: K–12 or college level education.
College-level practices of STEM + C framework
development often take the form of developing a new
course with joint efforts across disciplines. These
courses are designed for early years of college
education, and mostly involve programming. Several
studies on this provided detailed course descriptions.
Meanwhile, data manipulations, programming
concepts, and simulations are widely adopted by
several frameworks to help students learn scientific
inquiry, STEM gate-keeping courses, and general
courses like Liberal Studies (e.g., Hambrusch et al.,
2009; Perković et al., 2010; Swaid, 2015).
K–12 STEM + C framework development mostly
attempt to embed CT in current STEM courses.
However, programming is not always involved. This
strategy potentially saves schools from the financial
concerns of hiring new teachers and supporting new
courses (Jona et al., 2014; Weintrop et al., 2016).
Meanwhile, all students are required to take STEM
courses in school, the integration will expose a much
wider range of population than a specific course does
(Jona et al., 2014; Weintrop et al., 2016). In
particular, Perković et al. (2010) proposed an agent-
based learning environment for science learning and
modeling. In addition to proposed framework, they
CSEDU 2022 - 14th International Conference on Computer Supported Education
also specified the computational architecture
underlying the learning environment. Their work also
conducted an empirical study using the developed
tools. All these provide valuable reference to future
development. Weintrop et al. (2016) focused on
embedding CT in STEM courses for traditional
classrooms. Through examining literature, practice,
and interviewing teachers, STEM experts and
computer scientists, they define CT through a
taxonomy. They examined developed skills and
generalized four categories: data, modelling and
simulation, computational problem solving, and
systems thinking. The taxonomy provides significant
reference to future research and course development.
Although calls to computing education have
received substantial advocates in the past years, the
lack of qualified teachers, budgets, and standards
barricades its popularization in K–12 education
(Israel et al., 2015; Wang et al., 2016). Addressing CT
provides a feasible solution to many situations. First,
embedding CT within students’ current STEM
workload guarantees students’ exposure while
assuring the teacher’s comfort with learning materials
(Jona et al., 2014). Such practice requires less budget,
expertise and effort than developing and supporting a
new computer science course. Second, learning
programming is not easy, especially for young
children. Learning CT lowers the threshold
significantly. Although the youngest group of
learners are kindergarten children (Bers et al., 2014),
most of the work targets students in 5th grade or
higher. Block-based programming is preferred when
programming is involved. Third, CT addresses
adaptability to STEM courses, which takes form in
programming, data manipulation, modelling and
simulation, or systems thinking (e.g., Assaraf, 2005;
Wilensky & Reisman, 2006).
5.3 What Potential Research Directions
Shall Be Addressed based on
Current Literature?
There are several potential research topics in addition
to what has been identified as popular in the past.
First, from a disciplinary perspective, more efforts
can be made to explore engineering and technology.
Some college-level learning activities are designed to
utilize CT to solve engineering problems (Hambrusch
et al., 2009; Perković et al., 2010; Swaid, 2015).
However, this engineering context is much less
addressed in K–12 education. Second, there is a
potential lack of research on teachers’ professional
development or training. The work of Israel (2015) is
one of the few works that focuses on teacher’s
professional development. This qualitative study
reveals K–12 teachers’ concerns and needs to teach
CT in K–5 classrooms. Third, community college
seems absent from the current research scope. It
remains unclear how the college-level STEM + C
courses can be adopted by community colleges.
Fourth, “data” did not receive much research
attention until the past five years. Related activities
like data literacy, data science, data manipulation
worth more investigation.
5.4 Conclusion
Bibliometric analysis is useful to investigate and
explore existing literature in the field of STEM + C.
Based on 202 identified publications collected from
Web of Science, IEEE Xplore, ACM Digital Library
and Google Scholar, this work presents a
comprehensive overview of the field by showing
publication trends, identifying prolific
countries/regions, institutions and authors,
visualizing collaborations among countries/regions,
institutions and authors, generalizing content-based
topics, recognizing research keywords, popular
research fields and understudied research
There are a few interesting findings. The number
of publications and citations in STEM + C have
consistently grown since 2007, and have increased
rapidly for the last 5 years. STEM + C as an
interdisciplinary field has drawn interest from a wide
range of majors. We anticipate there will be more
publications in the future. Meanwhile, mathematics
has the highest frequency among the four STEM
subjects, making it the most popular STEM subject
for existing STEM + C research practice. When it
comes to computing education, the terms like
“programming”, “modelling”, “simulation” and
“data” have rather high frequency. Among these four
aspects, scholars favour “programming” the most,
while “data” seems to start to receive attention in the
past 5 years only.
5.5 Limitation
Bibliometric analysis, by its nature, focuses on
numbers instead of content. Although we have
conducted both term-level and document-level
content analysis through a data mining method to
address the issue, this work did not fully review all
identified articles. Meanwhile, this work only
searched three databases and one search engine: Web
of Science, IEEE Xplore, ACM Digital Library, and
Google Scholar. Search terms were identified based
Trends and Issues in STEM + C Research: A Bibliometric Perspective
on our understanding of the field as well as search
efficiency. It will be helpful if future work can
identify more efficient search terms or mechanisms
within the field and explore more databases.
Meanwhile, our work did not fully examine other
expressions that are argued similar to CT, like
computational literacy or systems thinking, leading to
unidentified related articles.
This work is supported by the National Science
Foundation (NSF) of the United States under grant
number 1901704. Any opinions, findings, and
conclusions or recommendations expressed in this
paper, however, are those of the authors and do not
necessarily reflect the views of the NSF.
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