Abstract
Background: Mathematics is a core discipline of Science, Technology, Engineering, and Mathematics (STEM) education. It is imperative to review the role of mathematics in that progress.
Aim: Using a bibliometric review, the study examined global research trends in mathematics of STEM education from 2010 to 2024.
Setting: Science, technology, engineering, and mathematics (STEM) education is key to national development. Understanding its nature is important. The study draws on international academic publications indexed primarily in the Scopus database, encompassing journal articles, books, book chapters, and conference proceedings.
Methods: A bibliometric review approach was employed, using targeted keywords – STEM, research, teaching or learning, students or learners, and mathematics – to identify relevant literature. The Scopus database, retrieved on 21 January 2025, was utilised to identify 669 documents. VOSviewer software was used to generate visual maps of research trends and international collaboration networks.
Results: The analysis reveals that the United States and Indonesia dominate research on mathematics-focused STEM education. The United Kingdom serves as a central node for collaboration, while major economies such as China, India, and Japan are present, their participation is small.
Conclusion: The study shows Africa’s absence as a singularly visible contributor, which corresponds to Mode 1 knowledge production. By contrast, countries such as the United States and Indonesia demonstrate Mode 2 characteristics: large volumes of research, extensive networks of joint authorship, and participation in cross-border collaborations.
Contribution: This study highlights the need for greater African engagement in STEM education research, particularly in mathematics, to ensure equitable development relative to other countries and greater representation in global educational innovation.
Keywords: bibliometric analysis; mathematics education; STEM education; research collaboration; global trends.
Introduction
Since about 2010, research in transdisciplinary Science, Technology, Engineering, and Mathematics (STEM) education has increased significantly, coinciding with the rapid adoption of Information and Communication Technology (ICT) in teaching and learning. Information and Communication Technology refers to the broad set of technologies, systems, and tools that enable the creation, storage, processing, transmission, and exchange of information through electronic and digital means. It integrates computing and telecommunications to facilitate communication and information access. As artificial intelligence (AI) now shows, ICT is accelerating the transformation of the educational landscape. Education stakeholders, including policymakers, industrialists, researchers, teachers, and others, strongly agree that STEM education has the potential not only to boost socio-economic development but also to increase learners’ interest and engagement in STEM subjects. This is welcome, given that today’s learners find studying STEM subjects unattractive. For example, Sikhosana, Maphalala and Mncube (2025:2) noted that despite reforms and support, learners remain under-represented in STEM fields, reflecting persistent disinterest and lack of attraction to these subjects.
In South Africa, many high school learners, particularly from townships and rural areas, show apathy towards learning STEM subjects, which are meant to empower them (see Sikhosana et al. 2025). These subjects enable them to take up STEM careers in the future. This apathy is unhelpful in building a democratic South Africa, as economic leveraging of STEM professions such as medicine, engineering, technology, and others continues to be the preserve of historically privileged social groups. Furthermore, these professions are imperative to a country’s economic competitiveness.
Daugherty et al. (2017:7) argue that interdisciplinary STEM education is ‘a pedagogical approach by which students learn the interconnectedness of the disciplines of STEM, often through problem-based and cooperative learning’. Meanwhile, Lesseig, Slavit and Simpson (2023:2) view transdisciplinary STEM education as ‘an integrated effort that removes the traditional barriers between subjects, and instead focuses on the applied process of designing solutions to complex contextual problems’.
In this article, we treat inter- and trans-disciplinarity in STEM education interchangeably, as they serve to unify STEM subjects through authentic problem-solving.
Mathematics is one of the major disciplines in interdisciplinary STEM education, where learner achievement is below expectations. There was a systematic review of factors affecting the underachievement of South African learners in mathematics through the Trends in International Mathematics and Science Study (TIMSS) 2003, 2007, and 2011, and the Programme for International Student Assessment (PISA) 2003 and 2012 international comparison tests. Lee and Stankov (2018) reported on several non-cognitive factors affecting learners’ performance in mathematics, such as home background and culture. In the same way, the South African Annual National Assessments (ANA), organised from 2012 to 2015, were abandoned because learner underperformance was starkly below expectations. A prominent South African mathematics education researcher, Graven (2015:1), points to a crisis in mathematics learning, whereby grade 4 learners are already almost two grades behind grade-level expectations. The mathematics performance lag of such cohorts continues to worsen as learners move to higher grades with ever-diminishing hope of recovery. The same scenario applies to reading and other STEM subjects, such as science. These situations are worrying, and research and action are needed to redress them.
Learners’ motivation to study STEM subjects needs to be increased, particularly in the current context, where learners regard these subjects as hard or unattractive (Sikhosana et al. 2025). Further, Zhou and Shirazi (2025:145) conclude that young people’s STEM career aspirations are declining, with many students perceiving STEM subjects as difficult, abstract, and less appealing compared to other fields. Learners regard STEM subjects as abstract and not connected to solving problems important to them. Learners need opportunities to negotiate different STEM concepts and processes in authentic problem-solving situations. This helps learners to witness the usefulness of each STEM subject in action. That way, students see how each subject works from first principles during problem solving. Students can extend the same methodologies in a problem-solving milieu post-school. In real life and the world of commerce, problems rarely present themselves as purely economic, scientific, or mathematical, for that matter. The problems are often multi-faceted, ill-defined, and rarely holistic. As the problem is scoped and interpreted, and key parameters are identified, it is tackled using collaborative expertise that requires different kinds of knowledge. The nature of knowledge required to make the next advance – whether scientific, technological, engineering, or mathematical reveals itself. Thus, different expertise is required as the solution to the problem evolves.
The terms ‘interdisciplinary’ and ‘transdisciplinary’ in STEM education are often not clearly distinguished. English (2016), Beane (1997), English and King (2019), and Vasquez, Sneider and Comer (2013) use these terms but do not explicitly define or differentiate them. This ambiguity causes confusion about the role of mathematics and research in transdisciplinary STEM. In this study, I treat the terms as interchangeable, given the lack of precise definitions in the literature.
What is bibliometrics?
Bibliometrics is an ICT- and AI-enabled methodology that helps identify and understand major research trends in selected fields of study, based on publications such as journal articles, conference proceedings, and books (Donthu et al. 2021). It enables the search of research databases using keywords linked to subjects, subject focus, authors, and sources, such as journal articles, books, and so on. Bibliometrics can also provide the number of citations for a single author or a group of authors. This way, a comprehensive picture of research undertaken worldwide in a particular field can be quickly established and analysed. The leading authors in a field of research can also be easily identified. What is essential is that bibliometric research establishes both the big and small picture, identifying the general issues, what has been researched, and to what extent, by whom?
Donthu et al. (2021) explained that before ICT and AI-enabled bibliometrics, researchers relied on experienced colleagues and conferences to identify relevant literature. Another way was to attend research conferences or review journal articles. However industrious a new researcher was, the efforts were essentially a hit-and-miss affair and much depended on luck. It was also time-consuming. It was possible to overlook important results and trends in a research area because the search was neither systematic nor random. There were many limitations, including limited journal availability. The issue of time was also limiting; one could not be expected to peruse every journal in the field.
The randomness of searching for research trends and impact has now been effectively eliminated by AI-enabled bibliometrics.
Donthu et al. (2021) report:
[B]ibliometric analysis is … rigorous method for exploring and analysing large volumes of scientific data … enables us to unpack the evolutionary nuances of a specific field, while shedding light on the emerging areas in that field. (p. 185)
Thus, bibliometric analysis helps researchers recognise underlying trends in scientific interaction, detect research trends, and evaluate the impacts of the research under study.
I now discuss the bibliometric parameters.
Bibliometric parameters: Some concepts
I discuss some major concepts of this process (see also Donthu et al. 2021; Li et al. 2020).
Firstly, there is citation analysis, which measures the number of citations in publications to quantify their impact and influence, such as journals. Secondly, there is research output, in which the number and frequency of publications by authors, institutions, or countries are reported. Thirdly, there is co-authorship analysis, which accounts for collaboration patterns among authors. Fourthly, there is co-citation analysis, which records the number of citations to multiple publications together (see Figure 1). Fifthly, there is bibliographic coupling, which analyses the similarity between publications based on shared references (see Figure 1). Sixthly, there is the journal impact factor, which accounts for the average number of citations per article in a journal. Seventhly, there is the h-index, which records an author’s productivity and citation impact. Eighthly, there is research collaboration, which analyses partnerships and networks among researchers. Other measures of bibliometric analysis include citation distribution across publications and scientometric mapping, which visualises the structure and dynamics of scientific fields. Then there is knowledge diffusion, examining the spread of ideas and innovations. Research evaluation assesses research performance. Lastly, there is science policy, which informs policy decisions using bibliometric data.
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FIGURE 1: Mathematics in science, technology, engineering, and mathematics education documents produced 2010–2024. |
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I explain more about the parameters that will be used in this research (see also Donthu et al. 2021; Li et al. 2020).
Bibliographic coupling measures similarity through citation analysis. It establishes connections between documents. If two works reference a common third work in their bibliographies, they are considered bibliographically coupled. The implication is that the two works address a common subject in a related way. Granted, the strength of the coupling between two documents increases if they cite more documents in common. For example, if documents A and B both cite documents C, D, and E, their bibliographic coupling strength is three. Thus, bibliographic coupling helps researchers locate related research through citation analysis patterns.
On the other hand, co-citation analysis examines how often two documents are cited together by other works. If two documents are frequently cited together by other authors, they are considered co-cited. For example, documents A and B are cited by C, D, and E, so A and B have a co-citation of three. This suggests that they address the same topics and handle related theories. It also indicates influential research in a research field.
Thus, bibliographic coupling and co-citation coupling are closely linked, the one being a mirror image of the other.
The VOSviewer software
One of the most important tools for visualising bibliometric data is VOSviewer software (CWTS B.V., Leiden University, The Netherlands) (Van Eck & Waltman 2010). VOSviewer constructs and visualises bibliometric networks. It collects data on research publications, citations, or collaborations by country, drawing on sources such as Web of Science, Scopus, or others. In research, the networks include journals, books, researchers, or individual publications. The bibliometric parameters often include citations, bibliographic coupling, co-citations, or co-authorship relations. Through text mining, the VOSviewer constructs and visualises the co-occurrence of keywords in research. There are several ways the VOSviewer helps to represent data, such as network and overlay visualisation.
Research problem
In the wake of advances in STEM education research, Li et al. (2020:1) comment that ‘reviews of the status and trends in STEM education research internationally support the development of the field’. Furthermore, Tytler (2020:21) contends that a key feature of concern for STEM in schools is the prospect of a vastly changing world of work that current students will enter, and the need to consider the STEM competencies that will prepare students for productive futures. Research in STEM has grown considerably since 2010. The same cannot be said of some developing countries, such as South Africa, from which this research is being undertaken. The natural position for STEM researchers in developing countries is to identify the current state of the field, global trends in STEM research, and the latest ideas. Who are the key players, and where do they come from? The aim is to understand and benchmark practices with the most potential. Similarly, it is crucial to explore ideas from around the world to adapt and implement them in home countries. There is clearly a gap between research in STEM in developed countries and in countries such as South Africa and Sub-Saharan Africa (Li et al. 2020). To improve STEM education in South Africa, it is important to understand the current state of research in the field as a basis for developing an appropriate curriculum.
Aims of the research
These are the aims of this bibliometric research.
This study aimed to:
- Track the output and impact of authors or researchers in interdisciplinary STEM education, including mathematics. In this research, I aim to analyse publication data to identify influential works and researchers in transdisciplinary STEM education.
- Visualise relationships in transdisciplinary STEM education, including mathematics, to understand how publications relate to each other, revealing otherwise hidden patterns such as authorship collaborations and countries where collaborative research is increasing.
- Highlight the implications of its findings for the South African educational landscape.
Significance of the study
At present, global cooperation and collaboration are imperative to solving macro-problems such as climate change, environmental sustainability, poverty and economic inequality, public health, and global pandemics. These are STEM-related challenges. All stakeholders are involved in solving these problems, including scientists, political leadership, and the public. We witnessed how this collaboration helped to beat the coronavirus disease 2019 (COVID-19) pandemic. Mathematicians used social media to forecast the disease’s spread. This prompted political leaders worldwide to implement measures such as lockdowns, hand sanitising, social distancing, and the mandatory wearing of masks to prevent widespread deaths in their countries. In the background, furious scientific research was underway, producing COVID-19 vaccines in record time. This demonstrates the nature of problem-solving in the modern world. Science, technology, engineering, and mathematics education works in the same way. It is realistic, problem-solving-driven, multi-disciplinary, and collaborative. Relevance to contexts and time is increasingly important. One important context for STEM education is the indigenous knowledge of local people in educational settings.
Theoretical framework and literature review
This study employs Knowledge Production Theory (Cloete 2021; Gibbons et al. 1994) to analyse mathematics research within STEM education. The theory regards Mode 1 knowledge production as discipline-specific and primarily investigator-driven. Mode 2 is regarded as problem-initiated, transdisciplinary, and collaborative. This framework enables analysis beyond descriptive bibliometric indicators by examining whether mathematics in STEM education remains confined to traditional disciplinary boundaries (Mode 1) or has shifted toward collaborative approaches addressing broader educational and societal challenges (Mode 2), such as climate change and environmental sustainability. Evidence from co-authorship networks, thematic clusters, and citation impact shows how mathematics education research contributes to evolving knowledge-production modes in STEM and underscores its role in tackling macro-level problems rather than micro-level ones.
Mathematics, though an important subject, does not operate in a vacuum. To be most useful and understood, it must be studied and applied in conjunction with science, engineering, and technology. This is because many concepts and procedures overlap across the STEM disciplines. Today, there is a global turn towards STEM education, as it is now regarded as a pillar of national development and international competitiveness (Freeman, Marginson & Tytler 2019; Tikly et al. 2018).
Methods
Bibliometric analysis is based on thorough, focused searching for research on a specific theme, using keywords and filtering techniques (Donthu et al. 2021). The search was conducted in a research database that stores research publications. In this case, the Scopus database was used.
The search strategy
This search uses a bibliometric approach and draws on the Scopus research database. The search focuses on research articles on mathematics in transdisciplinary STEM education. While the acronym STEAM, with an ‘A’, encompasses STEM, it also places the arts at the centre as an integral component. The arts in STEAM education, while important, are not part of this research. This research primarily focuses on the role of mathematics in STEM education. It is interesting how transdisciplinary STEM education deepens the learning of mathematics, and how mathematics, in turn, can facilitate the learning of other STEM subjects.
A bibliometric search was performed in the Scopus database on 21 January 2025 using the query: TITLE-ABS-KEY (‘STEM’ AND ‘mathematics’ AND (‘teaching’ OR ‘learning’) AND (‘students’ OR ‘learners’)). The search was restricted to English-language publications from 2010 to 2024. Included document types were journal articles, books and book chapters, and conference proceedings; books, book chapters, editorials, and other non-peer-reviewed sources were excluded to maintain focus on scholarly outputs. This search identified 619 documents for analysis.
Inclusion and exclusion
Of particular importance in this bibliometric review are the inclusion and exclusion criteria. In terms of time, the inclusion period is 2010 to 2024. The language was limited to English, and the keywords above were used. From these, 619 documents were obtained. In terms of document types, I searched for books, book chapters, journal articles, and conference proceedings. These criteria were mainly used to maintain my focus, which is research on mathematics in interdisciplinary and transdisciplinary STEM education. Since this search is limited to the Scopus database, other research articles and sources from the Web of Science and other research databases are excluded from this research. It is envisaged that, in the future, the Web of Science and Scopus databases could be used to increase coverage and achieve data saturation.
Justifications, limitations, and delimitations
This study used only the Scopus database and excluded other databases, such as Web of Science. The justification is that Scopus is one of the Major Databases and that, as this is a qualitative study, the results are interpreted with reference to the selected sample. This choice aided clarity but limited the scope.
Ethical considerations
This article followed all ethical standards for research without direct contact with human or animal subjects.
Review findings
The following descriptive statistics summarises the features of this search: the steady rise in STEM research (Figure 1). The graph shows a steep positive slope; however, we also see a decline from 2021, presumably caused by the COVID-19 pandemic, and ranked by country (Figure 2), showing the predominance of the United States, followed by Indonesia. The document type (Figure 3) shows the predominance of conference articles, followed by journal articles and a tiny number of books and book chapters. Most of the documents were from conferences. The visuals were obtained from Scopus.
These frequencies are shown in Table 1.
| TABLE 1: Documents by country or territory (Number and %). |
The bibliometric analysis revealed striking geographical disparities in research on mathematics-focused STEM education. Of the 669 documents studied, the United States showed 280 publications (41.9%). This demonstrated its leading role in this rapidly emerging research. Indonesia ranked second with 183 documents (27.4%), indicating it is another hub of STEM-related mathematics research. Thailand contributed 60 documents (9.0%), while the United Kingdom (29; 4.3%) and Germany (21; 3.1%) represented modest European engagement, followed by others, notably Australia.
The United States and Indonesia dwarf the rest of the world in research, producing about 70% of the total output. This showed a highly skewed distribution of research activity. The remaining countries contributed relatively small shares, with most registering below 5%. Notably, Africa was conspicuously absent from the dataset, showing the continent’s under-representation in global scholarship on mathematics in STEM education research.
In constructing the dataset, a threshold filter was applied to include only countries with at least 10 documents. Those with fewer than 10 publications were thrown together under the ‘Others’ category. This numerical choice helped clarity in visualisation and avoided clutter from numerous countries with minimal contributions. This also revealed a critical finding: no African country met the threshold of 10 documents in mathematics-focused transdisciplinary STEM education research. All African contributions were subsumed into the ‘Others’ category, which collectively accounted for 19 documents.
Further data analysis focused on networking, overlay visualisation as well as country density (see Figure 4).
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FIGURE 4: Cited authors by country network visualisation on science, technology, engineering, and mathematics education. |
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In visualising academic citation networks, colour represents recency while node size indicates the number of citations.
In Figure 5, the big nodes for the United States and Indonesia indicate the high number of citations. The yellow node for Indonesia indicates that most citations occurred in 2024 (Figure 6).
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FIGURE 5: Cited authors by country overlay visualisation on science, technology, engineering, and mathematics education. |
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FIGURE 6: Citation of authors by country on science, technology, engineering and mathematics education. |
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Discussion
This study examines the patterns that emerge in mathematics within transdisciplinary STEM education research across the world. These developments encompass the strands identified by Thibaut et al. (2018) for STEM integration at various levels of engagement, namely disciplinary integration; problem-centred learning; real-world challenges; inquiry-based learning; and design-based learning, all carried out in cooperation and collaboration.
One clear trend is the exponential increase in the number of research papers in that field, from about 2010 to about 2021 (see Figure 1). Therefore, research in this field is generating increasing interest.
In terms of collaboration, data also show that the United States is heavily involved in STEM education research, followed closely by Indonesia. Trailing but further behind are Thailand, the United Kingdom, Germany, and Australia, in that order. Suffice it to say, developing countries such as South Africa are not represented. The United States and Indonesia dwarf other countries in research on mathematics in STEM education (see Figure 5). Author citation frequency by country follows the same pattern. The United States mainly collaborates with the United Kingdom and European countries. Indonesia and Thailand collaborate with each other, but outside that, they mainly collaborate with the United Kingdom, Canada, Italy, Germany, and the Russian Federation. Although the United States and Indonesia are major countries in STEM education research, they do not collaborate directly. If they do, they do so indirectly via the United Kingdom, which collaborates heavily with both countries, viz., the United States and Indonesia. It is also surprising that the United States collaborates only with Norway. No other country collaborates with Norway.
As for document type, more than 60% of the research came from conference proceedings. Journal articles come second.
In addition to the above, the main sponsors of the research publications are worth noting. By far the largest sponsor is the National Science Foundation of the United States of America. Others, such as the European Commission and the Bill and Melinda Gates Foundation, also feature, but to a much smaller extent. A surprising inclusion is Addis Ababa University in Ethiopia, from Africa.
Consequently, the most-cited authors are from the United States and Indonesia. An interesting finding is that Indonesia has the highest publication frequency in STEM education, suggesting that it has become an important country for this research.
The main sources of citations are the Journal of Physics Conferences and the Frontiers in Education proceedings.
Finally, the study’s outcome underscores Africa’s absence as a singularly visible contributor to the global landscape of STEM education research. While African institutions produced outputs, their volume was insufficient to be registered independently in the bibliometric mapping. The grouping of African countries with other low-output nations highlights the marginalisation of African scholarship in this domain and reinforces the need for targeted strategies to strengthen research capacity, visibility, and collaboration in mathematics within STEM education across the continent.
Recommendations
African research communities need to prioritise collaborative projects that integrate mathematics in STEM contexts. This will foster Mode 2 knowledge production. Building regional networks and working with international partners increases visibility and impact. Based on the Indonesian experience, policymakers should support STEM research initiatives through funding, infrastructure, and training. Publishing in indexed journals and attending international conferences will raise global recognition for African scholarship.
Conclusion
The study’s outcome, which shows Africa’s absence as a singularly visible contributor, corresponds to the distinction between Mode 1 and Mode 2 knowledge production. Mode 1 refers to traditional, discipline-bound, investigator-driven research, while Mode 2 is problem-initiated, transdisciplinary, and joint (Cloete 2021; Gibbons et al. 1994). The fact that African countries did not individually meet the 10-document threshold suggests that mathematics in STEM education research across the continent remains largely fragmented, small-scale, and isolated within Mode 1 structures. These outputs, though present, lack the cooperative density and visibility needed to register in global bibliometric mapping.
By contrast, countries such as the United States and Indonesia demonstrate Mode 2 characteristics: large volumes of research, extensive networks of joint authorship, and participation in cross-border collaborations. Their visibility indicates a change toward problem-driven, networked, and globally impactful knowledge production. Africa’s marginalisation, therefore, is not simply a matter of low output but of being locked into Mode 1 practices that do not scale into transdisciplinary, collective frameworks.
While the descriptive statistics highlight the United States and Indonesia as dominant contributors, the reasons for Indonesia’s prominence warrant closer examination. National STEM reforms and a strong conference publication culture have likely contributed to its rapid rise. This pattern illustrates Mode 2 knowledge production, where collaborative and problem-driven research networks expand visibility. By contrast, African countries remain locked in Mode 1 practices, producing fragmented outputs that fail to register in global bibliometric mapping. These findings are consistent with prior bibliometric reviews (Donthu et al. 2021; Li et al. 2020), which also note concentration of STEM research in a few hubs, but this study adds the novel observation of Indonesia’s emergence as a significant player in mathematics-focused STEM education.
Acknowledgements
Competing interests
The author, Judah P. Makonye, declares that they have no financial or personal relationships that may have inappropriately influenced their writing of this article.
CRediT authorship contribution
Judah P. Makonye: Conceptualisation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualisation, Writing-original draft. The author confirms that this work is entirely their own, has reviewed the article, approved the final version for submission and publication, and takes full responsibility for the integrity of its findings.
Funding information
The author received no financial support for the research, authorship, and/or publication of this article.
Data availability
The data that support the findings of this study are available from Scopus (https://www.scopus.com/). Restrictions apply to the availability of these data, which were used under licence for this study. Data are available from the authors with the permission of Scopus. All figures in this article (Figure 1 to Figure 6) are associated with raw secondary data obtained from Scopus and analysed using VOSviewer, with no additional restrictions applied in the analysis.
Disclaimer
The views and opinions expressed in this article are those of the author and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The author is responsible for this article’s results, findings, and content.
References
Anderson, J., English, L., Fitzallen, N. & Symons, D., 2020, ‘The contribution of mathematics education researchers to the current STEM education agenda’, in J. Anderson, M. Cavanagh & A. Prescott (eds.), Research in mathematics education in Australasia 2016–2019, Springer Singapore, Singapore.
Beane, J.A., 1997, Curriculum integration: Designing the core of democratic education, Teachers College Press, New York, NY.
Brown, S.J., Collins, A. & Duguid, P., 1989, ‘Situated cognition and the culture of learning’, Educational Researcher 18(1), 32–41. https://doi.org/10.3102/0013189X018001032
Chilisa, B., 2012, Indigenous research methodologies, Sage, Los Angeles, CA.
Cloete, N., 2021, Knowledge production and contradictory functions in African higher education, African Minds, Cape Town.
Daugherty, M.K., Kindall, H.D., Carter, V., Swagerty, L.M., Wissehr, C. & Robertson, S., 2017, ‘Integrating informational text and STEM: An innovative and necessary curricular approach’, Journal of STEM Teacher Education 52(1), a4. https://doi.org/10.30707/JSTE52.1
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W.M., 2021, ‘How to conduct a bibliometric analysis: An overview and guidelines’, Journal of Business Research 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
English, L.D., 2016, ‘STEM education K–12: Perspectives on integration’, International Journal of STEM Education 3(1), 1–8. https://doi.org/10.1186/s40594-016-0036-1
English, L.D. & King, D.T., 2019, ‘STEM learning through engineering design: Fourth-grade students’ investigations in aerospace’, International Journal of STEM Education 6(1), 1–15. https://doi.org/10.1186/s40594-019-0178-2
Freeman, B., Marginson, S. & Tytler, R., 2019, ‘An international view of STEM education’, in Lewin & B. Freeman (eds.), STEM Education 2.0, pp. 350–363, Brill, Leiden.
Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P. & Trow, M., 1994, The new production of knowledge: The dynamics of science and research in contemporary societies, Sage, London.
Graven, M., 2015, ‘Going back in order to go forward – Recovery of mathematical foundations for intermediate phase improvement’, Journal of Educational Studies 2015(SI-1), 1–15. https://doi.org/10.24310/TRANS.2015.v1i19.2082
Lave, J. & Wenger, E., 1991, Situated learning: Legitimate peripheral participation, Cambridge University Press, Cambridge.
Lee, J. & Stankov, L., 2018, ‘Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA’, Learning and Individual Differences 65, 50–64. https://doi.org/10.1016/j.lindif.2018.05.009
Lesseig, K., Slavit, D. & Simpson, A., 2023, ‘Transdisciplinary STEM: Examples of student thinking within nonformal learning experiences’, Education Sciences 13(5), a435.
Li, Y., Wang, K., Xiao, Y. & Froyd, J.E., 2020, ‘Research and trends in STEM education: A systematic review of journal publications’, International Journal of STEM Education 7(1), 1–16. https://doi.org/10.1186/2196-7822-1-1
Scopus, 2025, Citation data for mathematics in STEM Education, Elsevier, viewed 14 February 2025, from https://www.scopus.com.
Sikhosana, M., Maphalala, M.C. & Mncube, V., 2025, ‘Learners’ perceptions and participation in STEM subjects in South Africa: Challenges and opportunities’, Cogent Education 12(1), 225–240. https://doi.org/10.1080/2331186X.2025.2556628
Thibaut, L., Ceuppens, S., De Loof, H., De Meester, J., Goovaerts, L., Struyf, A. et al., 2018, ‘Integrated STEM education: A systematic review of instructional practices in secondary education’, European Journal of STEM Education 3(1), a2. https://doi.org/10.20897/ejsteme/85525
Tikly, L., Joubert, M., Barrett, A.M., Bainton, D., Cameron, L. & Doyle, H., 2018, Supporting secondary school STEM education for sustainable development in Africa, Bristol Working Papers in Education Series, University of Bristol, viewed 22 July 2025, from https://www.bristol.ac.uk/media-library/sites/education/documents/bristol-working-papers-in-education/STEM%20education%20in%20Africa%20report.pdf.
Tytler, R., 2020, ‘STEM education for the twenty-first century’, in T. Jorgensen, K. Larkin & J. Van Aalst, (eds.), Integrated approaches to STEM education: An international perspective, pp. 21–43, Springer, Cham.
Van Eck, N.J. & Waltman, L., 2010, ‘Software survey: VOSviewer, a computer program for bibliometric mapping’, Scientometrics 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Vasquez, J., Sneider, C. & Comer, M., 2013, STEM lesson essentials, grades 3–8: Integrating science, technology, engineering, and mathematics, Heinemann, Portsmouth, NH.
Zhou, Y. & Shirazi, R., 2025, ‘STEM career aspirations: A systematic literature review of global trends’, Journal of Science Education and Technology 34(2), 145–162.
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