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MAPPING THE SCHOLARLY LANDSCAPE
OF HEUTAGOGY IN DISTANCE EDUCATION:
A BIBLIOMETRIC ANALYSIS (2020–2025)
MAPEO DEL PANORAMA ACADÉMICO DE LA
HEUTAGOGÍA EN LA EDUCACIÓN A DISTANCIA:
UN ANÁLISIS BIBLIOMÉTRICO (2020–2025)
Juan De Dios Marin Murillo
Universidad Nacional Abierta y a Distancia, Colombia
Juan Camilo Pérez Hernández
Universidad Nacional Abierta y a Distancia, Colombia
Hernán Andrés Carmona Garzón
Universidad Nacional Abierta y a Distancia, Colombia

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DOI: https://doi.org/10.37811/cl_rcm.v10i2.23081
Mapping the Scholarly Landscape of Heutagogy in Distance Education:
A Bibliometric Analysis (2020–2025)
Juan De Dios Marin Murillo1
juan.marin@unad.edu.co
https://orcid.org/0000-0001-6541-1204
Universidad Nacional Abierta y a Distancia
Medellín Colombia
Juan Camilo Pérez Hernández
camilo.perez@unad.edu.co
https://orcid.org/0000-0002-6744-5093
Universidad Nacional Abierta y a Distancia
Medellín Colombia
Hernán Andrés Carmona Garzón
hernan.carmona@unad.edu.co
https://orcid.org/0000-0003-1368-5558
Universidad Nacional Abierta y a Distancia
Medellín Colombia
ABSTRACT
Background: Heutagogy, or self-determined learning, has emerged as a critical pedagogical framework
for distance and online education, particularly following the global shift to digital learning environments
prompted by the COVID-19 pandemic. Despite growing scholarly interest, the global structure, key
contributors, and collaborative patterns of heutagogy research remain insufficiently mapped. Methods:
This study presents a comprehensive bibliometric analysis of 47 Scopus-indexed documents published
between 2020 and 2025, focusing on heutagogy, learner autonomy, and distance education.
Bibliometric indicators including annual production trends, geographic distribution, source analysis
(Bradford's Law), author productivity (Lotka's Law), co-authorship networks, and collaboration
patterns were computed and visualized. The PRISMA 2020 protocol guided document selection.
Results: The analysis reveals an accelerating growth trajectory, with 70% of all publications appearing
after 2022. The corpus spans 167 unique authors across 44 publication sources from over 15 countries,
with Malaysia, Australia, and Kazakhstan among the most productive nations. Journal articles dominate
(68.1%), and English is the predominant language. Author productivity follows Lotka's inverse square
law, and source concentration adheres to Bradford's Law of scattering. Co-authorship networks reveal
predominantly regional collaboration clusters with limited international co-authorship. Conclusions:
Heutagogy research for distance education is a rapidly growing but still emerging field, characterized
by fragmented collaboration and geographic concentration. Strengthening international research
networks and expanding non-English scholarship are identified as priorities for the field's consolidation.
Keywords: heutagogy, self-determined learning, distance education, online learning, bibliometric
analysis, science mapping, lotka's law, bradford's law, co-authorship networks, prisma 2020
1 Autor principal
Correspondencia: juan.marin@unad.edu.co

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Mapeo del Panorama Académico de la Heutagogía en la Educación a
Distancia: Un Análisis Bibliométrico (2020–2025)
RESUMEN
Antecedentes: La heutagogía, o aprendizaje autodeterminado, ha surgido como un marco pedagógico
fundamental para la educación a distancia y en línea, especialmente tras el cambio global hacia entornos
de aprendizaje digital impulsado por la pandemia de COVID-19. A pesar del creciente interés
académico, la estructura global, los principales contribuyentes y los patrones de colaboración de la
investigación en heutagogía aún no se han explorado completamente. Métodos: Este estudio presenta
un análisis bibliométrico exhaustivo de 47 documentos indexados en Scopus publicados entre 2020 y
2025, centrado en la heutagogía, la autonomía del estudiante y la educación a distancia. Se calcularon
y visualizaron indicadores bibliométricos que incluyen tendencias de producción anuales, distribución
geográfica, análisis de fuentes (Ley de Bradford), productividad de los autores (Ley de Lotka), redes de
coautoría y patrones de colaboración. El protocolo PRISMA 2020 guió la selección de documentos.
Resultados: El análisis revela una trayectoria de crecimiento acelerado, con el 70% de todas las
publicaciones apareciendo después de 2022. El corpus abarca 167 autores únicos en 44 fuentes de
publicación de más de 15 países, siendo Malasia, Australia y Kazajistán algunas de las naciones más
productivas. Los artículos de revistas predominan (68,1%) y el inglés es el idioma predominante La
productividad de los autores sigue la ley del cuadrado inverso de Lotka, y la concentración de fuentes
se ajusta a la ley de dispersión de Bradford. Las redes de coautoría revelan predominantemente clústeres
de colaboración regional con una coautoría internacional limitada. Conclusiones: La investigación en
heutagogía para la educación a distancia es un campo en rápido crecimiento, pero aún emergente,
caracterizado por una colaboración fragmentada y una concentración geográfica. El fortalecimiento de
las redes de investigación internacionales y la expansión de la producción académica en idiomas
distintos del inglés se identifican como prioridades para la consolidación del campo.
Palabras clave: heutagogía, aprendizaje autodeterminado, educación a distancia, aprendizaje en línea,
análisis bibliométrico, mapeo de la ciencia, ley de Lotka, ley de Bradford, redes de coautoría, PRISMA
2020
Artículo recibido 02 febrero 2026
Aceptado para publicación: 27 febrero 2026

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INTRODUCTION
Heutagogy, defined by Hase and Kenyon (2000, p. 2) as 'the study of self-determined learning,' extends
the continuum of pedagogical approaches beyond pedagogy (teacher-directed) and andragogy (self-
directed) toward full learner autonomy. In a heutagogical framework, learners not only determine what
and how they learn but also identify what is important to learn and how to assess their own
understanding (Blaschke, 2012). This approach has gained particular relevance in distance and online
education, where learner independence and self-regulation are prerequisites for success (Moore, 1993;
Garrison, 2011).
The global disruption caused by the COVID-19 pandemic dramatically accelerated the adoption of
online and distance education modalities, compelling institutions worldwide to rethink their
pedagogical approaches (Bozkurt et al., 2020; Hodges et al., 2020). Heutagogy, with its emphasis on
learner agency, capability development, and double-loop learning, emerged as a natural fit for the
challenges of emergency remote teaching and its aftermath (Blaschke & Hase, 2019). As education
systems transition from emergency responses to intentional digital transformation, understanding the
state of heutagogy research becomes essential for evidence-based pedagogical design.
Bibliometric analysis provides systematic, quantitative tools for mapping the intellectual structure,
evolution, and collaborative patterns of research fields (Donthu et al., 2021; Zupic & Cater, 2015).
Established bibliometric laws—Bradford's Law of scattering (Bradford, 1934), Lotka's inverse square
law of author productivity (Lotka, 1926), and Price's law of scientific growth (de Solla Price, 1965)—
offer normative benchmarks for assessing a field's maturation. Co-authorship network analysis
(Newman, 2001) and science mapping techniques (van Eck & Waltman, 2010; Aria & Cuccurullo,
2017) further illuminate the social and intellectual dimensions of knowledge production.
While systematic reviews of heutagogy have appeared (Blaschke, 2012; Blaschke & Hase, 2016; Jones
et al., 2019), bibliometric analyses specifically targeting the intersection of heutagogy, learner
autonomy, and distance education remain absent from the literature. This study addresses the following
research questions: (RQ1) What is the temporal evolution and growth trajectory of heutagogy research
in distance education? (RQ2) Which countries, institutions, and publication venues are most
productive? (RQ3) How is author productivity distributed, and does it conform to Lotka's Law? (RQ4)

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What collaboration patterns characterize this research community? By answering these questions, this
study provides the first comprehensive bibliometric mapping of heutagogy in the context of distance
and online education.
METHODOLOGY
Data Source and Search Strategy
The corpus was constructed through a systematic search of the Scopus database using the query terms
'heutagogy,' 'self-determined learning,' 'learner autonomy,' and 'distance education' combined with
Boolean operators. The search was restricted to the period 2020–2025 to capture the post-pandemic
surge in research activity. The PRISMA 2020 protocol (Page et al., 2021) guided the document selection
process (see Figure 1). After screening and eligibility assessment, 47 documents were retained for
analysis. The dataset was exported in CSV format and preprocessed using a custom Python pipeline to
handle the non-standard Scopus export format and ensure data integrity.
Bibliometric Indicators
The following bibliometric analyses were performed: (1) Annual scientific production to assess
temporal growth (de Solla Price, 1965); (2) Geographic production and international collaboration
mapping; (3) Source analysis including Bradford's Law (Bradford, 1934; Garfield, 1971); (4) Author
productivity analysis via Lotka's inverse square law (Lotka, 1926; Egghe, 2005); (5) Co-authorship
network construction and visualization (Newman, 2001; van Eck & Waltman, 2010); (6) Multi-country
(MCP) versus single-country (SCP) collaboration analysis; and (7) Document type, language, and open
access profiling.
Tools and VisualizationAll analyses and visualizations were generated using Python 3.x with pandas,
matplotlib, seaborn, networkx, and scipy.
Figures were exported as JPG at 300 DPI. The analytical pipeline follows the methodological
framework established by Aria and Cuccurullo (2017) in the bibliometrix R-package, adapted for
Python-based execution. Network layouts employ the Fruchterman-Reingold force-directed algorithm
for co-authorship networks.

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RESULTS
Overview of the Corpus
The dataset comprises 47 documents published between 2020 and 2025 by 167 unique authors across
44 publication sources. Author keywords total 181 unique terms. Journal articles constitute the majority
with 32 documents (68.1%), followed by conference papers (6, 12.8%), book chapters (6, 12.8%), and
reviews (3, 6.4%). The mean citation count is 8.7 per document, with a maximum of 67 citations.
English is the dominant language of publication. The collaborative nature of the field is reflected in the
average number of co-authors per document, consistent with findings by Wuchty et al. (2007) on the
increasing dominance of team science.
Annual Scientific Production and Growth
The temporal evolution of scholarly output reveals a clear acceleration in heutagogy research for
distance education (Figures 2 and 3). Production levels were modest in 2020–2021, consistent with the
initial response to the COVID-19 disruption. A marked increase is observed from 2022 onward, with
approximately 70% of all documents published after 2022. This growth pattern aligns with de Solla
Price's (1965) exponential growth model for emerging scientific fields and reflects the post-pandemic
mainstreaming of online learning approaches. The cumulative production curve (Figure 3) exhibits a
steep upward trajectory in recent years, suggesting the field has not yet reached saturation. Average
citation rates (Figure 4) show that earlier publications have accumulated more citations, as expected
due to the citation time window advantage (Moed, 2005).
Figure 1. Annual Scientific Production (2020–2025)

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Figure 2. Cumulative Scientific Production.
Figure 3. Average Citations per Year.
Geographic Distribution and Collaboration
The geographic analysis reveals a globally distributed but regionally concentrated research landscape
(Figure 5). Countries in Southeast Asia, particularly Malaysia, show strong engagement with
heutagogical approaches, reflecting the region's active investment in educational technology and
distance learning (Bozkurt et al., 2020). Australia and New Zealand, where heutagogy was originally
conceptualized by Hase and Kenyon (2000), maintain a visible presence.

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European and Central Asian contributions from Kazakhstan add diversity to the research geography.
The country impact analysis (Figure 6) shows that citation influence does not always correspond to
productivity volume, suggesting that some less prolific countries produce higher-impact research. The
collaboration heatmap (Figure 7) reveals limited international co-authorship, with most collaboration
occurring within national boundaries. The MCP/SCP analysis (Figure 8) quantifies this pattern, showing
that single-country papers significantly outnumber multi-country collaborations. Institutional
affiliations (Figure 9) indicate a diverse range of contributing universities without strong concentration
in any single institution.
Figure 4. Most Productive Countries.
Figure 5. Country Impact by Total Citations.

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Figure 6. Country Collaboration Heatmap.
Figure 7. International vs. Domestic Collaboration.

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Figure 8. Top Institutional Affiliations.
Figure 9. Country Production (Vertical Layout).
Source Analysis and Bradford's Law
The corpus is distributed across 44 unique publication sources, reflecting the interdisciplinary nature of
heutagogy research. Source analysis (Figure 10) identifies the leading journals, with specialized
education and distance learning outlets predominating. The source growth analysis (Figure 11) tracks
cumulative contributions from the top 5 journals, revealing which venues have been the most consistent
outlets for heutagogy research. Bradford's Law of Scattering (Bradford, 1934) is confirmed by the
cumulative source distribution (Figure 12), where a small nucleus of approximately 8–10 sources
accounts for one-third of the total output, consistent with the typical concentration patterns observed in

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bibliometric studies (Garfield, 1971). Source impact analysis (Figure 13) reveals that citation
concentration does not perfectly mirror publication volume, suggesting that some lower-output sources
publish higher-impact heutagogy research. The publisher analysis (Figure 14) shows the distribution
across academic publishers.
Figure 10. Top Sources by Publication Output.
Figure 11. Source Growth Over Time (Top 5 Journals).

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Figure 12. Bradford's Law Distribution.
Figure 13. Source Impact by Total Citations.
Figure 14. Top Academic Publishers.

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Author Productivity and Lotka's Law
The author productivity distribution (Figure 15) identifies the most prolific contributors to heutagogy
research. With 167 unique authors contributing to 47 documents, the field exhibits a dispersed
authorship pattern characteristic of an emerging interdisciplinary domain.
Lotka's Law (Figure 16) is confirmed by the log-log plot of author productivity, where the vast majority
of authors (approximately 85–90%) contributed a single publication, with progressively fewer authors
publishing multiple papers (Lotka, 1926; Egghe, 2005).
The author dominance analysis (Figure 17) examines first-authorship patterns, revealing which
researchers most frequently lead research projects. The author timeline (Figure 18) maps publication
activity over time for the top 10 authors, showing whether their engagement is sustained or concentrated
in specific years.
The author impact proxy (Figure 19), based on publication count within the collection, provides a
simplified indicator of sustained scholarly engagement with heutagogy.
Figure 15. Most Productive Authors (Top 15).

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Figure 16. Lotka's Law: Author Productivity Distribution.
Figure 17. Author Dominance by First Authorship.
Figure 18. Top Authors Publication Timeline.

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Figure 19. Author Impact Proxy (Publication Count).
Co-Authorship Networks and Collaboration Structures
The co-authorship network (Figure 20) visualizes collaborative relationships among the most
productive authors. The network reveals distinct clusters of researchers who frequently collaborate,
largely organized along institutional or national lines. These clusters suggest that heutagogy research,
while global in scope, remains operationally fragmented, with limited cross-cluster bridging (Newman,
2001). The predominantly regional character of collaboration is consistent with findings from other
emerging educational research fields (Donthu et al., 2021).
Figure 20. Co-Authorship Network (Top 25 Authors).

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Document Types, Languages, and Open Access
The document type distribution (Figure 21) confirms that journal articles dominate the collection
(68.1%), consistent with the field's orientation toward peer-reviewed empirical research. Conference
papers and book chapters each represent 12.8%, indicating that heutagogy research is also disseminated
through academic conferences and edited volumes. The language analysis (Figure 22) shows the
overwhelming predominance of English, with limited representation of other languages. The open
access distribution (Figure 23) characterizes the accessibility landscape of the corpus.
Figure 21. Document Types Distribution.
Figure 22. Languages of Publication.

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Figure 23. Open Access Distribution.
PRISMA 2020 Flow Diagram and Top Cited Papers
The PRISMA 2020 flow diagram (Figure 24) documents the systematic search and selection process
from initial identification through screening and eligibility to final inclusion of 47 documents (Page et
al., 2021). The top cited papers (Figure 25) identify the most influential contributions in the collection,
with the highest-cited paper receiving 67 citations. These foundational works have shaped the discourse
on heutagogy in distance education and provide the theoretical scaffolding upon which subsequent
research builds.
Figure 24. PRISMA 2020 Flow Diagram.

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Figure 25. Top Cited Papers.
DISCUSSION
Growth Trajectory and Field Maturation
The exponential increase in publications after 2022 demonstrates that heutagogy research for
distance education has moved beyond early theoretical exploration into a phase of rapid empirical
expansion. This post-pandemic growth mirrors broader trends in educational technology research
(Donthu et al., 2021) and reflects the widespread institutional recognition that self-determined learning
is essential for effective online education (Blaschke & Hase, 2019; Bozkurt et al., 2020). The growth
trajectory suggests the field is approaching a transition from rapid expansion to consolidation, consistent
with de Solla Price's (1965) model of scientific growth. However, the relatively small corpus (47
documents) indicates that heutagogy research remains in its early growth phase compared to more
established pedagogical paradigms.
Geographic Distribution and Collaboration Gaps
The geographic analysis reveals a globally distributed but fragmented research landscape. The strong
presence of Southeast Asian institutions, particularly from Malaysia, reflects the region's investment in
technology-enhanced distance education. The representation of Australia and New Zealand is expected
given heutagogy's origins in Australian scholarship (Hase & Kenyon, 2000). However, the limited
international co-authorship revealed by the MCP/SCP analysis and the collaboration heatmap represents
a significant gap. Strengthening cross-national research networks would enhance the generalizability
and cultural diversity of heutagogy research (Newman, 2001; Wuchty et al., 2007).

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Source Concentration and Disciplinary Identity
Bradford's Law analysis confirms that heutagogy research is distributed across a wide range of
publication venues without a single dominant journal. This pattern is characteristic of an
interdisciplinary field that has not yet crystallized its disciplinary identity (Bradford, 1934; Garfield,
1971). The absence of a dedicated heutagogy journal means that researchers must distribute their work
across education, technology, and distance learning outlets, which may limit visibility and coherent
field development. The emergence of specialized journals or dedicated special issues would accelerate
the field's consolidation.
Author Productivity and Team Science
The confirmation of Lotka's Law indicates that heutagogy research productivity follows the typical
power-law distribution, with a small group of highly productive authors and a large majority of single-
publication contributors (Lotka, 1926; Egghe, 2005). The dispersed authorship pattern, combined with
the relatively small corpus, suggests that heutagogy lacks the 'invisible college' structure (de Solla Price,
1963) observed in more mature research fields. Building sustained research programs and collaborative
networks will be essential for developing a cohesive scholarly community around heutagogy in distance
education.
Limitations
Several limitations should be acknowledged. First, the corpus is limited to Scopus-indexed documents,
which may exclude relevant literature in regional databases, conference proceedings not indexed by
Scopus, or grey literature (Hirsch, 2005; Moed, 2005). Second, the restriction to 2020–2025 captures
the post-pandemic period but excludes earlier foundational works on heutagogy. Third, the non-
standard Scopus export format required custom preprocessing, which may introduce minor parsing
artifacts despite validation. Fourth, the relatively small corpus size (47 documents) limits the robustness
of some bibliometric indicators. Future research should expand the temporal range, include Web of
Science and Google Scholar, and integrate open citation data from OpenAlex (Priem et al., 2022).

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CONCLUSIONS
This bibliometric analysis of 47 documents (2020–2025) provides the first comprehensive mapping of
the scholarly landscape of heutagogy in distance education. Five key conclusions emerge:
The field is in a phase of rapid growth, with approximately 70% of publications appearing after 2022,
driven by the post-pandemic mainstreaming of online learning and digital pedagogical transformation
(Bozkurt et al., 2020; Blaschke & Hase, 2019).
Geographic production is globally distributed but concentrated in Southeast Asia, Oceania, and Central
Asia, with limited international co-authorship indicating fragmented collaboration networks that require
strengthening (Newman, 2001).
Source analysis confirms Bradford's Law, with publications dispersed across 44 venues and no single
dominant journal, suggesting the field has not yet developed a clear disciplinary identity (Bradford,
1934).
Author productivity follows Lotka's Law (Lotka, 1926), with a dispersed authorship pattern
characteristic of an emerging field that lacks established 'invisible colleges' (de Solla Price, 1963).
Strengthening international research networks, establishing dedicated publication venues, and
expanding non-English and multi-database searches represent the most pressing priorities for the
consolidation of heutagogy research in distance education.
Future research directions include: extending the temporal range to capture pre-pandemic foundations;
integrating multiple databases (Scopus, Web of Science, OpenAlex) for broader coverage; conducting
longitudinal studies of collaboration evolution; performing thematic and conceptual analyses (which
are addressed in a companion study); and exploring the intersection of heutagogy with emerging
technologies such as artificial intelligence, learning analytics, and adaptive educational platforms.
Author Contributions
Conceptualization, S.G.; methodology, S.G.; software, S.G.; validation, S.G.; formal analysis, S.G.;
data curation, S.G.; writing—original draft preparation, S.G.; writing—review and editing, S.G.;
visualization, S.G. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.

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Institutional Review Board Statement
Not applicable. This study did not involve human participants or sensitive data.
Informed Consent Statement
Not applicable.
Data Availability Statement
The dataset analyzed in this study was exported from the author's personal Zotero library. The raw
bibliometric data and Python analysis scripts are available from the corresponding author upon
reasonable request. Processed metadata can also be retrieved from open databases such as OpenAlex
(Priem et al., 2022) using the included DOIs.
Conflicts of Interest
The author declares no conflict of interest.
Table 1Appendix A. Summary Statistics
Metric Value
Total Documents 47
Time Span 2020–2025
Unique Authors 167
Unique Sources 44
Unique Keywords 181
Journal Articles 32 (68.1%)
Conference Papers 6 (12.8%)
Book Chapters 6 (12.8%)
Reviews 3 (6.4%)
Mean Citations 8.7 per document
Max Citations 67
Total Citations 409
Most Productive Author Mazlan, C.A.N. (2 docs)
Dominant Keyword heutagogy (9 occurrences)

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REFERENCES
Aria, M. & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping
analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Blaschke, L. M. (2012). Heutagogy and lifelong learning: A review of heutagogical practice and self-
determined learning. The International Review of Research in Open and Distributed Learning,
13(1), 56–71. https://doi.org/10.19173/irrodl.v13i1.1076
Blaschke, L. M. & Hase, S. (2016). Heutagogy: A holistic framework for creating twenty-first-century
self-determined learners. In B. Gros, Kinshuk, & M. Maina (Eds.), The future of ubiquitous
learning (pp. 25–40). Springer. https://doi.org/10.1007/978-3-662-47724-3_2
Blaschke, L. M. & Hase, S. (2019). Heutagogy and digital media networks: Setting students on the path
to lifelong learning. Pacific Journal of Technology Enhanced Learning, 1(1), 1–14.
https://doi.org/10.24135/pjtel.v1i1.1
Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., ... Paskevicius, M. (2020). A
global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a
time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1–126.
https://doi.org/10.5281/zenodo.3878572
Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86.
Callon, M., Courtial, J.-P. & Laville, F. (1991). Co-word analysis as a tool for describing the network
of interactions between basic and technological research: The case of polymer chemistry.
Scientometrics, 22(1), 155–205. https://doi.org/10.1007/BF02019280
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E. & Herrera, F. (2011). An approach for detecting,
quantifying, and visualizing the evolution of a research field: A practical application to the
Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166.
https://doi.org/10.1016/j.joi.2010.10.002
de Solla Price, D. J. (1963). Little science, big science. Columbia University Press.
de Solla Price, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510–515.
https://doi.org/10.1126/science.149.3683.510

pág. 526
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
Egghe, L. (2005). Power laws in the information production process: Lotkaian informetrics. Elsevier
Academic Press.
Garfield, E. (1971). Bradford's Law and related statistical patterns. Essays of an Information Scientist,
4(19), 476–483.
Garrison, D. R. (2011). E-learning in the 21st century: A framework for research and practice (2nd ed.).
Routledge. https://doi.org/10.4324/9780203838761
Hase, S. & Kenyon, C. (2000). From andragogy to heutagogy. ultiBASE Articles, 5(3), 1–10.
Hase, S. & Kenyon, C. (2007). Heutagogy: A child of complexity theory. Complicity: An International
Journal of Complexity and Education, 4(1), 111–118. https://doi.org/10.29173/cmplct8766
Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the
National Academy of Sciences, 102(46), 16569–16572.
https://doi.org/10.1073/pnas.0507655102
Hodges, C., Moore, S., Lockee, B., Trust, T. & Bond, A. (2020). The difference between emergency
remote teaching and online learning. Educause Review, 27(1), 1–12.
Jones, C., Penaluna, K. & Penaluna, A. (2019). The promise of andragogy, heutagogy, and
academagogy to enterprise and entrepreneurship education pedagogy. Education + Training,
61(9), 1170–1186. https://doi.org/10.1108/ET-10-2018-0211
Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington
Academy of Sciences, 16(12), 317–323.
Moed, H. F. (2005). Citation analysis in research evaluation. Springer. https://doi.org/10.1007/1-4020-
3714-7
Moore, M. G. (1993). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of
distance education (pp. 22–38). Routledge.
Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the
National Academy of Sciences, 98(2), 404–409. https://doi.org/10.1073/pnas.021544898

pág. 527
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... Moher,
D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic
reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Priem, J., Piwowar, H. & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors,
venues, institutions, and concepts. arXiv preprint arXiv:2205.01833.
https://doi.org/10.48550/arXiv.2205.01833
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
Wuchty, S., Jones, B. F. & Uzzi, B. (2007). The increasing dominance of teams in production of
knowledge. Science, 316(5827), 1036–1039. https://doi.org/10.1126/science.1136099
Zupic, I. & Cater, T. (2015). Bibliometric methods in management and organization. Organizational
Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629