Mobile Learning in Higher Education: Insights from a Bibliometric Analysis of the Body of Knowledge

Godwin Kaisara, Kelvin Joseph Bwalya

Abstract


Mobile learning is a research domain that has gained wide prominence in contemporary Information and Communication Technology (ICT) literature. As a result, there is a need for periodic and extensive review studies to keep abreast with the latest scholarly trends. Nevertheless, review studies on mobile learning, particularly in the post-COVID-19 era, are still limited. This article presents a bibliometric overview of mobile learning in higher education literature published between 1 January 2002 and 15 November 2022. The methodology used in this research is enshrined in the core principles of scientometrics forming the basis of the bibliometric approach utilised in the study. The articles for analysis were extracted from the Web of Science (WoS) database and analysed according to defined bibliometric indicators. The VOSviewer software tool (version 1.6.18) was employed in mapping the bibliometric articles. The findings of this research reveal that mobile learning scholarship has grown consistently in the period of analysis covered in this study. It was observed in the bibliometric analysis that the most productive countries in mobile learning in higher education are the USA and China. The most influential author is M.A. Almaiah. In the recent past, universities in the Middle East have demonstrated an excellent growth projectile in mobile learning research. Education and Information Technologies produced most content on mobile learning research demonstrating its stature as a leading publication platform advancing scholarly debate on mobile learning relating to context, augmented reality, COVID-19, continuance intention and knowledge. From the foregoing, possible future research avenues are discussed.

https://doi.org/10.26803/ijlter.22.6.9


Keywords


mobile learning; higher education; bibliometric analysis; VOSviewer; trends

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References


Abhishek, & Srivastava, M. (2021). Mapping the influence of influencer marketing: a bibliometric analysis. Marketing Intelligence and Planning, 39(7), 979–1003. https://doi.org/10.1108/MIP-03-2021-0085

Aksnes, D. W., Langfeldt, L., & Wouters, P. (2019). Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories. SAGE Open, 9(1). https://doi.org/10.1177/2158244019829575

Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2020). Towards a conceptual model for examining the impact of knowledge management factors on mobile learning acceptance. Technology in Society, 61(September 2019), 1–13. https://doi.org/10.1016/j.techsoc.2020.101247

Alshehri, A., & Cumming, T. M. (2020). Mobile Technologies and Knowledge Management in Higher Education Institutions: Students’ and Educators’ Perspectives. World Journal of Education, 10(1), 12–22. https://doi.org/10.5430/wje.v10n1p12

Basak, S. K., Wotto, M., & Belanger, P. (2018). E-learning, m-learning and d-learning: conceptual definition and comparative analysis. E-Learning and Digital Media, 15(4), 191–216. https://doi.org/10.1177/2042753018785180

Bonilla, C. A., Merigo, J., & Torres-Abad, C. (2015). Economics in Latin America: a bibliometric analysis. Scientometrics, 105, 1239–1252. https://doi.org/10.1007/s11192-015-1747-7

Brainard, J. (2020). Scientists are drowning in COVID-19 papers. Can new tools keep them afloat? Science Insider. https://doi.org/10.1126/science.abc7839

Chadegani, A. A., Salehi, H., Yunus, M., Farhadi, H., Fooladi, M., Farhadi, M., & Ebrahim, N. A. (2013). A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases. Asian Social Science, 9(5), 18–26. https://doi.org/10.5539/ass.v9n5p18

Chao, C. M. (2019). Factors determining the behavioral intention to use mobile learning: An application and extension of the UTAUT model. Frontiers in Psychology, 10(JULY), 1–14. https://doi.org/10.3389/fpsyg.2019.01652

Chibisa, A., & Mutambara, D. (2022). Determinants of High School Learners’ Continuous Use of Mobile Learning during the Covid-19 Pandemic. International Journal of Learning, Teaching and Educational Research, 21(3), 1–21. https://doi.org/10.26803/ijlter.21.3.1

Chigbu, U. E., Atiku, S. O., & Plessis, C. C. Du. (2023). The Science of Literature Reviews: Searching, Identifying, Selecting, and Synthesising. Publications, 11(2), 1–16.

Coccia, M. (2018). General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology. Scientometrics, 117(2), 1265–1283. https://doi.org/10.1007/s11192-018-2902-8

Djeki, E., Dégila, J., Bondiombouy, C., & Alhassan, M. H. (2022). E-learning bibliometric analysis from 2015 to 2020. Journal of Computers in Education, 0123456789, 727–754. https://doi.org/10.1007/s40692-021-00218-4

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Marc, W. (2021). How to conduct a bibliometric analysis : An overview and guidelines. Journal of Business Research, 133(April), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Elaish, M. M., Shuib, L., Ghani, N. A., Mutjaba, G., & Ebrahim, N. A. (2019). A Bibliometric Analysis of M-Learning from Topic Inception to 2015. International Journal of Mobile Learning and Organisation, 13(1), 91–112. https://doi.org/10.1504/IJMLO.2019.096470

Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z

European Commission. (2022). Multidisciplinary teams for digital-ready policymaking. https://joinup.ec.europa.eu/sites/default/files/document/2022-01/Issue paper - Multidisciplinary teams for digital-ready policymaking_0.pdf

Fassin, Y. (2021). Research on Covid-19: a disruptive phenomenon for bibliometrics. Scientometrics, 126(6), 5305–5319. https://doi.org/10.1007/s11192-021-03989-w

Frohberg, D., Göth, C., & Schwabe, G. (2009). Mobile Learning projects - a critical analysis of the state of the art: Original article. Journal of Computer Assisted Learning, 25(4), 307–331. https://doi.org/10.1111/j.1365-2729.2009.00315.x

Goksu, I. (2021). Bibliometric mapping of mobile learning. Telematics and Informatics, 56(August 2020), 101491. https://doi.org/10.1016/j.tele.2020.101491

Hamidi, H., & Chavoshi, A. (2018). Analysis of the essential factors for the adoption of mobile learning in higher education: A case study of students of the University of Technology. Telematics and Informatics, 35(4), 1053–1070. https://doi.org/10.1016/j.tele.2017.09.016

Hou, L., Wu, Q., & Xie, Y. (2022). Does early publishing in top journals really predict long-term scientific success in the business field? Scientometrics, 127(11), 6083–6107. https://doi.org/10.1007/s11192-022-04509-0

Imtinan, U., Chang, V., & Issa, T. (2012). MOBILE LEARNING-THEORETICAL UNDERPINNINGS. In P. Kommers, T. Issa, & P. Isaías (Eds.), IADIS International Conference on Internet Technologies & Society (Issue 2004, pp. 190–197).

Inamdar, Z., Raut, R., Narwane, V. S., Gardas, B., Narkhede, B., & Sagnak, M. (2021). A systematic literature review with bibliometric analysis of big data analytics adoption from period 2014 to 2018. Journal of Enterprise Information Management, 34(1), 101–139. https://doi.org/10.1108/JEIM-09-2019-0267

Kaisara, G., & Bwalya, K. J. (2022). Trends in Mobile Learning Research in sub-Saharan Africa: A Systematic Literature Review. International Journal of Education and Development Using Information and Communication Technology, 18(2), 231–244.

Khan, F. M., & Gupta, Y. (2022). A bibliometric analysis of mobile learning in the education sector. Interactive Technology and Smart Education, 19(3), 338–359. https://doi.org/10.1108/ITSE-03-2021-0048

Khodabandelou, R., Fathi, M., Amerian, M., & Fakhraie, M. R. (2022). A comprehensive analysis of the 21st century’s research trends in English Mobile Learning: a bibliographic review of the literature. International Journal of Information and Learning Technology, 39(1), 29–49. https://doi.org/10.1108/IJILT-07-2021-0099

Krull, G., & Duart, J. M. (2017). Research trends in mobile learning in higher education: A systematic review of articles (2011 - 2015). International Review of Research in Open and Distance Learning, 18(7), 1–23. https://doi.org/10.19173/irrodl.v18i7.2893

Kuzior, A., & Sira, M. (2022). A Bibliometric Analysis of Blockchain Technology Research Using VOSviewer. Sustainability (Switzerland), 14, 1–15.

Liaw, S., Hatala, M., & Huang, H. (2010). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computers & Education, 54(2), 446–454. https://doi.org/10.1016/j.compedu.2009.08.029

Meng, L., Wen, K., Brewin, R., & Wu, Q. (2020). Knowledge Atlas on the Relationship between Urban Street Space and Residents’ Health - A Bibliometric Analysis Based on VOSviewer and CiteSpace. Sustainability (Switzerland), 12, 1–20.

Moosavi, L. (2020). The decolonial bandwagon and the dangers of intellectual decolonisation. International Review of Sociology—Revue Internationale de Sociologie, 30(2), 332–354. https://doi.org/10.1080/03906701.2020.1776919

Okeji, C. C. (2019). Research output of librarians in the field of library and information science in Nigeria: a bibliometric analysis from 2000-March, 2018. Collection and Curation, 38(3), 53–60. https://doi.org/10.1108/CC-04-2018-0012

Park, Y. (2011). Pedagogical Framework for Mobile Learning: Categorizing Educational Applications of Mobile Technologies into Four Types. International Review of Research in Open and Distributed Learning, Volume 12(Issue 2), 78–102.

Plancikova, D., Duric, P., & O’May, F. (2021). High-income countries remain overrepresented in highly ranked public health journals: a descriptive analysis of research settings and authorship affiliations. Critical Public Health, 31(4), 487–493. https://doi.org/10.1080/09581596.2020.1722313

Pranckute, R. (2021). Web of Science ( WoS ) and Scopus: The Titans of Bibliographic Information in Today ’ s Academic World. Publications, 9(12), 1–59.

Sharara, H., Getoor, L., & Norton, M. (2011). Active surveying: A probabilistic approach for identifying key opinion leaders. IJCAI International Joint Conference on Artificial Intelligence, 1485–1490. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-250

Singh, V. K., Singh, P., Karmakar, M., Leta, J., & Mayr, P. (2021). The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis. Scientometrics, 126(6), 5113–5142. https://doi.org/10.1007/s11192-021-03948-5

SpringerNature. (2019). The top 10 countries for scientific research in 2018. Nature Index. https://www.nature.com/nature-index/news-blog/top-ten-countries-research-science-twenty-nineteen

Thompson, M. P. A., & Walsham, G. (2004). Placing Knowledge Management in Context. Journal of Management Studies, 41(5), 725–747.

Traxler, J. (2016). Mobile Learning Research: the Focus for Policy-Makers. Journal of Learning for Development, 3(2), 7–25. https://doi.org/10.56059/jl4d.v3i2.150

UNESCO. (2022). National distance learning programmes in response to the COVID-19 education disruption: case study of the Kingdom of Saudi Arabia. https://unesdoc.unesco.org/ark:/48223/pf0000381533

Valenzuela Fernandez, L. M., Nicolas, C., Merigó, J. M., & Arroyo-Cañada, F. J. (2019). Industrial marketing research: a bibliometric analysis (1990-2015). Journal of Business and Industrial Marketing, 34(3), 550–560. https://doi.org/10.1108/JBIM-07-2017-0167


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