Examining mobile learning trends 2003–2008: a categorical meta-trend analysis using text mining techniques
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This study investigated the longitudinal trends of academic articles in Mobile Learning (ML) using text mining techniques. One hundred and nineteen (119) refereed journal articles and proceedings papers from the SCI/SSCI database were retrieved and analyzed. The taxonomies of ML publications were grouped into twelve clusters (topics) and four domains, based on abstract analysis using text mining. Results include basic bibliometric statistics, trends in frequency of each topic over time, predominance in each topic by country, and preferences for each topic by journal. Key findings include the following: (a) ML articles increased from 8 in 2003 to 36 in 2008; (b) the most popular domain in current ML is Effectiveness, Evaluation, and Personalized Systems; (c) Taiwan is most prolific in five of the twelve ML clusters; (d) ML research is at the Early Adopters stage; and (e) studies in strategies and framework will likely produce a bigger share of publication in the field of ML.
KeywordsMobile learning M-learning Text mining Bibliometrics Mobile learning trends
- Attewell, J. (2005). Mobile technologies and learning: A technology update and m-learning project summary. London, UK: Learning and Skills Development Agency. Retrieved from http://www.m-learning.org/docs/The%20m-learning%20project%20-%20technology%20update%20and%20project%20summary.pdf.
- Attewell, J., & Savill-Smith, C. (2004). Learning with mobile devices: Research and development. London, UK: Learning and Skills Development Agency.Google Scholar
- Executive Yuan of the Republic of China. (2005). National Science and Technology Program for e-learning [website]. Retrieved from http://elnpweb.ncu.edu.tw/old/english/english1.htm.
- Fayyad, U. M., Pitatesky-Shapiro, G., Smyth, P., & Uthurasamy, R. (1996). Advances in knowledge discovery and data mining, AAAI/MIT Press.Google Scholar
- Feldman, R., & Dagan, I. (1995). Knowledge discovery in textual databases (KDT). Proceedings of the first international conference on knowledge discovery and data mining (KDD-95), 112–117.Google Scholar
- Gall, M. D., Borg, W. R., & Gall, J. P. (1996). Educational research: An introduction (6th ed.). White Plains, NY: Longman.Google Scholar
- Geddes, S. (2004). Mobile learning in the 21st century: Benefit for learners. Knowledge Tree e-journal. Retrieved from https://olt.qut.edu.au/udf/OLTCONFERENCEPAPERS/gen/static/papers/Cobcroft_OLT2006_paper.pdf.
- Keshaval, G. A. G., & Gowda, M. P. (2008). ACM transaction on information systems (1989–2006): A bibliometric study. Information Studies, 14(4), 223–234.Google Scholar
- Gwet, K. L. (2010). Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters. Gaithersburg, MD: Advanced Analytics, LLC.Google Scholar
- Hung, J. L. (2010). Trends of e-learning research from 2000–2008: use of text mining and bibliometrics. British Journal of Educational Technology. Retrieved from http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291467-8535/earlyview.
- Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
- Johnson, S. D., Aragon, S. R., & Shaik, N. (2000). Comparative analysis of learner satisfaction and learning outcomes in online and face-to-face learning environments. Journal of Interactive Learning Research, 11(1), 29–49.Google Scholar
- Lehner, F., & Nösekabel, H. (2002). The role of mobile devices in e-learning: First experiences with a wireless e-learning environment. Paper presented at IEEE International Workshop on Wireless and Mobile Technologies in Education, Växjö, Sweden.Google Scholar
- Lockwood, F. (2007). Forword. In G. Conole & M. Oliver (Eds.), Contemporary perspectives in e-learning research: Themes, methods, and impacts on practice (pp. xvi–xvii). New York, NY: Routledge.Google Scholar
- Mihalca, L., & Miclea, M. (2007). Current trends in educational technology research. Cognition, Brain, Behavior, 11(1), 115–129.Google Scholar
- Mogil, S. J., Simmonds, K., & Simmonds, J. M. (2009). Pain research from 1975 to 2007: A categorical and bibliometric meta-tend analysis of every research paper published in the journal, Pain. Pain, 142, 48–58.Google Scholar
- Motlik, S. (2008). Mobile learning in developing nations. International Review of Research in Open and Distance Learning, 9(2). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/564/1071.
- Okubo, Y. (1997). Bibliometric indicators and analysis of research systems: Methods and examples, STI Working Papers 1997/1, OECD Science, Paris.Google Scholar
- Osareh, F. (1996). Bibliometrics, citation analysis and co-citation analysis: A review of literature I. Libri, (46), 149–158.Google Scholar
- Polsson, K. (2009). Chronology of handheld computers. Retrieved from http://www.islandnet.com/~kpolsson/handheld/.
- Rogers, E. M. (2003). Diffusion of innovations (5th ed.). New York, NY: Free Press.Google Scholar
- Sharma, S., & Kitchens, F. (2004). Web services architecture for m-learning. Electronic Journal of e-Learning (2), 203–216.Google Scholar
- Zhang, K., & Hung, J. L. (2009). E-learning in supplemental educational systems in Taiwan: Present status and future challenges. International Journal on E-Learning, 8(4), 49–64.Google Scholar