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Journal of Computing in Higher Education
Erschienen in: Journal of Computing in Higher Education 1/2012

01.04.2012

Examining mobile learning trends 2003–2008: a categorical meta-trend analysis using text mining techniques

verfasst von: Jui-Long Hung, Ke Zhang

Erschienen in: Journal of Computing in Higher Education | Ausgabe 1/2012

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Abstract

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.
Nächster Artikel Key factors to instructors’ satisfaction of learning management systems in blended learning

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Metadaten
Titel
Examining mobile learning trends 2003–2008: a categorical meta-trend analysis using text mining techniques
verfasst von
Jui-Long Hung
Ke Zhang
Publikationsdatum
01.04.2012
Verlag
Springer US
Erschienen in
Journal of Computing in Higher Education / Ausgabe 1/2012
Print ISSN: 1042-1726
Elektronische ISSN: 1867-1233
DOI
https://doi.org/10.1007/s12528-011-9044-9

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