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

Hung, Jui Long; Zhang, Ke

doi:10.1007/s12528-011-9044-9

Cited by 178 publications

(122 citation statements)

References 13 publications

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“…Employing virtual learning environments (VLE), trainees are empowered to follow their own training pace, learning topics and assessment tests that fit their needs (Emmanouilidis 2009;Papathanasiou et al 2013). Training can become ubiquitous through mobile learning (Hung and Zhang 2011;Mierlus-Mazilu 2010;Papathanasiou et al 2014). The spread of use of social networking tools has also brought attention to the contribution of social and groupware learning (Casagranda et al 2011;Westerhout et al 2011).…”

Section: Human Learning Solutions For Continuous Educationmentioning

confidence: 99%

Current trends on ICT technologies for enterprise information systems

Kadiri

Grabot

Thoben

et al. 2016

Computers in Industry

125

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Section: Human Learning Solutions For Continuous Educationmentioning

confidence: 99%

Current trends on ICT technologies for enterprise information systems

Kadiri

Grabot

Thoben

et al. 2016

Computers in Industry

125

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“…Among the increasing volume of literature, there are great efforts in developing models and frameworks for designing mobile learning experiences and environments (e.g., Koole, 2009;Vavoula & Sharples, 2009). The developments mentioned above in turn lead to an emerging body of literature starting to identify trends in mobile learning research using methods such as content analysis (e.g., Hwang & Tsai, 2011;Wu et al, 2012) and text mining (e.g., Hung & Zhang, 2012). These reviews of literature examined published articles in journals and proceedings to identify research themes and increasing/decreasing trends based on researchers' analysis and interpretation.…”

Section: Introductionmentioning

confidence: 99%

A review of models and frameworks for designing mobile learning experiences and environments

Hsu

Ching

2015

CJLT / RCAT

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Mobile learning has become increasingly popular in the past decade due to the unprecedented technological affordances achieved through the advancement of mobile computing, which makes ubiquitous and situated learning possible. At the same time, there have been research and implementation projects whose efforts centered on developing mobile learning experiences for various learners' profiles, accompanied by the development of models and frameworks for designing mobile learning experiences. This paper focuses on categorizing and synthesizing models and frameworks targeted specifically on mobile learning. A total of 17 papers were reviewed, and the models or frameworks were divided into five categories and discussed: 1) pedagogies and learning environment design; 2) platform/system design; 3) technology acceptance; 4) evaluation; and 5) psychological construct. This paper provides a review and synthesis of the models/frameworks. The categorization and analysis can also help inform evaluation, design, and development of curriculum and environments for meaningful mobile learning experiences for learners of various demographics.

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“…Text mining makes it easy to obtain a meaningful and structured data from the irregular data patterns [24], [25], and [26]. It is really not an easy task for the computers to understand the unstructured data [27], [28] and make it structured.…”

Section: Text Miningmentioning

confidence: 99%