Distinctive mobile learning: where it is different and how it can make a difference

ABSTRACT

In recent years, the emerging trend of mobile learning (hereinafter “m-learning”) has obtained increasing attention from educators and higher popularity among students worldwide. Partly due to its short history, there seems to be a lack of comprehensive and in-depth understanding on the distinctiveness of m-learning. It is maintained in this paper that m-learning should be described and researched in three intertwined aspects: the mobility of devices, learners and materials. The multi-faceted mobility may help achieve highly portable and genuinely personal learning, which distinguishes m-learning from other forms of technology-enhanced education. This paper also reports an empirical study on m-learning for academic English. With a total of 60 participants (and six in the pilot study) over three progressive phases, the data obtained from mixed methods of automatic logging, questionnaire and interview were used to assess multidimensional learner engagement in the self-directed process of m-learning. The triangulated data can reflect and highlight some known features of m-learning, and they may also imply learners’ expectations towards m-learning. Based on the literature review and data analysis, this paper aims to contribute to the (re)conceptualisation of “m-learning” and also provide some technical and pedagogical implications on future and further development of m-learning.

KEYWORDS:

• Mobile learning
• mobile-assisted language learning (MALL)
• technology-enhanced learning
• learner engagement

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Notes

1 In fact, there are two versions of the app, carrying out slightly different concordancing based on language patterns and keywords, respectively. Hence, P users refer to those who used the pattern-based concordancer, while K stands for the keyword-based version. Their m-learning behavior was not differentiated according to different groups in the later analysis.

Additional information

Notes on contributors

Zhi Quan

Dr. Zhi Quan (preferably Bill) is an assistant professor at Southwestern University of Finance and Economics in Chengdu, China. He received his PhD from Auckland University of Technology (AUT) in New Zealand. His research interests include corpus linguistics, mobile learning, English for academic purposes (EAP) and translation studies. He has been devoted to promoting and enhancing self-directed and autonomous learning via emerging technologies.

Lynn Grant

Dr. Lynn Grant is a senior lecturer (above the bar) at Auckland University of Technology. She received her PhD in applied linguistics from Victoria University of Wellington. She has decades of academic experience and expertise in corpus linguistics, discourse analysis, pedagogy and education, and so on.

Darryl Hocking

Dr. Darryl Hocking (PhD Linguistics, Macquarie University) is a discourse analyst with a background in art and design. His research interests involve discourses of creativity and communicative interaction in creative contexts. He also researches theoretical writing in art and design, including exegetical writing for creative practice in post-graduate settings.

Andy Connor

Dr. Andy Conner is currently employed as an Associate Professor and have a range of teaching, research and leadership duties in the area of Creative Technologies at Colab, AUT. He is actively engaged in curriculum development at both course and programme level that involves integrating disciplinary knowledge and practices into a trandisciplinary student experience.