Abstract
Mobile learning has become increasingly popular in the past decade due to the unprecedented technological affordances achieved through the advancement of mobile computing, making 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.
Highlights
In the past decade, we have seen increasing sales of mobile devices (Gartner, 2011), rapid advancement in mobile technologies and features (Wu et al, 2012), and growing availability of various mobile applications (Hsu, Rice, & Dawley, 2012). Gartner (2014) expected the sales of mobile phones to reach 1.9 billion units in 2014, a 3.1% increase from 2013. Gartner (2014) estimated that smartphone sales would represent 88% of global mobile phone sales by 2018 — a 66% increase from 2014
These experts indicated that while there are increasing models and frameworks in mobile learning, more research effort is needed because conceptual and theoretical guidance can help support design and research in mobile learning. The purpose of this current paper is to categorize and synthesize existing mobile learning models and frameworks. This topic is important because a systematic analysis can help generate useful suggestions and insightful implications for educators, researchers, instructional designers, and developers who are interested in providing meaningful mobile learning experiences and environments based on sound theoretical foundations
We conducted a search of article titles in databases including WorldCat, ERIC (Education Resources Information Center), and Google Scholar, using keyword combinations including: 1) ti: mobile learning and ti: model; 2) ti: mlearning ti: model; 3) ti: mlearning ti:model; 4) ti: mobile learning ti: framework; 5) ti: mlearning ti: framework; 6) ti: mlearning ti: framework
Summary
We have seen increasing sales of mobile devices (Gartner, 2011), rapid advancement in mobile technologies and features (Wu et al, 2012), and growing availability of various mobile applications (Hsu, Rice, & Dawley, 2012). Gartner (2014) expected the sales of mobile phones to reach 1.9 billion units in 2014, a 3.1% increase from 2013. Gartner (2014) estimated that smartphone sales would represent 88% of global mobile phone sales by 2018 — a 66% increase from 2014. Hsu, Ching, and Snelson (2014) reported ten areas of research in mobile learning that should receive attention and have high priority in the five years, based on the consensus of a panel of international experts of mobile learning These experts indicated that while there are increasing models and frameworks in mobile learning, more research effort is needed because conceptual and theoretical guidance can help support design and research in mobile learning. The purpose of this current paper is to categorize and synthesize existing mobile learning models and frameworks This topic is important because a systematic analysis can help generate useful suggestions and insightful implications for educators, researchers, instructional designers, and developers who are interested in providing meaningful mobile learning experiences and environments based on sound theoretical foundations. It can help identify gaps in the existing literature and provide future research directions in mobile learning
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