Research Article
Mobile Learning Content Authoring Tools (MLCATs): A Systematic Review
@INPROCEEDINGS{10.1007/978-3-642-12701-4_3, author={Raymond Mugwanya and Gary Marsden}, title={Mobile Learning Content Authoring Tools (MLCATs): A Systematic Review}, proceedings={E-Infrastructures and E-Services on Developing Countries. First International ICST Conference, AFRICOM 2009, Maputo, Mozambique, December 3-4, 2009. Proceedings}, proceedings_a={AFRICOMM}, year={2012}, month={5}, keywords={Mobile Education Content Authoring Tools Systematic Review}, doi={10.1007/978-3-642-12701-4_3} }- Raymond Mugwanya
Gary Marsden
Year: 2012
Mobile Learning Content Authoring Tools (MLCATs): A Systematic Review
AFRICOMM
Springer
DOI: 10.1007/978-3-642-12701-4_3
Abstract
Mobile learning is currently receiving a lot of attention in the education arena, particularly within electronic learning. This is attributed to the increasing mobile penetration rates and the subsequent increases in university student enrolments. Mobile Learning environments are supported by a number of crucial services such as content creation which require an authoring tool. The last decade or so has witnessed increased attention on tools for authoring mobile learning content for education. This can be seen from the vast number of conference and journal publications devoted to the topic. Therefore, the goal of this paper is to review works that were published, suggest a new classification framework and explore each of the classification features. This paper is based on a systematic review of mobile learning content authoring tools (MLCATs) from 2000 to 2009. The framework is developed based on three broad dimensions i.e. Technology, Pedagogy and Usability and a number of features such as system type, development context, Tools and Technologies used, tool availability, ICTD relation, Multimedia support, tool purpose, support for standards, learning style support, intuitive Graphical User Interface and accessibility. This paper provides a means for researchers to extract assertions and several important lessons for the choice and implementation of MLCATs.
