A disciplinary analysis of Internet Science

Internet Science is an interdisciplinary field. Motivated by the unforeseen scale and impact of the Internet, it addresses Internet-related research questions in a holistic manner, incorporating epistemologies from a broad set of disciplines. Nonetheless, there is little empirical evidence of the levels of disciplinary representation within this field.This paper describes an analysis of the presence of different disciplines in Internet Science based on techniques from Natural Language Processing and network analysis. Key terms from Internet Science are identified, as are nine application contexts. The results are compared with a disciplinary analysis of Web Science, showing a surprisingly low amount of overlap between these two related fields. A practical use of the results within Internet Science is described. Finally, next steps are presented that will consolidate the analysis regarding representation of less technologically-oriented disciplines within Internet Science

Towards Developing an Online Social Media-based Mobile Learning System

The advancement of Information and Communication Technology (ICT) and the Internet revolution gave rise to the several learning technologies on the web and mobile platform. During the last decade, the social media network became available for users to socialise and collaborate among peer group. Hence, The integration of e-learning and social media using mobile device as access point is to allow for learning and collaboration anytime, anywhere. This study seeks to provide learning on the social network platform for users to view the application on a mobile device and also foster collaboration among scholars. The system was developed using an open source Content Management System (CMS) Wordpress and Buddypress running on a WAMP or XAMPP server. MySQL was used as database. The usability of the System on the different mobile devices used was evaluated by identifying the usability attributes; designing a questionnaire based on those attributes and then analyzing the results with Statistical Package for Social Science (SPSS). The results showed that the learning system had a good usability score on mobile device