A M-learning content recommendation service by exploiting mobile social interactions

Han Chieh Chao, Chin Feng Lai, Shih Yeh Chen, Yueh Min Huang

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


With the rapid development of the Internet and the popularization of mobile devices, participating in a mobile community becomes a part of daily life. This study aims the influence impact of social interactions on mobile learning communities. With m-learning content recommendation services developed from mobile devices and mobile network techniques, learners can generate the learning stickiness by active participation and two-way interaction within a mobile learning community. Individual learning content is able to be recommended according to the behavioral characteristics of the response message of individual learners in the community, and other browsers not of this community are attracted to participate in the learning content with the proposed recommendation service. Finally, as the degree of devotion to the community and learning time increases, the learners' willingness to continue learning increases. The experiment results and analysis show that individualized learning content recommendation results in better learning effect. In addition, the proposed service proved that the experiment results can be easily extended to handle the recommended learning content for learners' time-varying interests.

Original languageEnglish
Article number6814314
Pages (from-to)221-230
Number of pages10
JournalIEEE Transactions on Learning Technologies
Issue number3
Publication statusPublished - 2014 Jul 1

All Science Journal Classification (ASJC) codes

  • Education
  • Engineering(all)
  • Computer Science Applications


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