Adaptive object re-ranking mechanism for ubiquitous learning environment

Neil Y. Yen, Timothy K. Shih, Qun Jin, Jason C. Hung, Qingguo Zhou, Louis R. Chao

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Ubiquitous Learning (U-Learning), as an emerging learning paradigm, makes it possible for learners to carry out the learning activities at any places and at anytime. With the advantages of the devices, learners can obtain a variety of supplementary materials from the Internet. In the scope of distance learning, LOR (Learning Object Repository) stands for managing and sharing of learning related materials (known as learning objects). However, some challenges may raise while performing these activities. For instance, a huge amount of learning objects may appear while learners utilize the search service provided by LOR. Learners have to spend time on collecting relevant resources for specific purposes. This situation may discourage the reusability of learning objects especially in a ubiquitous environment. In this paper, based on systematic re-examination of reuse scenarios, an adaptive mechanism, as a resource discovery and search middleware, was proposed to assist learners in obtaining possible objects under ubiquitous environment. Achievement of the proposed mechanism can produce search results adaptive to specific situations in order of similarity degree based on the mixed information. We try to filter out some irrelevant results by using the past usage history, current geographical information and input query, so as to enhance the efficiency of learning objects retrieval in a ubiquitous environment. As a pilot test, Apple iPhone was utilized to be the major client testbed.

Original languageEnglish
Pages (from-to)129-138
Number of pages10
JournalJournal of Multimedia
Issue number2
StatePublished - 2011


  • Information filtering
  • Object retrieval
  • Re-ranking algorithm
  • Ubiquitous learning


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