A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

  • Jun-Ming Su
  • Shian-Shyong Tseng
  • Huan-Yu Lin
  • Chun-Han Chen
Original Paper


With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners’ preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners’ requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.


Personalized learning content Content adaptation Mobile learning environment Data mining Learning object repository 


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jun-Ming Su
    • 1
  • Shian-Shyong Tseng
    • 2
  • Huan-Yu Lin
    • 3
  • Chun-Han Chen
    • 3
  1. 1.Department of Information and Learning TechnologyNational University of TainanTainanTaiwan
  2. 2.Department of Applied Informatics and MultimediaAsia UniversityTaichungTaiwan
  3. 3.Department of Computer ScienceNational Chiao Tung UniversityHsinchuTaiwan

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