• DocumentCode
    2721855
  • Title

    User Centric Retrieval of Learning Objects in LMS

  • Author

    Sabitha, A.S. ; Mehrotra, Deepti

  • Author_Institution
    Dept. of CS & IT, Krishna Eng. Coll., Ghaziabad, India
  • fYear
    2012
  • fDate
    23-25 Nov. 2012
  • Firstpage
    14
  • Lastpage
    19
  • Abstract
    Research and Academic Institutions own and archive a great number of documents like lesson plan, study material and research related resources, which are needed to be stored and used over for a longer period of time by lecturers and researchers. In order to achieve this it is required to convert these educational resources into Learning Objects and store them in structured & meaningful way via a learning management system (LMS) thus enriching classical teaching. Also with enhancement of e-learning environment there is a great need of managing the LMS repositories by storing information resources as Learning object, a digital entity which can be used in electronic learning environment. These learning objects are stored in repositories and are managed by Learning Management Systems. It aids teaching and learning process and helps in communications between users. Many designs of LMS are non user-centric and has limited capabilities in delivering user preferred learning material. Searching through keywords or metadata of learning material will result in display of huge quantity of information. Thus there is an earnest need to identify the techniques that can provide more efficient mechanism for information retrieval. Recommendation techniques have shown to be successful in many domains (e.g. movies, books, music, etc.). Thus there is a need to deploy a recommending system in the E-Learning domain to extend the functionality of standard-based learning management systems with providing the user based retrieval. In this paper a model is being proposed that can enhance the search and delivery of a relevant learning object based on his/her preferences and further ranking & clustering of learning objects are done through K-Mean and Self Organising Maps for a personalised learning environment.
  • Keywords
    computer aided instruction; educational institutions; information resources; information retrieval; pattern clustering; recommender systems; self-organising feature maps; teaching; LMS; academic institutions; classical teaching; e-learning environment; educational resources; electronic learning environment; information resources; information retrieval; k-mean clustering; learning material; learning object clustering; learning object ranking; learning process; lesson plan; personalised learning environment; recommendation techniques; recommending system; research institutions; self organising maps; standard-based learning management systems; study material; user based retrieval; user centric retrieval; Data mining; Electronic learning; Least squares approximation; Measurement; Search problems; Standards; Clustering; K-mean; Learning Objects (LO); Quality Metrics; Ranking; Self Organizing Map;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Communication Technology (ICCCT), 2012 Third International Conference on
  • Conference_Location
    Allahabad
  • Print_ISBN
    978-1-4673-3149-4
  • Type

    conf

  • DOI
    10.1109/ICCCT.2012.13
  • Filename
    6394660