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
Link To Document :
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