The week's pick
Random Articles
Reseach Article
Recommendation System for Adaptive E-learning using Semantic Net
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 63 - Number 7 |
| Year of Publication: 2013 |
| Authors: V. Senthil Kumaran, A. Sankar |
10.5120/10478-5210
|
V. Senthil Kumaran, A. Sankar . Recommendation System for Adaptive E-learning using Semantic Net. International Journal of Computer Applications. 63, 7 ( February 2013), 19-24. DOI=10.5120/10478-5210
Abstract
In the e-learning system an abundant amount of information is created and delivered to the learners over electronic media. Learners are often getting confusion by the flow of information and have difficulty in selecting the topic to learn that satisfies their needs and interests. There are several researches have been performed to provide personalized learning paths for individual learners. But many of them collect the learners' interest, habits and behavior from their profile and based on that they recommend learning path. It is the fact that the learners' interest, learning attitude and need will vary from time to time and course to course. In this paper a recommendation system is proposed using semantic net that helps the learners by offering a more intelligent approach to navigating and searching course content. In this the learner will get more personalized and contextual recommendation. The results show that semantic net based methods enable interoperability of heterogeneous course content representation and result in accurate recommendations. The validity of the proposed model is shown using sample learners and performance measures for the recommendation effects are given for evaluating the proposed system.
References
- Tuukka Ruotsalo, Lora Aroyo, and Guus Schreiber, (2009) "Knowledge-Based Linguistic Annotation of Digital Cultural Heritage Collections" IEEE Intelligent Systems, 24(2), pp. 64–75.
- Tuukka Ruotsalo, Eetu Mäkelä, Tomi Kauppinen, Eero Hyvönen, Krister Haav, Ville Rantala,Matias Frosterus, Nima Dokoohaki, and MihhailMatskin, (2009) "Smartmuseum: Personalized Context-aware Access to Digital Cultural Heritage", In Proceedings of the International Conference on Digital Libraries and the Semantic Web 2009 (ICSD 2009), September, pp. 178–192. Università diTrento, Trento, Italy.
- Tomoharu lwata, Kazumi, Takeshi Yamada, (2007) "Modelling User Behavior in Recommender Systems based on Maximum Entropy", Proceedings of the 16th international conference on World Wide Web, pp. 1281 – 1282
- Robin Burke, (2000) "Knowledge-based Recommender Systems", In: A. Kent, Encyclopedia of Library and Information Systems, 69(32).
- Greg Linden, Brent Smith, and Jeremy York, (2003) "Amazon. com recommendations item-to-item collaborative filtering", IEEE Internet Computing, 7(1), pp. 76–80.
- Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl, (1997) "GroupLens: applying collaborative filtering to Usenet news", Communications of the ACM , 40(3), pp. 77–87.
- Bradley N. Miller, Istvan Albert, Shyong K. Lam, Joseph A. Konstan, and John Riedl, (2003) "MovieLens unplugged: experiences with an occasionally connected recommender system", In: IUI '03: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 263–266. ACM, New York, NY, USA.
- Gediminas Adomavicius and Alexander Tuzhilin, (2005) "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge and Data Engineering, 17(6), pp. 734–749.
- Qi Gao, junwei YAN, MMin LIU, (2008) "A Semantic approach to Recommendation System based on user ontology and spreading activation model", In the proceedings of the International Conference on Network and Parallel Computing (IEEE), pp. 488 – 492.
- T. P. Liang, (2007) "A semantic expansion approach to personalized knowledge recommendation", Decision Support Systems, 45(3), 2007
- Raymond J. Mooney and Loriene Roy, (2000) "Content-based book recommending using learning for text categorization", In: DL '00: Proceedings of the fifth ACM conference on Digital libraries, pp 195–204. ACM, New York, NY, USA.
- David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry, (1992) "Using collaborative filtering to weave an information tapestry", Communications of the ACM, 35(12), pp. 61–70.
- Gediminas Adomavicius and Alexander Tuzhilin, (2005) "Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions", IEEE Transactions on Knowledge and Data Engineering, 17(6), pp. 734–749.
- Javed Khan and Manas Hardas, (2007) "Hierarchical Course knowledge representation using ontologies", In: IICAI'07: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, pp. 1684 – 1698.
- Adriana da Silva Jacinto, (2008) "An ontology-based architecture for intelligent tutoring system", Interdisciplinary Studies in Computer Science. 19 (1) pp. 25 – 35.
- Tiffany Ya TANG, Gordon Mccalla (2005) "Smart Recommendation for an Evolving E-learning System: Architecture and Experiment", International Journal on e-Learning, 4(1),pp. 105-129 .
- H. Srimathi, (2010) "Knowledge Representation of LMS using Ontology", International Journal of Computer Applications, 6(3), pp. 35 – 38.
- Sunita B. Aher, Lobo L. M. R. J. , (2012) "Mining Association Rule in Classified Data for Course Recommender System in E-learning", International Journal of Computer Applications, 39(7), pp. 1 – 7.
- Sunita B. Aher, Lobo L. M. R. J. , (2011) "A Framework for Recommendation of courses in E-learning System", International Journal of Computer Applications, 35(4), pp. 21 – 28.
- Y. Koren, (2008) "Factorization meets the neighborhood: a multifaceted collaborative filtering model", In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426–434, New York, NY, USA, 2008. ACM.
Index Terms
Keywords