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http://dx.doi.org/10.9708/jksci.2011.16.7.127

A Method for Recommending Learning Contents Using Similarity and Difficulty  

Park, Jae -Wook (Dept. of Computer Science and Engineering, Dongguk University-Seoul)
Lee, Yong-Kyu (Dept. of Computer Science and Engineering, Dongguk University-Seoul)
Abstract
It is required that an e-learning system has a content recommendation component which helps a learner choose an item. In order to predict items concerning learner's interest, collaborative filtering and content-based filtering methods have been most widely used. The methods recommend items for a learner based on other learner's interests without considering the knowledge level of the learner. So, the effectiveness of the recommendation can be reduced when the number of overall users are relatively small. Also, it is not easy to recommend a newly added item. In order to address the problem, we propose a content recommendation method based on the similarity and the difficulty of an item. By using a recommendation function that reflects both characteristics of items, a higher-level leaner can choose more difficult but less similar items, while a lower-level learner can select less difficult but more similar items, Thus, a learner can be presented items according to his or her level of achievement, which is irrelevant to other learner's interest.
Keywords
Learning Content; Recommendation System; Similarity; Difficulty;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Robin Burke, "Hybrid Recommender System: Survey and Experiments," User Modeling and User Adapted Interaction, Vol. 12, No. 4, pp. 331-370, 2002.   DOI   ScienceOn
2 Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, John Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Proceeding of the ACM Conference on Computer-Supported Cooperative Work, pp. 175-186, 1994.
3 Khairil Imran Bin Ghauth, Nor Aniza Abdullah, "Building an E-Learning Recommender System using Vector Space Model and Good Learners Average Rating," Proceedings of the IEEE International Symposium on Advanced Learning Technologies, pp. 194-196, July 2009.
4 Hong-Ren Chen, "Learning Object Recommendation Services in Interactive E-Learning Systems," Proceedings of the 5th WSEAS International Conference on E-ACTIVITIES, pp. 410-414, Nov. 2006.
5 Leyla Zhuhadar, Olfa Nasraoui, Robert Wyatt, Elizabeth Romero, "Multi-model Ontology-based Hybrid Recommender System in E-Learning Domain," Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 91-95, Sep. 2009.
6 Ehsan Emadzadeh, Amade Nikfarjam, Kairil Imran Ghauth, Ng Kok Why, "Learning Materials Recommendation Using a Hybrid Recommender System with Automated Keyword Extraction," World Applied Science Journal, Vol. 9, No. 11, pp. 1260-1271, 2010.
7 Li-ping Shen, "Ontology-based Learning Content Rec ommendation," International Journal of Continuing Engineering Education and Life Long Learning, Vol. 15, No. 3, pp. 308-317, June 2005.   DOI   ScienceOn
8 Feng-Jung Liu, "Design of Self-directed E-Learning Material Recommendation System with On-line Evaluation," Proceedings of IEEE International Conference on Hybrid Information Technology, pp. 274-277, Aug. 2008.
9 Saman Shishechi, Seyed Yashar Banihasem, Nor Azan Mat Zin, "A Proposed Semantic Recomm endation System for E-Learning," Proceedings of IEEE International Symposium on Information technology, pp. 1-5, June 2010.
10 Yi-Hung Wu, Yong-Chuan Chen, Arbee L. P. Chen, "Enabling Personalized Recommendation on The Web Based on User Interests and Behaviors," Proceedings of IEEE International Workshop on Research Issues in Data Engineering, pp. 17-24. Apr. 2001.
11 Huiyi Tan, Junfei Guo, Yong Li, "E-Learning Recom mendation System," Proceedings of IEEE International Conference on Computer Science and Software Engineering, pp. 430-433. Apr. 2008.
12 Christopher D. Manning, Prabhakar Raghavan, Hinich Schutze, "An Introduction to Information Retrieval," Cambridge University Press, pp. 109-133, Apr. 2009
13 Mojdeh Talabeigi, Rana Forsati, Mohammad Reza Meybodi, "A Dynamic Web Recommender System Based on Cellular Learning Automata," Proceedings of the IEEE International Conference on Computer Engineering and Technology, pp. 755-761. Apr. 2010.
14 Jian Chen, Roman Y. Shtykh, Qun Jin, "A Web Recommender System Based on Dynamic Sampling of User Information Access Behaviors," Proceedings of the IEEE International Conference on Computer and Information Technology, pp. 172-177, Oct. 2009.
15 Yong Kim, Sung Been Moon, "A Study on Hybrid Re commendation System Based on Usage frequency for Multimedia Contents," Journal of The Korea Society for Information management, Vol. 23, No. 3, pp. 91-125, Sep. 2006.   DOI   ScienceOn
16 Jae Wook Park, Mee Hwa Park, Yong Kyu Lee, "An Ontology-Based Method for Calculating the Difficulty of a Learning Content," Journal of The Korea Society of Computer and Information, Vol. 16, No. 2, pp. 83-91, Feb. 2011.   DOI
17 Byeong Man Kim, Qing Li, Si Gwan Kim, En Ki Lim, Ju Yeon Kim, "A New Approach Combining Content-based Filtering and Collaborative Filtering for Recommender Systems," Journal of The Korean Institute of Information Scientists and Engineers, Vol. 31, No. 3, pp. 332-342, Mar. 2004.
18 JET, http://exam.ybmsisa.com
19 Inay Ha, Gyu Sik Song, Heung Nam Kim, Geun Sik Jo, "Collaborative Recommendation of Online Video Lectures in e-Learning System," Journal of The Korea Society of Computer and Information, Vol. 14, No. 9, pp. 85-94, Feb. 2009.
20 Byung Il Kwon, Nam Mi Moon, "Recommendation System for Supporting Self-directed Learning on E-learning Marketplace," Journal of The Korea Society of Computer and Information, Vol. 15, No. 2, pp. 135-146, Feb. 2010.   DOI