Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

A Method for Recommending Learning Contents Using Similarity and Difficulty

유사도와 난이도를 이용한 학습 콘텐츠 추천 방법

  • Park, Jae -Wook (Dept. of Computer Science and Engineering, Dongguk University-Seoul) ;
  • Lee, Yong-Kyu (Dept. of Computer Science and Engineering, Dongguk University-Seoul)
  • 박재욱 (동국대학교 컴퓨터공학과-서울) ;
  • 이용규 (동국대학교 컴퓨터공학과-서울)
  • Received : 2011.06.01
  • Accepted : 2011.07.12
  • Published : 2011.07.31
Download PDF
⟨ Previous Next ⟩

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.

이러닝 시스템에서 학습자에게 적합한 콘텐츠 선택을 돕기 위한 콘텐츠 추천 시스템은 필수적이다. 학습자의 선호도를 통한 콘텐츠 추천은 협업 필터링 추천 방법과 내용 기반 추천 방법이 가장 많이 사용되고 있다. 그러나 기존추천 방법들은 학습자의 학습수준을 고려하지 않고 다른 사용자의 선호도를 기반으로 학습 콘텐츠를 추천한다. 따라서 상대적으로 콘텐츠를 학습한 학습자가 적은 경우 추천의 효율성이 떨어지고, 새로운 아이템이 추가될 경우 추천이 쉽지 않은 단점이 있다. 이 문제를 해결하기 위해 우리는 학습 콘텐츠의 유사도와 난이도에 기반한 콘텐츠 추천 방법을 제안한다. 학습 콘텐츠의 두 특성을 반영한 추천함수에 의해 선행학습 성취도가 낮은 학습자에게는 난이도가 낮고 유사도가 높은 콘텐츠를 추천하고, 성취도가 높은 학습자에게는 난이도가 높고 유사도가 낮은 콘텐츠를 추천한다. 이와 같이 다른 학습자의 선호도와는 무관하게 학습자의 성취도에 따라 가장 적합한 콘텐츠를 추천할 수 있다.

Keywords

References

  1. 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.
  2. 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. https://doi.org/10.3743/KOSIM.2006.23.3.091
  3. 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. https://doi.org/10.9708/jksci.2011.16.2.083
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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. https://doi.org/10.1504/IJCEELL.2005.007719
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Robin Burke, "Hybrid Recommender System: Survey and Experiments," User Modeling and User Adapted Interaction, Vol. 12, No. 4, pp. 331-370, 2002. https://doi.org/10.1023/A:1021240730564
  16. 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.
  17. Christopher D. Manning, Prabhakar Raghavan, Hinich Schutze, "An Introduction to Information Retrieval," Cambridge University Press, pp. 109-133, Apr. 2009
  18. 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.
  19. 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. https://doi.org/10.9708/jksci.2010.15.2.135
  20. JET, http://exam.ybmsisa.com