Abstract
Recently, the teaching-learning paradigm is focusing on learners. The individual’s right to select curriculum is gaining ground. As this selection right increases, there is increasing concern and more time needs to be invested in selecting the curriculum suitable for an individual’s situation and preferences. Therefore, an individualized service that can recommend a desirable curriculum to individuals is needed to minimize individuals’ efforts and help them make the right choices. This paper proposes a curriculum recommender system through which individual learners can get advice when they enroll. This research provides the foundation of learner-oriented education by providing a personalized curriculum from the beginning of a course of study.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baek, J., Kim, Y.: A Study on Personalization System for Improving Satisfaction in Web-based Education Environment. The Korean Association of Computer Edu. 6(4), 171–180 (2003)
Schubert, P., Koch, M.: The Power of Personalization: Customer Collaboration and Virtual Communities. In: Proceedings of the Conference on AMCIS 2002, pp. 1955–1965 (2002)
Lee, Y.J., Lee, S.H., Wang, C.J.: The Educational Contents Recommendation System Design based on Collaborative Filtering Method. The Korean Association of Computer Education 6(2), 147–156 (2003)
Kang, S.C., Han, S.R., Park, J.W.: Developing The Web Agent for Supporting and Facilitating Teaching and Learning on the Web. The Korean Association of Computer Education 6(1), 87–94 (2003)
Kim, H.I., Kim, J.T.: Data Blurring Method for Solving Sparseness Problem in Collaborative Filtering. The Korea Information Science Society 32(6), 542–553 (2005)
Lee, D.C.: Personalized Tour-Guide-Expert-System Using e-CRM Process. Journal of the Korea society of computer and information 7(1), 161–173 (2002)
Sung, K.S., Park, Y.C., Ahn, J.M., Oh, H.S.: Goods Recommendation System using a Customer’s Preference Features Information. The Korea Information Processing Society 11-D(5), 1205–1212 (2004)
Adomavicius, G., Tuzhilin, A.: 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), 734–749 (2005)
Kim, B.M., Li, Q., Kim, S.G., Lim, E.K., Kim, J.Y.: A New Approach Combining Content-based Filtering and Collaborative Filtering for Recommender Systems. The Korea Information Science Society 31(3), 332–342 (2004)
Ko, S.J.: Extracting Typical Group Preferences through User-Item Optimization and User Profiles in Collaborative Filtering System. The Korea Information Science Society 32(7), 581–591 (2005)
Breese, J., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cho, J., Kang, Ey. (2010). Personalized Curriculum Recommender System Based on Hybrid Filtering. In: Luo, X., Spaniol, M., Wang, L., Li, Q., Nejdl, W., Zhang, W. (eds) Advances in Web-Based Learning – ICWL 2010. ICWL 2010. Lecture Notes in Computer Science, vol 6483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17407-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-17407-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17406-3
Online ISBN: 978-3-642-17407-0
eBook Packages: Computer ScienceComputer Science (R0)