Abstract
With the fast development of online and mobile technologies, individualized or personalized learning is becoming increasingly important. Online courses especially Massive Open Online Courses (MOOCs) often have students from many countries, with different prior knowledge, expectations, and skills. They in particular could benefit from learning materials or learning systems that are customized to meet their needs. On this note, this paper suggests a personalized recommendation system for learners in online courses. The system recommends learning resources such as relevant courses to learners enrolled in formal online courses, by using a combination of association rules, content filtering, and collaborative filtering. Pilot testing of this system in the Shanghai Lifelong Learning Network, a platform for free and open education, indicates that this recommendation system can improve the utilization rate of educational resources and also promote the learning autonomy and efficiency of students.
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References
Apache. (2011/2012) Hadoop overview [EB/OL]. http://hadoop.apache.org/common/docs/rO.20.203.0/.2011-04-05/2012-03-30
Bong RP, Iacovou N, Suchak M (1994) Group lens: an open architecture for collaborative filtering of Netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work, Chapel Hill, NC. ACM, New York, p 175–186
Breese J, Hecherman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence, Medison, US. Morgan Kaufmann Publishers Inc., San Francisco, p 43–52
Chen C-M (2008) Intelligent web-based learning system with personalized learning path guidance. Comput Educ 51:787–814
Deng A, Zhu Y, Shi B (2003) A collaborative filtering recommendation algorithm based on item rating prediction. J Softw 14:1621–1627
Gaeta M, Mangione GR, Orciuoli F, Salerno S. (2013) Ambient e-Learning: a metacognitive approach. J Ambient Intell Humaniz Comput 4(1):141–54
Goldberg D, Nichols D, Oki BM, Terry D (1992) Using collaborative filtering to weave an information tapestry. Commun ACM 35(12):61–70
Good N, Schafer JB, Konstan, JA, Borchers A, Sarwar B., Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: AAAI/IAAI, p 439–446
Herlocker JL, Konstan, JA, Riedl, J (2000) Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on computer supported cooperative work, p 241–250
Hu S, Li J, Li J (2007) Video retrieval based on latent semantic analysis. Comput Eng 33:216–217
Huang RH, Yang J, Hu Y (2012) From digital to smart: the evolution and trends of learning environment. Open Educ Res 1:75–84
Khribi MK, Jemni M, Nasraoui O (2009) Automatic recommendations for E-Learning personalization based on web usage mining techniques and information retrieval. Educ Technol Soc 12(4):30–42
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80
Liu ZY, Liu L, Liu PP (2009a) Learning resource personalizing recommendation based on semantic. J Jilin Univ (Eng Technol Ed) 39:390–395
Liu JG, Zhou T, Wang BH (2009b) Research progress of personalized recommendation system. Prog Nat Sci 19:1–15
Sun X, Wang G, Qiu F (2012) The research of personalized recommendation of online learning resources based on collaborative filtering recommendation technology. Distance Educ China 8:78–82
Wang, MJ (2012) The architecture of a cloud-based Intelligent Learning System (C-iLearning). EBTIC’s international iCampus initiative internal report, p 1–12
Wang Z, Yang F (2006) Resource recommendation system based on similar learners exploitation. J Zhejiang Univ (Eng Sci) 40(10):1688–1691
Wang MJ, Aziz B, Hauze S, Olmstead W, Zaineb B, Ng J (2014) An exploration of intelligent learning (iLearning) systems. In: Proceesing of IEEE international conference on teaching, assessment, and learning for engineering (TALE), Wellington, New Zealand
Wengang C, De X (2002) Content-based video retrieval using audio and visual clues. In: IEEE proceeding of 2002 Region 10 conference on computers, communications, control and power engineering, Beijing, China, p 586–589
White T (2009) Hadoop: the definitive guide. O’Reilly Media, Inc., Sebastopol
Xiao J, Wang MJ, Wang LM, Wang X (2013) Design and implementation of C-iLearning: a cloud-based intelligent learning system. Int J Distance Educ Technol 11(3):79–97
Yang, LN (2014) Research on promoting the effect of personalized recommendation on digital learning resources. Mod Educ Technol 24(6):84–91
Yang, L, Yan Z (2011) Personalized recommendation for learning resources based-on case reasoning agents. In: 2011 international conference on electrical and control engineering (ICECE), p 6689–6692
Yu SQ, Wang MJ, Che HY (2005) An exposition of the crucial issues in China’s educational informatization. Education Technol Res Dev 53(4):88–101
Yu Z, Nakamura Y, Jang S, Kajita S, Mase K (2007) Ontology-based semantic recommendation for context-aware e-learning. In: International conference on ubiquitous intelligence and computing, Springer, Berlin Heidelberg, p 898–907
Zeng C, Xing CX, Zhou LZ (2003) A personalized search algorithm by using content-based filtering. J Softw 14(5):999–1004
Zhang ZG, Liu HL (2007) Research on video retrieval using high-level semantic. Comput Eng Appl 43:168–170
Zhao YX, Liang CY (2006) The application of e-commerce recommendations based on association rules. Value Eng 5:88–91
Acknowledgements
This research was supported by the Engineering Technology Research Centre of Shanghai Science and Technology Research Program (13DZ2252200). It is also supported by the Shanghai Education Scientific Research project “The Study of Online Learning mode for Shanghai lifelong education” (A1403) and the Oriental Scholar program (TPKY052WMJ) of the Shanghai Municipal Educational Commission.
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Xiao, J., Wang, M., Jiang, B. et al. A personalized recommendation system with combinational algorithm for online learning. J Ambient Intell Human Comput 9, 667–677 (2018). https://doi.org/10.1007/s12652-017-0466-8
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DOI: https://doi.org/10.1007/s12652-017-0466-8