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Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems

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Abstract

In this paper, we present a recommendation approach for learning objects (LOs) in ubiquitous e-learning systems. Many of these systems are social learning networks, and learners can interact with other users through forums or chats. In these systems, learners usually perform a set of choices or make decisions (“what to learn”, “how to learn”, “with whom to learn”, among others) during learning, depending on the system. The developed approach uses the result of these choices as a source of information. It is an extension of the User-based Nearest Neighbor recommendation approach, which has roots in the Nearest Neighbor search problem. Moreover, this approach uses social signals, interests, and preferences of learner users. With the fusion of these elements, we sought to find the most similar users to the active user, and then, to generate more accurate recommendations. We present an experimental evaluation of this approach showing that the usage prediction accuracy varies according to the combination of user choices and presents statistically significant higher prediction than baseline approaches. Despite being focused on ubiquitous e-learning systems, we briefly discuss how to use it in other domains where we observe that users can make decisions when interacting with other systems.

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Notes

  1. http://www.merlot.org/

  2. http://moodle.org/

  3. http://ead.joinville.udesc.br/adaptweb

  4. http://www.flickr.com/

  5. http://www.foursquare.com/

  6. http://www.edx.org

  7. https://sghud-srv01.nuvem.ufrgs.br/adaptweb_novo

  8. http://mahout.apache.org

  9. http://www.triphobo.com

  10. http://roadtrippers.com

References

  1. Adomavicius G, Tuzhilin A (2015) Context-aware recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Boston

    Google Scholar 

  2. Alexander A, Kernohan W, Mccullagh P (2004) Self directed and lifelong learning. Global Health Informatics Education - Stud Health Technol Inform 109:152–166

    Google Scholar 

  3. Amatriain X, Pujol JM (2015) Data mining methods for recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Boston

    Google Scholar 

  4. Bell RM, Koren Y (2007) Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Proceedings of the 2007 Seventh IEEE International Conference on Data Mining (ICDM '07). IEEE Computer Society, Washington, DC, pp 43–52. https://doi.org/10.1109/ICDM.2007.90

    Google Scholar 

  5. Bhargava P, Phan T, Zhou J, Lee J (2015) Who, what, when, and where: multi-dimensional collaborative recommendations using tensor factorization on sparse user-generated data. In Proceedings of the 24th International Conference on World Wide Web (WWW'15). International World Wide Web Conferences Steering Committee, Switzerland, p 130–140

  6. Bill & Melinda Gates Foundation, the Michael & Susan Dell Foundation, Silicon Schools, EDUCAUSE, iNACOL, and others. Personalized learning: a working definition. 2014. Education Week. V.34, Issue 09, Page s4. Retrieved from https://www.documentcloud.org/documents/1311874-personalized-learning-working-definition-fall2014.html Accessed 3 May 2018

  7. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence (UAI’98)

  8. Chen L, Pu P (2010) Eye-tracking study of user behavior in recommender interfaces. Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. UMAP 2010: User Modeling, Adaptation, and Personalization,, p 375–380

  9. Dey A, Abowd G, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications, HumAN-Computer Interaction, vol 16, pp 97–166, Dec.

  10. Dias SA, Wives KL (2018) Assessment of the most relevant learning object metadata - relieving the learner-user from information overload. Proceedings of the 10th International Conference on Computer Supported Education (CSEDU 2018), vol 1. p 175–182

  11. Dias SA, Wives KL (2018) Definition of learner choices from learner-driven learning for ubiquitous e-learning systems and its application in the AdaptWeb platform. Proceedings of the 29th Brazilian Symposium on Informatics in Education (SBIE'2018), Fortaleza, Brazil. Accepted

  12. Dourish P (2004) What we talk about when we talk about context. Pers Ubiquit Comput 8:19–30

    Article  Google Scholar 

  13. Drachsler H, Verbert K, Santos O, Manouselis N (2015) Panorama of recommender systems to support learning. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Boston

    Google Scholar 

  14. Draft Standard for Learning Object Metadata (2002) Retrieved from http://grouper.ieee.org/groups/ltsc/wg12/files/LOM_1484_12_1_v1_Final_Draft.pdf Accessed 3 May 2018

  15. Durao F, Dolog P (2009) Social and behavioral aspects of a tag-based recommender system. Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, ISDA’09

  16. Fazeli S, Loni B, Drachsler H, Sloep P (2014) Which recommender system can best fit social learning platforms? Proceedings of the 9th European Conference on Open Learning and Teaching in Educational Communities - Volume 8719 (EC-TEL 2014), Christoph Rensing, Sara Freitas, Tobias Ley, and Pedro Muñoz-Merino (Eds.), vol 8719. Springer-Verlag New York, Inc., New York, pp 84–97. https://doi.org/10.1007/978-3-319-11200-8_7

  17. Fazeli S, Rajabi E, Lezcano L, Drachsler H, Sloep P (2016)Supporting users of open online courses with recommendations: an algorithmic study. Proceedings of the IEEE 16th International Conference on Advanced Learning Technologies (ICALT), Austin, TX, 2016, pp 423–427. https://doi.org/10.1109/ICALT.2016.119

  18. Garcia V, Debreuve E, Barlaud M (2008) Fast k nearest neighbor search using GPU. Computer Vision and Pattern Recognition Workshops. CVPRW'08. IEEE Computer Society Conference on. IEEE, 2008

  19. Ginsberg MB (2015) Excited to learn: motivation and culturally responsive teaching. Corwin Press, Thousand Oaks

    Google Scholar 

  20. Jameson A, Willemsen MC, Felfernig A, Gemmis M, Lops P, Semeraro G, Chen L (2015) Human decision making and recommender systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Boston

    Google Scholar 

  21. Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, New York

    Book  Google Scholar 

  22. Jannach D, Resnick P, Tuzhilin A, Zanker M (2016) Recommender systems — beyond matrix completion. Commun ACM 59(11 (October 2016)):94–102. https://doi.org/10.1145/2891406

    Article  Google Scholar 

  23. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8 (August 2009)):30–37. https://doi.org/10.1109/MC.2009.263

  24. Kusner M, Tyree S, Weinberger K, Agrawal K (2014) Stochastic neighbor compression. In International Conference on Machine Learning pp 622–630

  25. Li L, Zheng Y, Ogata H, Yano Y (2014) A framework of ubiquitous learning environment, Proceedings of the Fourth Int Conf Computer and Information Technology (CIT ‘04), p 345–350

  26. Liu H, Hu Z, Mian A, Tian H, Zhu X (2014) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56(January 2014):156–166

    Article  Google Scholar 

  27. Manouselis N, Vuorikari R, Assche FV (2010) Collaborative recommendation of e-learning resources: an experimental investigation. J Comput Assist Learn 26(4):227–242

    Article  Google Scholar 

  28. Manouselis N, Drachsler H, Verbert K, Duval E (2012) Recommender systems for learning. Springer Publishing Company, Incorporated, New York

    Google Scholar 

  29. Miliband D (2006) Choice and voice in personalised learning. IN OECD, Personalizing education

  30. Palazzo JMO, Brunetto M, Proença JM, Pimenta MS, Pinto CHF, Lima JV, Freitas V, Marçal VP, Gasparine I, Amaral M (2003) AdaptWeb: um Ambiente para Ensino-aprendizagem Adaptativo na Web. Educar em revista, Curitiba, número especial, p 175–197

  31. Powell A, Kennedy K, Patrick S (2013) Mean what you say: defining and integrating personalized, blended and competency education Retrieved from https://www.inacol.org/resource/mean-what-you-say-defining-and-integrating-personalized-blended-and-competency-education Accessed 3 May 2018

  32. Rotărescu E (2011) Applying PERT and critical path method in human resource training. Review of General Management, Braşov, acceptat pentru publicare în, 7

  33. Saha S, Ghrera SP (2015) Nearest Neighbor search in Complex Network for Community Detection. arXiv preprint arXiv:1511.07210. Retrieved from https://arxiv.org/pdf/1511.07210.pdf Accessed 3 May 2018

  34. Shani G, Gunawardana A (2015) Evaluating recommendation systems. In: Ricci F, Rokach L, Shapira B (eds) Recommender systems handbook. Springer, Boston

    Google Scholar 

  35. Symeonidis P, Ntempos D, Manolopoulos Y (2014) Recommender systems for location-based social networks. Springer Briefs in Electrical and Computer Engineering, Cham

    Book  Google Scholar 

  36. Takács G, Pilászy I, Németh B, Tikk D (2009) Scalable collaborative filtering approaches for large recommender systems. J Mach Learn Res 10(June 2009):623–656

    Google Scholar 

  37. Takano K, Li KF (2009) An adaptive personalized recommender based on web-browsing behavior learning. In Proceedings of the 23rd International Conference on Advanced Information Networking and Applications, AINA 2009, Workshops Proceedings, Bradford, United Kingdom. https://doi.org/10.1109/WAINA.2009.160

  38. The LEADLAB Project (2010) Retrieved from http://leadlab.euproject.org/services/files/Download/LEADLABMODEL-EN.pdf Accessed 3 May 2018

  39. Verbert K, Drachsler H, Manouselis N, Wolpers M, Vuorikari R, Duval E (2011) Dataset-driven research for improving recommender systems for learning. Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK’11). ACM, New York, pp 44–53

    Google Scholar 

  40. Watkins C, Carnell E, Lodge C (2007) Effective learning in classrooms. Paul Chapman Publishing, London

    Book  Google Scholar 

  41. Zhuhadar L, Butterfield J (2014) Analyzing students logs in open online courses using SNA techniques. Proceedings of the 20th Americas Conference on Information Systems

Download references

Acknowledgements

This work is partially supported by CNPq (Brazilian Council for Scientific and Technological Development), and CAPES.

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  1. Informatics Institute, Universidade Federal do Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre, Brazil

    Alessandro da S. Dias & Leandro K. Wives

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  1. Alessandro da S. Dias
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Correspondence to Alessandro da S. Dias.

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da S. Dias, A., Wives, L.K. Recommender system for learning objects based in the fusion of social signals, interests, and preferences of learner users in ubiquitous e-learning systems. Pers Ubiquit Comput 23, 249–268 (2019). https://doi.org/10.1007/s00779-018-01197-7

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  • DOI: https://doi.org/10.1007/s00779-018-01197-7

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