Study partners recommendation for xMOOCs learners

Computational Intelligence and Neuroscience

Volumn 2015, Issue , 2015, Pages

  Xu, Bin ^a   Yang, Dan ^b  

^a NORTHEASTERN UNIVERSITY   (China)

^b NORTHEASTERN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EDUCATION; SOCIAL NETWORKING (ONLINE); STUDENTS; TEACHING;

BETTER PERFORMANCE; COLLEGE TEACHERS; CONTENT INFORMATION; CONTENT OF MESSAGES; DISCUSSION FORUM; KNOWLEDGE AWARENESS; MASSIVE OPEN ONLINE COURSE; SOCIAL NETWORK INFORMATIONS;

CURRICULA;

ALGORITHM; BEHAVIOR; HUMAN; INTERNET; LEARNING; PHYSIOLOGY; SOCIAL SUPPORT; STUDENT;

ALGORITHMS; BEHAVIOR; HUMANS; INTERNET; LEARNING; SOCIAL SUPPORT; STUDENTS;

EID: 84947649455     PISSN: 16875265     EISSN: 16875273     Source Type: Journal    
DOI: 10.1155/2015/832093     Document Type: Article

References (25)

1. F. M. Hollands andD. Tirthali,MOOCs: expectations and reality full report [Ph. D. thesis], Center for Benefit-Cost Studies of Education, Teachers College, Columbia University, 2014.
2. "Measuring the MOOC Dropout Rate [EB/OL]," March 2013, http://www.insidehighered.com/news/2013/03/08/researchersexplore-who-taking-moocs-and-why-so-many-drop-out.
3. L. Perna, A. Ruby, R. Boruch et al., "The life cycle of a million MOOC users," in Proceedings of the MOOC Research Initiative Conference, December 2011, http://www.gse.upenn.edu/pdf/ahead/pernarubyboruchmoocsdec2013. pdf.
4. L. M. Vaquero andM. Cebrian, "The rich club phenomenon in the classroom," Scientific Reports, vol. 3, article 1174, 2013.
5. D. Coetzee, A. Fox, M. A. Hearst, and B. Hartmann, "Should your MOOC forum use a reputationsystem?" in Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW '14), pp. 1176-1187, 2014.
6. G. Balakrishnan and D. Coetzee, Predicting student retention in massive open online courses using hidden markov model [Ph. D. thesis], 2013.
7. X. Chen, M. Vorvoreanu, and K. Madhavan, "Mining social media data for understanding students' learning experiences," IEEE Transactions on Learning Technologies, vol. 7, no. 3, pp. 246-259, 2014.
8. J. Jiang, C. Wilson, X. Wang et al., "Understanding latent interactions in online social networks," ACM Transactions on theWeb, vol. 7, no. 4, article 18, 2013.
9. A. Koutropoulos, S. C. Abajian, I. de Waard, R. Hogue, N. O. Keskin, andC. O. Rodriguez, "What tweets tellus aboutMOOC participation," International Journal of Emerging Technologies in Learning, vol. 9, no. 1, pp. 8-21, 2014.
10. F. Griinewald, C. Meinel, M. Totschnig et al., "Designing MOOCs for the support of multiple learning styles," in Scaling up Learning for Sustained Impact, vol. 8095 of Lecture Notes in Computer Science, pp. 371-382, Springer, Berlin, Germany, 2013.
11. A. Anderson, D. Huttenlocher, J. Kleinberg, and J. Leskovec, "Engaging with massive online courses," in Proceedings of the ACMInternational Conference onWorldWideWeb (WWW'14), pp. 687-698, 2014.
12. T. Sinha, "Supporting MOOC instruction with social network analysis," http://arxiv.org/abs/1401.5175.
13. C. G. Brinton,M. Chiang, S. Jain, H. Lam, Z. Liu, and F. Wong, "Learning about social learning in MOOCs: from statistical analysis to generative model," IEEE Transactions on Learning Technologies, 2014.
14. L. B. Breslow,D. E. Pritchard, J. DeBoer et al., "Studying learning in the worldwide classroom: research into edX's first MOOC," Research and Practice in Assessment, vol. 8, pp. 13-25, 2013.
15. J. Naruchitparames, M. H. Gunes, and S. J. Louis, "Friend recommendations in social networks using genetic algorithms and network topology," in Proceedings of the IEEE Congress of Evolutionary Computation (CEC '11), pp. 2207-2214, June 2011.
16. D. J. Hruschka and J. Henrich, "Friendship, cliquishness, and the emergence of cooperation," Journal of Theoretical Biology, vol. 239, no. 1, pp. 1-15, 2006.
17. Z. Du, L. Hu, X. Fu, and Y. Liu, "Scalable and explainable friend recommendation in campus social network system," in Frontier and Future Development of Information Technology inMedicine and Education, vol. 269 of Lecture Notes in Electrical Engineering, pp. 457-466, Springer, Amsterdam,The Netherlands, 2014.
18. L. Bian and H. Holtzman, "Online friend recommendation through personality matching and collaborative filtering," in Proceedings of the 5th International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM '11), pp. 230-235, IARIA, 2011.
19. V. Agarwal and K. K. Bharadwaj, " A collaborative filtering framework for friends recommendationin social networks based on interaction intensity and adaptive usersimilarity," Social Network Analysis and Mining, vol. 3, no. 3, pp. 359-379, 2013.
20. D. Liben-Nowell and J. Kleinberg, "Thelink prediction problem for social networks," in Proceedings of the 12th ACM International Conference on Information and Knowledge Management (CIKM '03), pp. 556-559, November 2003.
21. P. Symeonidis, E. Tiakas, and Y. Manolopoulos, "Transitive nodesimilarity for link prediction in social networks with positive andnegative links," in Proceedings of the 4th ACM Conference on Recommender Systems (RecSys '10), pp. 183-190, 2010.
22. D. Xu, P. Cui,W. Zhu, and S. Yang, "Find you fromyour friends: graph-based residence location prediction for users in social media," in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME '14), pp. 1-6, Chengdu, China, July 2014.
23. M. Manca, L. Boratto, and S. Carta, "Producing friend recommendations in a social bookmarking system by mining users content," in International Conference on Advances in Information Mining and Management (IMMM '13), 2013.
24. Z. Wang, J. Liao, Q. Cao, H. Qi, and Z. Wang, "Friendbook: a semantic-based friend recommendation system for social networks," IEEE Transactions on Mobile Computing, 2014.
25. M. Cheung and J. She, "Bag-of-features tagging approach for a better recommendation with social big data," in Proceedings of the 4th International Conference on Advances in Information Mining and Management (IMMM '14), pp. 83-88, 2014.