Abstract
E-learning systems often include a personalization component, which adapts the learning content to the learner’s particular needs. One obstacle to personalization is the question of how to obtain a learner profile for a learner who just starts using an E-learning system without overwhelming her with questions or unsuitable learning material. One possible solution to this problem lies in the social Web. If a learner is active on the social Web, a considerable amount of information about her is already available. Depending on the social Web service(s) the learner uses, her tweets, photos, bookmarks, etc. are publicly accessible. We investigate if it is feasible to exploit the social Web, more specifically the social Web service Twitter, to infer a learner’s knowledge profile in order to overcome the “cold-start” problem in E-learning systems.
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References
Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Amsterdam (1999)
Borau, K., Ullrich, C., Feng, J., Shen, R.: Microblogging for language learning: Using twitter to train communicative and cultural competence. In: Spaniol, M., Li, Q., Klamma, R., Lau, R.W.H. (eds.) ICWL 2009. LNCS, vol. 5686, pp. 78–87. Springer, Heidelberg (2009)
Cavnar, W., Trenkle, J.: N-gram-based text categorization. In: SDAIR 1994, pp. 161–175 (1994)
Hecht, B., Hong, L., Suh, B., Chi, E.H.: Tweets from justin bieber’s heart: the dynamics of the location field in user profiles. In: CHI 2011, pp. 237–246 (2011)
Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pp. 56–65. ACM Press, New York (2007)
Johnson, K.: The effect of Twitter posts on students perceptions of instructor credibility. Learning, Media and Technology 36(1), 21–38 (2011)
Krovetz, R.: Viewing morphology as an inference process. In: SIGIR 1993, pp. 191–202 (1993)
Lerman, K., Ghosh, R.: Information contagion: An empirical study of the spread of news on Digg and Twitter social networks. In: ICWSM 2010, pp. 90–97 (2010)
Letierce, J., Passant, A., Breslin, J., Decker, S.: Understanding how Twitter is used to widely spread Scientific Messages. In: WebSci 2010: Extending the Frontiers of Society On-Line (2010)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT Press, Cambridge (1999)
McWilliams, J., Hickey, D., Hines, M., Conner, J., Bishop, S.: Using Collaborative Writing Tools for Literary Analysis: Twitter, Fan Fiction and The Crucible in the Secondary English Classroom. Journal of Media Literacy Education 2(3), 238–245 (2011)
Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: AND 2010, pp. 73–80 (2010)
Mislove, A., Lehmann, S., Ahn, Y.-Y., Onnela, J.-P., Rosenquist, J.N.: Understanding the Demographics of Twitter Users. In: ICWSM 2011 (2011)
Naaman, M., Boase, J., Lai, C.-H.: Is it really about me?: message content in social awareness streams. In: CSCW 2010, pp. 189–192 (2010)
Phelan, O., McCarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: RecSys 2009, pp. 385–388 (2009)
Priem, J., Costello, K.L.: How and why scholars cite on twitter. In: ASIS&T 2010, pp. 75:1–75:4 (2010)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in twitter. In: SMUC 2010, pp. 37–44 (2010)
Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, pp. 41–46 (2001)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW 2010, pp. 851–860 (2010)
Westman, S., Freund, L.: Information interaction in 140 characters or less: genres on twitter. In: IIiX 2010, pp. 323–328 (2010)
Zhao, D., Rosson, M.B.: How and why people twitter: the role that micro-blogging plays in informal communication at work. In: GROUP 2009, pp. 243–252 (2009)
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Hauff, C., Houben, GJ. (2011). Deriving Knowledge Profiles from Twitter. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds) Towards Ubiquitous Learning. EC-TEL 2011. Lecture Notes in Computer Science, vol 6964. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23985-4_12
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DOI: https://doi.org/10.1007/978-3-642-23985-4_12
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