Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2014 | 3 | nr 1 | 77--88
Tytuł artykułu

On Using Data Mining Techniques for Context-Aware Student Grouping in E-Learning Systems

Treść / Zawartość
Warianty tytułu
Języki publikacji
Performance of an e-learning system depends on an extent to which it is adjusted to student needs. Priorities of the last ones may differ in accordance with the context of use of an e-learning environment. For personalized e-learning system based on student groups, different distribution of the groups should be taken into account. In the paper, using of data mining techniques for building student groups depending on the context of the system use is considered. As the main technique unsupervised classification is examined,. Context parameters depending on courses and student models are tested. Experiment results for real student data are discussed. (original abstract)
Słowa kluczowe
Opis fizyczny
  • Lodz University of Technology, Poland
  • [1] Beaudoin M.F. (2002) Learning or Lurking? Tracking the "Invisible" Online Student, Internet & Higher Educ., 2/2002, 147-155.
  • [2] Schmidt A., Winterhalter C. (2004) User Context Aware Delivery of E-learning Material: Approach and Architecture, J. Univers. Comput. Sci., 10/2004, 38-46.
  • [3] Romero C., Ventura S. (2010) Educational Data Mining: a Review of the State of the Art, IEEE T. Systems, Man & Cybernetics, Part C: Applications & Reviews 6/2010, 601-618.
  • [4] Zakrzewska D. (2012) Eksploracja danych w modelowaniu użytkowników edukacyjnych systemów internetowych, AOW EXIT, Warszawa, Poland.
  • [5] Perera D., Kay J., Koprinska I., Yacef K., Zaïane O.R. (2009) Clustering and Sequential Pattern Mining of Online Collaborative Learning Data, IEEE T. Knowl. Data En., 6/2009, 759-772.
  • [6] Talavera L., Gaudioso E. (2004) Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces, Workshop on Artificial Intelligence in CSCL. 16th European Conference on Artificial Intelligence, 17-23.
  • [7] Shen R., Han P., Yang F., Yang Q., Huang J. (2003) Data Mining and Case-based Reasoning for Distance Learning, Journal of Distance Education Technologies, 3/2003, 46-58.
  • [8] Tang T., McCalla G. (2005) Smart Recommendation for an Evolving E-learning System, International Journal on E-Learning, 1/2005, 105-129.
  • [9] Minaei-Bidgoli B., Tan P., Punch W. (2004) Mining Interesting Contrast Rules for a Web-based Educational System, The Twenty-First International Conference on Machine Learning Applications, 1-8.
  • [10] Wang F. (2002) On Using Data-Mining Technology for Browsing Log File Analysis in Asynchronous Learning Environment, Conference on Educational Multimedia, Hypermedia and Telecommunications, 2005-2006.
  • [11] Jovanowić J., Gašewić D., Knight C., Richards G. (2007) Ontologies for Effective Use of Context in E-learning Settings, Educ. Technol. Soc., 10/2007, 47-59.
  • [12] Yang S.J.H. (2006) Context Aware Ubiquitous Learning Environments forPeer-to- Peer Collaborative Learning, Educ. Technol. Soc., 9/2006, 188-201.
  • [13] Das M.M., Chithralekha T., SivaSathya S. (2010) Static Context Model for Context Aware E-learning, International Journal of Engineering Science and Technology, 2/2010, 2337-2346.
  • [14] Andronico A., Carbonaro A., Casadei G., Colazzo L., Molinari A., Ronchetti M. (2003) Integrating a Multi-Agent Recommendation System into a Mobile Learning Management System, Proc. of Artificial Intelligence in Mobile Systems 200, October 12, Seattle, USA.
  • [15] Rosaci D., Sarné G. (2010) Efficient Personalization of E-learning Activities Using a Multi-Device Decentralized Recommender System, Comput. Intell., 26/2010, 121-141.
  • [16] Zaïane O.R. (2002) Building a Recommender Agent for E-learning Systems, Proc. of the 7th Int. Conf. on Computers in Education, Auckland, New Zeland, 55-59.
  • [17] Zakrzewska D. (2011) Building Context-Aware Group Recommendations in E-learning Systems, LNAI 6922, Jędrzejowicz P., Nguyen N.T., Hoang K. (eds.): Computational Collective Intelligence - Technologies and Applications. ICCCI 2011, Part I, pp. 132-141.
  • [18] Dey A.K. (2001) Understanding and Using Context, Pers. Ubiquit. Comput., 5/2004, 4-7.
  • [19] Das M.M., Chithralekha T., SivaSathya S. (2010) Static Context Model for Context Aware E-learning, International Journal of Engineering Science and Technology, 2/2010, 2337-2346.
  • [20] Viola S.R., Graf S., Kinshuk, Leo T. (2007) Investigating Relationships within the Index of Learning Styles: a Data Driven Approach, Interactive Technology & Smart Education, 4/2007, 7-18.
  • [21] Felder R.M., Silverman L.K. (1988) Learning and Teaching Styles in Engineering Education, Eng. Educ., 78/1988, 674-681.
  • [22] Index of Learning Style Questionnaire,
  • [23] De Marsico M., Levialdi S. (2004) Evaluating web sites: exploiting user's expectations, Intern. Journal of Human-Computer Studies, 60/2004, 381-416.
  • [24] Zakrzewska D., Wojciechowski A. (2008) Identifying students usability needs in collaborative learning environments, Proc. of 2008 Conference on Human System Interaction, Cracov, 862-867.
  • [25] Zakrzewska D. (2008) Validation of Cluster Analysis Techniques for Students' Grouping in Intelligent E-learning Systems, Proceedings of 14th International Congress of Cybernetics and Systems of WOSC, Wroclaw, Poland, 893-901.
  • [26] Witten I.H., Frank E. (2005) Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufmann Publishers, San Francisco, CA.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.