Abstract
The aim of the paper is the probability modelling of accesses to the categories of activities of e-learning course in learning management system. We are concerned with the access probabilities to individual activities of e-learning course content depending on the part of the week (workweek and weekend). The probabilities are estimated through multinomial logit model. We pay attention to data preparation issues. We describe used model in more detail and deal with parameter estimations. Finally, we figure that the multinomial logit model finds its application mainly in the process of restructuring the existing e-learning courses. We discuss about its possible contribution to the improvement of the learning management as well as in the personalization of the course content and structure.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Anděl, J.: Základy matematické štatistiky. MATFYZPRESS, Praha (2007)
Rodríguez, G.: Generalized Linear Models (2007)
Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression. John Wiley and Sons, Inc., Chichester (2000)
Baltagi, B.D.: Econometrics. Springer, Heidelberg (2008)
Macfadyen, L.P., Dawson, S.: Mining LMS data to develop an ”early warning system” for educators: A proof of concept. Comput. Educ. 54, 588–599
Stratton, L.S., O’Toole, D.M., Wetzel, J.N.: A multinomial logit model of college stopout and dropout behavior. Economics of Education Review 27, 319–331 (2008)
Domenech, J.M., Lorenzo, J.: A tool for web usage mining. In: Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning. Springer, Birmingham (2007)
Drlik, M., Munk, M., Skalka, J.: Usage Analysis of System for Theses Acquisition and Plagiarism Detection. Procedia Computer Science (2010)
Chitraa, V., Davamani, A.S.: A Survey on Preprocessing Methods for Web Usage Data. International Journal of Computer Science and Information Security 7 (2010)
Zaine, O.R., Xin, M., Han, J.: Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs. In: Proceedings of the Advances in Digital Libraries Conference. IEEE Computer Society, Los Alamitos (1998)
Mor, E., Minguillon, J.: E-learning personalization based on itineraries and long-term navigational behavior. In: Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers & posters. ACM, New York (2004)
Wei, W., Jui-Feng, W., Jun-Ming, S., Shian-Shyong, T.: Learning portfolio analysis and mining in SCORM compliant environment. In: 34th Annual Frontiers in Education, FIE 2004, vol. 11 (2004), T2C-17-24
Talavera, L., Gaudioso, E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. In: Workshop on Artificial Intelligence in CSCL, Valencia, Spain, pp. 17–23 (2004)
Ramli, A.A.: Web usage mining using apriori algorithm: UUM learning care portal case. In: International Conference on Knowledge Management, Malaysia, pp. 1–19 (2005)
Raju, G.T., Satyanarayana, P.S.: Knowledge Discovery from Web Usage Data: a Complete Preprocessing Methodology. IJCSNS International Journal of Computer Science and Network Security 8 (2008)
Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33, 135–146 (2007)
Gaudioso, E., Talavera, L.: Data mining to support tutoring in virtual learning communities: Experiences and challenges. In: Romero, C., Ventura, S. (eds.) Data Mining in e-learning, pp. 207–226. Wit Press, Southampton (2006)
Ba-Omar, H., Petrounias, I., Anwar, F.: A Framework for Using Web Usage Mining to Personalise E-learning. In: Seventh IEEE International Conference on Advanced Learning Technologies, ICALT 2007, pp. 937–938 (2007)
Bayir, M.A., Toroslu, I.H., Cosar, A.: A New Approach for Reactive Web Usage Data Processing. In: Proceedings of the 22nd International Conference on Data Engineering Workshops, p. 44 (2006)
Zhang, H., Liang, W.: An intelligent algorithm of data pre-processing in Web usage mining. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA), pp. 3119–3123 (2004)
Cooley, R., Mobasher, B., Srivastava, J.: Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information Systems 1, 5–32 (1999)
Yan, L., Boqin, F., Qinjiao, M.: Research on Path Completion Technique in Web Usage Mining. In: International Symposium on Computer Science and Computational Technology, ISCSCT 2008, vol. 1, pp. 554–559 (2008)
Yan, L., Boqin, F.: The Construction of Transactions for Web Usage Mining. In: International Conference on Computational Intelligence and Natural Computing, CINC 2009, pp. 121–124 (2009)
Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis. INFORMS J. on Computing 15, 171–190 (2003)
Munk, M., Kapusta, J., Svec, P.: Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor. Procedia Computer Science 1, 2273–2280 (2010)
Anděl, J.: Matematická štatistika. SNTL, Praha (1985)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Munk, M., Drlik, M., Vrábelová, M. (2011). Probability Modelling of Accesses to the Course Activities in the Web-Based Educational System. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds) Computational Science and Its Applications - ICCSA 2011. ICCSA 2011. Lecture Notes in Computer Science, vol 6786. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21934-4_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-21934-4_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21933-7
Online ISBN: 978-3-642-21934-4
eBook Packages: Computer ScienceComputer Science (R0)