Marian Cristian Mihăescu
ABSTRACT
Proper classification of students is one of the key aspects in e-Learning environments. This paper uses support vector machines (SVM) for classifying users whose features are represented by the performed activity. The classification of students is performed at discipline level since the features are related only to general activities. The output of the process is represented by a set of classes. The obtained classes are further used for classifying new users, whose activity data has not been used for building the classifier.
- D.D. Burdescu and M. C. Mihaescu, "Tesys: e-Learning Application Built on a Web Platform", Proceedings of International Joint Conference on e-Business and Telecommunications, Setubal, Portugal, pp. 315--318, 2006.Google Scholar
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I. H. Witten, "The WEKA Data Mining Software: An Update", SIGKDD Explorations, Volume 11, Issue 1, 2009. Google ScholarDigital Library
- A. K. Kaw and E. E. Kalu, Numerical Methods with Applications, http://www.autarkaw.com, second edition, 2010.Google Scholar
- C. Romero and S. Ventura, "Educational Data Mining: A Survey from 1995 to 2005", Expert Systems with Applications, 33(1), pp. 135--146, 2007. Google ScholarDigital Library
- E. García, C. Romero, S. Ventura, T. Calders, "Drawbacks and solutions of applying association rule mining in learning management systems", Proceedings of the International Workshop on Applying Data Mining in e-Learning, pp. 1--10, 2008.Google Scholar
- R. Nugent, N. Dean, E. Ayers, "Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers", Proceedings of The 3rd International Conference on Educational Data Mining, pp. 151--160, 2010.Google Scholar
- N. Khodeir, N. M. Wanas, N. M. Darwish, N. Hegazy, "Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks", The 3rd International Conference on Educational Data Mining, pp. 299--300, 2010.Google Scholar
- M. Mavrikis, "Data-driven modelling of students' interactions in an ILE", The 1st International Conference on Educational Data Mining, pp. 87--96, 2008.Google Scholar
- H. Jeong and G. Biswas, "Mining Student Behavior Models in Learning-by-Teaching Environments", First International Conference on Educational Data Mining, Montreal, pp. 127--136, 2008.Google Scholar
- H. F. Yu et. al., Feature Engineering and Classifier Ensemble for KDD Cup 2010, JMLR Workshop and Conference Proceedings, Invited Paper of KDD Cup 2010 Winner, 2010.Google Scholar
- Corinna Cortes and V. Vapnik, "Support-Vector Networks", Machine Learning, 20, 1995. Google ScholarDigital Library
- V. Fomichov and O. Fomichova. The Theory of Dynamic Conceptual Mappings and its significance for Education, Cognitive Science, and Artificial Intelligence. Informatica. An Intern. Journal of Computing and Informatics (Slovenia), 1994, 18 (2).Google Scholar
- V. Fomichov and O. Fomichova. Cognitonics as a New Science and Its Significance for Informatics and Information Society. Special Issue on Developing Creativity and Broad Mental Outlook in the Information Society (Guest Editor Vladimir Fomichov), Informatica. (Slovenia), 2006, 30 (4).Google Scholar
- V. Vapnik, The Nature of Statistical Learning Theory, Springer, N. Y., 1995. Google ScholarDigital Library
- C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, pages 1--47, 1998. Google ScholarDigital Library
- Koby Crammer and Yoram Singer, On the algorithmic implementation of multiclass kernel-based vector machines, Journal of Machine Learning Research, pages 265--292, 2001. Google ScholarDigital Library
- Yoonkyung Lee, Yi Lin, and Grace Wahba, Multicategory support vector machines: Theory and application to the classification of microarray data and satellite radiance data. Journal of the American Statistical Association, 99(465):67--81, March 2004.Google ScholarCross Ref
- Erin Allwein, Robert Shapire, and Yoram Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, pages 113--141, 2000. Google ScholarDigital Library
- T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error correcting output codes. Journal of Artificial Intelligence Research, 39:1--38, 1995. Google ScholarDigital Library
- J. Friedman. Another approach to polychotomous classification. Technical report, Stanford University, 1996.Google Scholar
- Ryan Rifkin and Aldebaro Klautau. Parallel networks that learn to pronounce english text. Journal of Machine Learning Research, pages 101--141, 2004.Google Scholar
- Trevor Hastie and Robert Tibshirani. Classification by pairwise coupling. In Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998. Google ScholarDigital Library
- Burlea Schiopoiu A., Badica A., Radu C., 2011, The evolution of e-learning platform TESYS user preferences during the training processes, ECEL2011, 11--12 Novembre Brighton, UK, pp.754--761.Google Scholar
Index Terms
- Classification of users by using support vector machines
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