International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.4, No.4, May. 2012

Comparison and Evaluation of Intelligence Methods for Distance Education Platform

Full Text (PDF, 153KB), PP.21-27

Views:172   Downloads:4


Maysam Hedayati,Seyed Hossein Kamali,Reza Shakerian

Index Terms

E-Learning, Intelligence Methods, ANN, SVM, Comparison


In this paper two favorite artificial intelligence methods: ANN and SVM are proposed as a means to achieve accurate question level diagnosis, intelligent question classification and updates of the question model in intelligent learning environments such as E-Learning or distance education platforms. This paper reports the investigation of the effectiveness and performances of two favorite artificial intelligence methods: ANN and SVM within a web-based environment (E-Learning) in the testing part of an undergraduate course that is "History of Human Civilizations" to observe their question classification abilities depending on the item responses of students, item difficulties of questions and question levels that are determined by putting the item difficulties to Gaussian Normal Curve. 
The effective nesses of ANN and SVM methods were evaluated by comparing the performances and class correct nesses of the sample questions using the same 3 inputs as: item responses, item difficulties, question levels to 5018 rows of data that are the item responses of students in a test composed of 13 questions. The comparative test performance analysis conducted using the classification correctness revealed yielded better performances than the Artificial Neural Network (ANN) and Support Vector Machine (SVM).

Cite This Paper

Maysam Hedayati,Seyed Hossein Kamali,Reza Shakerian,"Comparison and Evaluation of Intelligence Methods for Distance Education Platform", IJMECS, vol.4, no.4, pp.21-27, 2012.


[1]Dalziel, J. "Integrating Computer Assisted Assessment with Textbooks and Question Banks: Options for Enhancing Learning", Fourth Annual Computer Assisted Assessment Conference, Loughborough, UK, 2000.

[2]Fei T., Heng W.J., Toh K.C., Qi T. "Question classification for elearning by artificial neural network," Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint Conference of the Fourth International Conference, 3, pp.1757 – 1761, 2003.

[3]Hacioglu K., Ward W. "Question Classification with Support Vector Machines and Error Correcting Codes," in the Proceedings of HLT-NACCL 2003, Edmonton, Alberta, Canada, pp. 28-30, 2003.

[4]Zaanen M., Pizzato L. A., Mollá D. "Question lassification by Structure Induction". Proceedings of the International Joint Conferences on Artificial Intelligence, Edinburgh, U.K., pp. 1638-1639, 2005.

[5]Zhang D., Lee W.S. "Question Classification using Support Vector Machines." In Proceedings of the 26th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), Toronto, Canada, 2003.

[6]Haykin, S. Neural Networks: A Comprehensive Foundation, 2nd edn., Prentice Hall International, Inc, 1999.

[7]Churchland P.S. and Sejnowski T.J. The computational Brain, Cambridge, MA: MIT Press, 1992.

[8]Genov R., Member, IEEE, and Gert Cauwenberghs. "Kerneltron: Support Vector "Machine" in Silicon", IEEE TRANSACTIONS ON NEURAL NETWORKS, 14(5), 2003.

[9]Boser B., Guyon I., and Vapnik V. "A training algorithm for optimal margin classifier," in Proc. 5th Annu. ACM Workshop Computational Learning Theory, pp. 144–52, 1992.

[10]Vapnik V. The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995. Girosi F., Jones M., and Poggio T. "Regularization theory and neural networks architectures," Neural Comput., 7, pp. 219–269, 1995.