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Study of an Adaptive Web Based E-Learning System through SVM


Affiliations
1 Informatics & Computational Sciences, Mohanlal Sukhadia University, Udaipur, Rajasthan, India
 

In this article we are presenting machine learning mechanism through Support Vector Machine (SVM). Through SVM the automatic learning task on various types of learners will analyze. SVM is another technique in machine learning and it’s also helpful for analysis of learner’s knowledge level. SVM aims at facilitating searching and organizing learning objects. During an evaluation period, the SVM models are used for the classification of different learning objects according to the different parameters of SVM like correct rate, support vectors, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood value, negative likelihood value and prevalence. Through the parameters SVM models can also analyze learner’s knowledge level category and Adaptive Web Based E-Learning System [39] can perform accordingly.

Keywords

Adaptive Web Based E-Learning System (AWBES), SVM.
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  • Ahmad Abdul Rahim, Khalid Marzuki, Yusof Rubiyah. Machine Learning Using Support Vector Machines.

  • Akhras, F. N. and Felix, J. A. (2000) System Intelligence in Constructivist Learning. International Journal of Artificial Intelligence in Education (IJAIED) 11, 344–376. [Cited on pages 33, 39, 50 and 53.]

  • B¨ocker, H. D., Hohl, H. and Schwab, T. (September, 2005). Hypadapter Individualizing Hypertext. Proceedings of the IFIP TC13 Third Interational Conference on Human-Computer Interaction, pages 931–936, Amsterdam, The Netherlands, North-Holland Publishing Co.

  • Basheer, I. A., and Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design and application. Journal of Microbiological Methods, 43, 3–31.

  • Baylari Ahmad, Montazer, Gh. A. (2009). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 36 8013–8021.

  • Beaumont, I. (1994). User modeling in the interactive anatomy tutoring system ANATOM-TUTOR. In User Models and User Adapted Interaction, 4(1), pages 21– 45.

  • Boyle, C. D. B., and Encarnacion, A. O. (1994). Metadoc: An Adaptive Hypertext Reading System. In User Model. User-Adapt. Interact., 4(1), pages 1–19.

  • Brusilovsky, P. (1992) Intelligent Tutor, Environment and Manual for Introductory Programming. In Educational and Training Technology International, 29(1), pages 26–34.

  • Brusilovsky, P. (1994). The Construction and Application of Student Models in Intelligent Tutoring Systems. Journal of Computer and System Sciences International 32(1), 70–89. [Cited on page 75.].

  • Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User- Adapted Interaction (UMUAI) 6(2-3), 87–129. [Cited on pages 16, 23, 25, 33, 34, 79 and 80.].

  • Brusilovsky, P. (2000) Adaptive Hypermedia: From Intelligent Tutoring Systems to Web-Based Education. In: Proceedings of the International Conference on Intelligent Tutoring Systems (ITS). pp. 1–7. [Cited on pages 53 and 133.].

  • Brusilovsky, P. (2004). KnowledgeTree: A Distributed Architecture for Adaptive ELearning. In: Proceedings of the World Wide Web Conference (WWW). pp. 104– 113. [Cited on pages xvii, 17, 57, 81 and 134.].

  • Brusilovsky, P., Eklund, J. and Schwarz, E. (April, 1998).Web-Based Education for All: A Tool for Developing Adaptive Courseware. In Proceedings of Seventh International World Wide Web Conference, WWW 98, pages 291–300.

  • Brusilovsky, P., Farzan, R. and Ahn, J.W. (2006). Layered Evaluation of Adaptive Search. In Proceedings of Workshop on Evaluating Exploratory Search Systems, at SIGIR2006.

  • Bull, J. and McKenna, C. (2004). Blueprint for computer-assisted assessment. London, Routledge-Falmer.

  • Chen, C.M., Lee, H.M., and Chen, Y.H. (2005). Personalised e-learning system using item response theory. Computers & Education, 44(3), 237-255.

  • Cohn, D., Ghahramani, Z., and Jordan, M. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129–145, 1996.

  • De Bra, P. and Calvi, L. (1998). AHA! An open Adaptive Hypermedia Architecture. In The New Review of Hypermedia and Multimedia, 4, pages 115– 139, Taylor Graham Publishers.

  • De Bra, P., Aerts, A., Smits, D. and Stash, N. (May, 2002). AHA! meets AHAM. In Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pages 381–384, Springer LNCS 2347.

  • Gamoran, A., Secada, W. and Marrett, C. (2006). The Organizational Context of Teaching and Learning Changing Theoretical Perspectives. Handbooks of Sociology and Social Research, (1):37–63.

  • Garcia, D. F., Uria, C., Granda, J. C., Suarez, F. J. and Gonzalez, F. (2007). Functional Evaluation of the Commercial Platforms and Tools for Synchronous Distance e-Learning. North Atlantic University Union International Journal of Education and Information Technologies, 1(2):95–104.

  • Garcia-Barrios, V. M., G¨utl, C., and Pivec, M. (2002). Semantic Knowledge Factory: A New Way of Cognition Improvement for the Knowledge Management Process. In: Proceedings of the International Conference on Society for Information Technology & Teacher Education (SITE). pp. 168–172. [Cited on pages xviii, 53, 93, 134, 135 and 136.].

  • Georgiadou, E., and Economides, A. (2000). Evaluation Factors of Educational Software. Proceedings of the International Workshop on Advanced Learning Technologies Proceedings, Los Alamitos, CA: IEEE Computer Society, 113-116.

  • Gilbert, J. E., and Han, C. Y. (2002). Arthur: A personalized instructional system. Journal of Computing in Higher Education, 14(1), 113-129.

  • Granda, J. C., Ura, C., Surez, F. J., and Garca, D. F. (2010). An efficient networking technique for synchronous e-learning platforms in corporate environments. Computer Communications, 33(14).

  • Gunawardena, C. N. and McIsaac, M. S. (2004). Distance Education. Handbook of Research on Educational Communications and Technology. Mahwah: Lawrence Erlbaum Associates, second edition, pp. 355–395. [Cited on pages 27 and 39.].

  • Hamdi, M. S. (2007). MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students. Applied Soft Computing, 7, 746–771.

  • Holmes, B. and Gardner, J. (2006). E-Learning: Concepts and Practice. SAGE Publications.

  • Honggang Wu, (November, 2002). Designing a Reusable and Adaptive E-Learning System”, University of Saskatchewan.

  • Honggang Wu. (November, 2002). Designing a Reusable and Adaptive E-Learning System. University of Saskatchewan, Saskatoon.

  • Jain, L. C., Howlett, R. J., Ischalkaranje, N. S. and Tonfoni, G. Virtual Environments for Teaching & Learning. (2002) Singapore: World Scientific Publishing. [Cited on pages 27 and 65.].

  • Khaled Fouad, M., Mostafa Nofal A., Hany Harb, M., Nagdy Nagdy, M. (2011). Using Semantic Web to support Advanced Web-Based Environment” (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 12.

  • Klausmeier, H. J. (1976). Individually Guided Education: 1966-1980. Journal of Teacher Education 27(3), 199–205. [Cited on page 51.].

  • Lewis, B. A., MacEntee, V. M., DeLaCruz, S., Englander, C., Jeffrey, T., Takach, E., S. W., and Woodall, J. (2005). Learning Management Systems Comparison. In Informing Science and IT Education Joint Conference.

  • Lewis, B. A., MacEntee, V. M., DeLaCruz, S., Englander, C., Jeffrey, T., Takach, E., S. W., and Woodall, J. (2005). Learning Management Systems Comparison. In Informing Science and IT Education Joint Conference.

  • Merino J.C.S. (2010). Challenging students’ responsibility: An engagement methodology. In The Engineering Education Conference (EDUCON), pages 253 – 262.

  • Nello Cristianini and John Shawe-Taylor, (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press.

  • Parminder Kaur, Kiranjit Kaur, Gurdeepak Singh (2012). Improving E-Learning with Neural Networks, International Journal of Computing & Business Research ISSN (Online): 2229-6166, Proceedings of ‘I-Society’ at GKU, Talwandi Sabo Bathinda (Punjab)

  • Pooja Shrimali, K. Venugopalan, Prabha Vajpeyee; “Study and Analysis of an Adaptive Web Based ELearning System”, International Journal of Computer Application, ISSN: 2250-1797, Issue 4, Volume 3 (May - July 2014). http://www.rspublication.com/ijca/ijca_index.htm

  • Seridi Hassina, Sari Toufik and Sellami Mokhtar. (july, 2006). Adaptive Instructional Planning Using Neural Networks in Intelligent Learning Systems. The international Arab journal of Information Technology Vol 3 No.3.

  • Singh, H. (2003). Building Effective Blended Learning Programs. Educational Technology, 43(6):51–54.

  • Susan, G., Mirco, S. and Alexander, P. (2007). Ontology-Based User Profiles for Personalized Search, DOI10.1007/978-0-387-37022-4, Springer US.

  • Yevgen, B., Hamidreza, B., Igor, K., Michael, F. (2009). An adjustable personalization of search and delivery of learning objects to learners. Expert Systems with Applications Elsevier Ltd. 36 (2009) 9113–9120, doi:10.1016/j.eswa.2008.12.038.

  • Zhang Linfeng, Fei YU, Yue Shen, Guiping Liao, Ken Chen. (July, 2007). E-learning System Based on Neural Networks. Proceedings of the World Congress on Engineering, London, U.K.


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  • Study of an Adaptive Web Based E-Learning System through SVM

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Authors

Pooja Shrimali
Informatics & Computational Sciences, Mohanlal Sukhadia University, Udaipur, Rajasthan, India

Abstract


In this article we are presenting machine learning mechanism through Support Vector Machine (SVM). Through SVM the automatic learning task on various types of learners will analyze. SVM is another technique in machine learning and it’s also helpful for analysis of learner’s knowledge level. SVM aims at facilitating searching and organizing learning objects. During an evaluation period, the SVM models are used for the classification of different learning objects according to the different parameters of SVM like correct rate, support vectors, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood value, negative likelihood value and prevalence. Through the parameters SVM models can also analyze learner’s knowledge level category and Adaptive Web Based E-Learning System [39] can perform accordingly.

Keywords


Adaptive Web Based E-Learning System (AWBES), SVM.

References