The week's pick
Reseach Article
Student Modeling in Distributed Adaptive Knowledge based E-Learning Environment
by
Santosh Kumar Agarwal,
Madhavi Sinha
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 76 - Number 14 |
| Year of Publication: 2013 |
| Authors: Santosh Kumar Agarwal, Madhavi Sinha |
10.5120/13317-0935
|
Santosh Kumar Agarwal, Madhavi Sinha . Student Modeling in Distributed Adaptive Knowledge based E-Learning Environment. International Journal of Computer Applications. 76, 14 ( August 2013), 30-36. DOI=10.5120/13317-0935
@article{
10.5120/13317-0935,
author = {
Santosh Kumar Agarwal,
Madhavi Sinha
},
title = { Student Modeling in Distributed Adaptive Knowledge based E-Learning Environment },
journal = {
International Journal of Computer Applications
},
issue_date = { August 2013 },
volume = { 76 },
number = { 14 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = {
30-36
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume76/number14/13317-0935/
},
doi = { 10.5120/13317-0935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:45:55.338858+05:30
%A Santosh Kumar Agarwal
%A Madhavi Sinha
%T Student Modeling in Distributed Adaptive Knowledge based E-Learning Environment
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 14
%P 30-36
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The previously developed and researches of e-learning system are based on "one size fits all" approaches. Where differences among the learners and student were disregarded and supplied the same learning materials to the learner or students. The newly research and development style changes the needs and preferences for the researcher and learners. The result of this required most adaptive and distribute knowledge-based e-learning system. In the present paper distributed adaptive knowledge based model for e –learning system is described which primarily focuses on student modelling module. The student modelling is responsible to fulfil the individual requirement in teaching –learning environment.
References
- De Bra P, Aroyo L, Cristea A. Adaptive web-based educational hypermedia. Book chapter in: Mark Levene, Alexandra Poulovassilis (Eds. ) Web dynamics, adaptive to change in content, size, topology and use Springer. , pp. 387-410, 2004.
- G. Weber and P. Brusilovsky, "ELM-ART: An Adaptive Versatile System for Web-based Instruction," International journal of Artificial Intelligence in education, vol. 12, pp. , 2001.
- Paredes P, Rodriguez P, Considering learning styles in adaptive web-based education, In Nagib Callaos, Ana Breda and MªYolanda Fernandez (eds) Proceedings of the 6th World Multi-conference on Systemics, Cybernetics and Informatics. Orlando, Fl. , 2:481-485,2002.
- Manochehr N,The influence of learning styles on learners in elearning environments: An empirical study, Computers in HigherEducation Economics Revew, 18(1): 10-14, 2006.
- K. M. Fouad, M. A. Hogo, S. Ganalel-Din, N. M. Nagdy, "Adaptive Elearning System Based on Semantic Web and Fuzzy Clustering", International Journal of Computer science and Information Security,vol 8, No. 9, 2010.
- Brown E, Brailsford T, Fisher T, Moore A, Evaluating learning style personalization in adaptive systems: Quantitative methods and approaches. IEEE Trans. Learn. Technol. , 2(1): 10-22, 2009.
- National Association of State Boards of Education Study Group (2001). Any time, any place, any path, any pace: Taking the lead on e-learning policy, Retrieved April 10, 2003, from http://www. nasbe. org/e_Learning. html.
- IEEE LTSC. (2003). Learning object metadata. Retrieved April 10, 2003.
- IMS Global Learning Consortium. (2001). Learning resource metadata (version 1. 2. 1). Retrieved April 10, 2003,.
- Advanced Distributed Learning (2001). SCORM (version 1. 2). Retrieved April 10, 2003.
- Mislevy, R. J. , Steinberg, L. S. ,&Almond, R. G. (1999). On the roles of task model variables in assessment design (CSE Tech. Rep. No. 500). Los Angeles: University of California, Center for the Study of Evaluation, Graduate School of Education & Information Studies.
- Quinn, C. , Framework for a learning management system [Slide]. Unpublished PowerPoint presentation, Knowledge Planet. com, Emeryville, CA, 2001.
- Gagné, R. M. (1965). The conditions of learning. New York: Holt, Rinehart & Winston.
- Kruse, K. (2000). Web rules. (20000. Retrieved April 10, 2003.
- Gagné, R. M. , Briggs, L. J. , & Wager, W. W. , Principles of instructional design (4th ed. ). Fort Worth, TX: Harcourt Brace , 1992.
- Shute, V. J. , SMART: Student Modeling Approach for Responsive Tutoring. User Modeling and User-Adapted Interaction, 5, 1–44, 1995.
- Mislevy, R. J. , Almond, R. G. , Yan, D. , & Steinberg, L. S. , Bayes nets in educational assessment: Where do the numbers come from? In K. B. Laskey & H. Prade (Eds. ), Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 437–446). San Francisco: Kaufmann, 1999.
- Koza, J. , Genetic programming. Cambridge, MA: MIT Press, 1992.
- Andre, D. Learning and upgrading rules for an OCR system using genetic programming. In Proceedings of the First IEEE Conference on Evolutionary Computation (Vol. 1,pp. 462–467). Piscataway, NJ: IEEE. 1994.
- Snow, C. E,. & Jones, J. , Making a silk purse. Education Week Commentary, 25April, 2001.
- BinglanHan Masters' Thesis, "STUDENT MODELLING AND ADAPTIVITY IN WEB BASED LEARNING SYSTEMS" Massey University New Zealand, 2001.
- Asnicar, F. A. and Tasso, C. (1997) if Web: A prototype of user model-based intelligent agent for document filtering and navigation in the World Wide Web. In: P. Brusilovsky, J. Fink and J. Kay Proceedings of Workshop "Adaptive Systems and User Modeling on the World Wide Web" at 6th International Conference on User Modeling, UM97, Chia Laguna, Sardinia, Italy, 2 June, 1997.
- Edmonds, A. N. , Burkhardt, D. ,&Adjei, O. , Genetic programming of fuzzy logic rules. In Proceedings of the Second IEEE Conference on Evolutionary Computation, Vol. 2, 1995.
- Hambleton, R. K. , Advances in assessment models, methods, and practices. In D. C. Berliner & R. C. Calfee (Eds. ), Handbook of educational psychology (pp. 889–925). New York: American Council on Education/Macmillan, 1996.
- Sampson, D. , Karagiannidis, C. , & Kinshuk. (2002). Personalised learning: Educational, technological and standardisation perspectives, interactive educational multimedia (on-line journal ISSN 1576–4990), Special Issue on Adaptive Educational Multimedia, 4 (invited paper), April, 2002.
- Shute, V. J. , Lajoie, S. P. , & Gluck, K. A. (2000). Individualized and group approaches to training. In S. Tobias & J. D. Fletcher (Eds. ), Training and retraining: A handbook for business, industry, government, and the military (pp. 171–207). New York: Macmillan.
- Minstrell, J. , Student thinking and related instruction: Creating a facet-based learning environment. In J. Pellegrino, L. Jones, & K. Mitchell (Eds. ), Grading the nation's report card: Research for the evaluation of NAEP. Washington, DC: National Academy Press, 2000.
- Allen, I. E. and Seaman, J. (2003) Sizing the Opportunity: The Quality and Extent of Online Education in the United States, 2002 and Wellesley, 2003.
- Bates, A. and Poole, G. Effective Teaching with Technology in Higher Education San Francisco: Jossey-Bass/John Wiley, 2003.
Index Terms
Computer Science
Information Sciences
Keywords