Abstract : In this article an innovative platform is being presented that integrates intelligent agents in E-learning environments. An approach to recognize automatically the learning styles of individual learners according to the actions that he or she has performed in an e-learning environment is introduced with the help of a scalable and interoperable integration platform supporting various assessment techniques for e-learning environments. These techniques are implemented in order to provide intelligent assessment services to computational intelligent pattern recognition technique is based upon soft computing.
Cite : Tyagi, T. (2023). Pattern Recognition Technique in E-Learning with Soft Computation (1st ed., pp. 99-107). Noble Science Press. https://doi.org/10.52458/9789388996587.2023.eb.ch22
References :
Ayre M. & Nafalski A. (2000) Recognising diverse learning styles in teaching and assessment of electronic engineering. In 30th ASEE/IEEE Frontiers in Education Conference (eds D. Budny & G. Bjedov), pp. 18–23.Kansas City, MO.
Budhu M. (2002) Interactive web-based learning using interactive multimedia simulations. International Conference on Engineering Education (ICEE 2002), pp.1–6.
Carver C.A.J., Howard R.A. & Lane W.D. (1999) Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Transactions on Education 42, pp. 33–38.
Coffield F., Moseley D., Hall E. & Ecclestone K. (2004a) Learning styles and pedagogy in post-16 learning: a systematic and critical review. Technical Report 041543, Learning and Skills Research Centre.
Felder R.M. & Silvermann L.K. (1988) Learning and teaching styles in engineering education. Journal of Engineering Education 78, 674–681.
Hornik K., Stinchcombe M. & White H. (1989) Multilayer feed forward networks are universal approximators. Neural Networks 2, 359–366.
International Network for Engineering Education & Research, Manchester, UK.
J.E. Villaverde, D. Godoy (2006) Learning style’s recognition with feed forword neural networks, journal of computer assisted learning 22,pp197-206
Kim K.S. & Moore J.L. (2005) Web-based learning: factors affecting students’ satisfaction and learning experience.
Kuri N.P. & Truzzi O.M.S. (2002) Learning styles of freshmen engineering students. In International Conference on Engineering Education (ICEE 2002), Manchester, UK.
McCulloch W.S. & Pitts W. (1943) A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133.
Pen˜a C.I., Marzo J.L. & de la Rosa J.L (2002) Intelligent agents in a teaching and learning environment on the Web. In ICALT2002. IEEE, Kazan, Russia.
Rumelhart D.E., Hinton G.E. & Williams R.J. (1986) Learning representations by back-propagating errors. Nature 323, 533–536.
Stash N. & Brau P.D. (2004) Incorporating cognitive styles in AHA! (The adaptive hypermedia architecture). In IASTED International Conference (ed. V. Uskov), pp. 378–383. ACTA Press, Innsbruck, Austria.