LS-Plan: An Effective Combination of Dynamic Courseware Generation and Learning Styles in Web-Based Education
This paper presents LS-Plan, a system capable of providing Educational Hypermedia with adaptation and personalization. The architecture of LS-Plan is based on three main components: the Adaptation Engine, the Planner and the Teacher Assistant. Dynamic course generation is driven by an adaptation algorithm, based on Learning Styles, as defined by Felder-Silverman’s model. The Planner, based on Linear Temporal Logic, produces a first Learning Objects Sequence, starting from the student’s Cognitive State and Learning Styles, as assessed through pre-navigation tests. During the student’s navigation, and on the basis of learning assessments, the adaptation algorithm can propose a new Learning Objects Sequence. In particular, the algorithm can suggest different learning materials either trying to fill possible cognitive gaps or by re-planning a newly adapted Learning Objects Sequence. A first experimental evaluation, performed on a prototype version of the system, has shown encouraging results.
KeywordsCognitive State Linear Temporal Logic Adaptation Algorithm Student Model Planning Language
Unable to display preview. Download preview PDF.
- 1.Bajraktarevic, N., Hall, W., Fullick, P.: Incorporating learning styles in hypermedia environment: Empirical evaluation. In: Proceedings of the Fourteenth Conference on Hypertext and Hypermedia, pp. 41–52 (2003)Google Scholar
- 2.Bloom, B.S.: Taxonomy of Educational Objectives. David McKay Comp. Inc. (1964)Google Scholar
- 3.De Bra, P., Smits, D., Stash, N.: Creating and delivering adaptive courses with AHA. In: EC-TEL, pp. 21–33 (2006)Google Scholar
- 5.Brusilovsky, P., Millan, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321. Springer, Heidelberg (2007)Google Scholar
- 6.Brusilowsky, P., Vassileva, J.: Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Life-long Learning 13, 75–94 (2003)Google Scholar
- 7.Capuano, N., Gaeta, M., Micarelli, A., Sangineto, E.: Automatic student personalization in preferred learning categories. In: 3rd International Conference on Universal Access in Human-Computer Interaction (2005)Google Scholar
- 10.Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Engineering Education 78(7), 674 (1988)Google Scholar
- 11.Felder, R.M., Spurlin, J.: Application, reliability and validity of the index of learning styles. Int. Journal of Engineering Education 21(1), 103–112 (2005)Google Scholar
- 13.Ghallab, M., Howe, A., Knoblock, C., McDermott, D., Ram, A., Veloso, M., Weld, D., Wilkins, D.: Pddl—the planning domain definition language (1998)Google Scholar
- 15.Mohan, P., Greer, J., McCalla, G.: Instructional planning with learning objects. In: Baumgartner, K.M., Cairns (eds.) n IJCAI 2003 Workshop Knowledge Representation and Automated Reasoning for E-Learning Systems (2003)Google Scholar
- 16.Piaget, J.: Language and thought of the child. Harcourt, New York (1926)Google Scholar
- 17.Weber, G., Brusilovsky, P.: Elm-art: An adaptive versatile system for web-based instruction. International Journal of AI in Education 12(4), 351–384 (2001)Google Scholar