A method for learning scenario determination and modification in intelligent tutoring systems
Computers have been employed in education for years. They help to provide educational aids using multimedia forms such as films, pictures, interactive tasks in the learning process, automated testing, etc. In this paper, a concept of an intelligent e-learning system will be proposed. The main purpose of this system is to teach effectively by providing an optimal learning path in each step of the educational process. The determination of a suitable learning path depends on the student's preferences, learning styles, personal features, interests and knowledge state. Therefore, the system has to collect information about the student, which is done during the registration process. A user is classified into a group of students who are similar to him/her. Using information about final successful scenarios of students who belong to the same class as the new student, the system determines an opening learning scenario. The opening learning scenario is the first learning scenario proposed to a student after registering in an intelligent e-learning system. After each lesson, the system tries to evaluate the student's knowledge. If the student has a problem with achieving an assumed score in a test, this means that the opening learning scenario is not adequate for this user. In our concept, for this case an intelligent e-learning system offers a modification of the opening learning scenario using data gathered during the functioning of the system and based on a Bayesian network. In this paper, an algorithm of scenario determination (named ADOLS) and a procedure for modifying the learning scenario AMLS with auxiliary definitions are presented. Preliminary results of an experiment conducted in a prototype of the described system are also described.
- Bouzeghoub, A. Defude, B., Ammour, S., Duitama, J.F. and Lecocq, C.(2004). A RDF description model for manipulating learning objects, Proceedings of the International Conference on Advanced Learning Technologies, Joensuu, Finland, pp. 81-85.
- Brusilovsky, P. (1999). Adaptive and intelligent technologies for web-based education, Kunstliche Intelligenz 13(4): 19-25.
- Brusilovsky, P., Schwarz, E. and Weber, G. (1996). ELM-ART: An intelligent tutoring system on World Wide Web, in C. Frasson, G. Gauthier, A. Lesgold (Eds.), Intelligent Tutoring Systems, Springer-Verlag, Berlin, pp. 261-269.
- Essalmi, F., Jemni, L. and Ayed, L. (2007). A process for the generation of personalized learning scenarios based on ontologies, Proceedings of International Conference on Information and Communication Technology and Accessibility, Hammamet, Tunisia, pp. 173-175.
- Gamboa, H. and Fred, A. (2001). Designing intelligent tutoring systems: A Bayesian approach, ICEIS 2001-International Conference on Enterprise Information Systems, Setubal, Portugal, pp. 452-458.
- Grigoriadou, M. , Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H. and Magoulas, G.D. (2002). INSPIRE: An intelligent system for personalized instruction in a remote environment, in S. Reich. M. Tzagarakis and P.M.E. De Bra (Eds.), Hypermedia: Openess, Structural Awareness, and Adaptivity, Lecture Notes in Computer Science, Vol. 2266, Springer-Verlag, Berlin/Heidelberg, pp. 215-225.
- Hewahi, N. (2007). Intelligent tutoring system: Hierarchical rule as a knowledge representation and adaptive pedagogical model, Information Technology Journal 6(5): 739-744.
- Kelly, D. and Tangney, B. (2002). Incorporating learning characteristics into an intelligent tutor, in S.A. Cerri, G. Gouarderes and F. Paraguaçu (Eds.), Intelligent Tutoring Systems, Lecture Notes in Computer Science, Vol. 2363, Springer-Verlag, Berlin/Heidelberg, pp. 729-738.
- Kelly, D. and Tangney, B. (2004). Predicting learning characteristics in a multiple intelligence based tutoring system, in J.C. Lester, R.M. Vicari and F. Paraguaçu (Eds.), Intelligent Tutoring Systems, Springer-Verlag, Berlin, pp. 9-30.
- Klaus-Dieter, S., Thalheim B., Binemann-Zdanowicz A., Kaschek R., Kuss T. and Tschiedel B. (2005). A conceptual view of web-based e-learning systems, Education and Information Technologies 10(1): 83-110.
- Kobsa, A., Koenemann, J. and Pohl, W. (2001). Personalized hypermedia presentation techniques for improving online customer relationships, Knowledge Engineering Review 16(2): 111-155.
- Kozierkiewicz, A. (2008a). Content and structure of learner profile in an intelligent e-learning system, in N.T. Nguyen, G. Kolaczek and B. Gabrys (Eds.), Knowledge Processing and Reasoning for Information Society, Exit, Warsaw, pp. 101-116.
- Kozierkiewicz, A. (2008b). Determination of opening learning scenarios in intelligent tutoring systems, in A. Zgrzywa, K. Choroś and A. Siemiński (Eds.), New Trend in Multimedia and Network Information Systems, IOS Press, Amsterdam, pp. 204-213.
- Kozierkiewicz-Hetmańska, A. (2009). A conception for modification of learning scenario in an intelligent e-learning system, in N.T. Nguyen, R. Kowalczyk and S.-M. Chen (Eds.), Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems, Lecture Notes in Artificial Intelligence, Vol. 5796, Springer-Verlag, Berlin/Heidelberg, pp. 87-96.
- Kozierkiewicz-Hetmańska, A. and Nguyen, N.T. (2010). A method for scenario modification in intelligent e-learning systems using graph-based structure of knowledge, in N.T. Nguyen, R. Katarzyniak, Ch. Shyi-Ming (Eds.), Advances in Intelligent Information and Database Systems, Studies in Computational Intelligence, Vol. 283 Springer-Verlag, Berlin, pp. 169-179.
- Kukla, E. E. Nguyen, N.T., Danilowicz, C., Sobecki, J. and Lenar M. (2004). A model conception for optimal scenario determination in an intelligent learning system, ITSE - International Journal of Interactive Technology and Smart Education 1(3): 171-184.
- Nguyen, N.T. (2002). Consensus systems for conflict solving in distributed systems, Information Sciences 147(1): 91-122.
- Nguyen, N.T. (2008). Advanced Methods for Inconsistent Knowledge Management, Springer-Verlag, New York, NY.
- Nguyen, V.A., Nguyen, V.H. and Ho, S.D. (2008). Constructing a Bayesian belief network to generate learning path in adaptive hypermedia system, Journal of Computer Science and Cybermetics 24(11): 12-19.
- Popescu, E., Badica, C. and Trigano, P. (2008). Learning objects' architecture and indexing in WELSA adaptive educational system, Scalable Computing: Practice and Experience 9(1): 11-20.
- Rius, A., Sicilia, M. and Garsia-Barriocanal, E. (2008). An ontology to automate learning scenarios? An approach to its knowledge domain, Interdisciplinary Journal of ELearning and Learning Objects 4(1): 151-165.
- Soloman, B.A. and Felder, R. (2010). Index of Learning Styles Questionnaire, http://www.engr.ncsu.edu/learningstyles/ilsweb.html.
- Stankov, S., Glavinic, V. and Rosic, M. (2000). On knowledge representation in an intelligent tutoring system, Proceedings of INES'2000-International Conference on Intelligent Engineering Systems, Portoroz, Slovenija, pp. 381-384.
- Weber, G. and Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for web-based instruction, International Journal of Artificial Intelligence in Education 6(5): 351-384.