Abstract
In this paper we present a new framework for predicting the proper instructional strategy for a given teaching material based on its attributes. The framework is domain-based in the sense that it is based on the qualitative observation of the teaching materials’ attributes stored by the system. The prediction process is based on a machine learning approach using feed forward artificial neural network to generate a model that both fit the input data attributes and predict the proper instructional strategy by extracting knowledge implicit in these attributes. The framework was adapted in an Intelligent Tutoring System (ITS) to teach Modern Standard Arabic language to adult English-speaking learners with no pre-knowledge of Arabic language is required. The learning process will be through the Internet since the online education is better suited to mature individuals who are self-motivated and have a good sense of purpose.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fausett, L., (1994). “Fundamentals of Neural Networks. Architectures Algorithm and Applications”, Prentice Hall, Englewood Cliffs, NJ, USA.
Gardner, H., (2006), “Multiple Intelligences: New Horizons in Theory and Practice”, Basic books, USA.
Koedinger, K., Anderson, J., Hadley, W., Mark, M., (1997), “Intelligent tutoring goes to school in the big city”, Journal of Artificial intelligence in Education, 8(1), pp. 30-43.
Haykin, Simon, (1994), “Neural networks a comprehensive foundation”, Macmillan publishing company, USA.
Mitchell, T. (1997), “Machine Learning”, New York: McGraw-Hill.
Rao, V. B. and Rao, H. V. (1995). “Neural Networks and Fuzzy logic”. MIS Press, New York, USA.
Rumelhart, D., and McClelland, J. (1986). “Parallel Distributed Processing”. M.I.T. press. Cambridge, MA.
Sevarac, Z, (2006), “Neuro Fuzzy Reasoner for student modelling”, Proceeding of the sixth international conference on advance learning technologies (ICALT’06).
Smith, P., and Ragan, T. (1999), “Instructional design”, second edition, John Wiley & sons, Inc, NY, USA.
Stathacopoulou, R., Samarakou, M., R., and Magoulas, G. (2004), “A Neuro-Fuzzy Approach to Detect Student’s Motivation”, ICALT.
Stathacopoulou, R., Grigoriadou, M., Samarakou, M., and Mitropoulos, D., (2007), “Monitoring students’ actions and using teachers’ expertise in implementing and evaluating the neural network-based fuzzy diagnostic model”, Expert Syst. Appl. 32(4).
United Kingdom Department for Education and Skills, (2005), “Working together: coaching and assessment for learning”, Crown copyright 2005, UK.
Veermans, K., and Joolingen, W. (2004), “Combining heuristics and formal methods in a tool for supporting simulation-based discovery learning”, Proceedings of the Workshop on Intelligent Tutoring Systems for Ill-Defined Domains, pages 217–226. Jhongli, Taiwan.
Wang, T., and Mitovic, A. (2002), “Using neural networks to predict students performance”, In Proceeding of the international conference on computer education (ICCE’02), IEEE.
Witten, I., and Frank, E. (2000), “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”, San Diego, CA: Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media B.V.
About this paper
Cite this paper
Kseibat, D., Mansour, A., Adjei, O. (2010). Domain-Based Intelligent Tutoring System. In: Sobh, T. (eds) Innovations and Advances in Computer Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3658-2_3
Download citation
DOI: https://doi.org/10.1007/978-90-481-3658-2_3
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-3657-5
Online ISBN: 978-90-481-3658-2
eBook Packages: EngineeringEngineering (R0)