Abstract
The use of one-size-fits-all approach is getting replaced by the adaptive, personalized perspective in recently developed learning environments. This study takes a look at the need of personalization in e-learning systems and the adaptivity and distribution features of adaptive distributed learning environments. By focusing on how personalization can be achieved in e-learning systems, the technologies used for establishing adaptive learning environments are explained and evaluated briefly. Some of these technologies are web services, multi-agent systems, semantic web and AI techniques such as case-based reasoning, neural networks and Bayesian networks used in intelligent tutoring systems. Finally, by discussing some of the adaptive distributed learning systems, an overall state of the art of the field is given with some future trends.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kay, J.: Learner control. User Modeling and User-Adapted Interaction 11, 111–127 (2001)
Gallis, H., Kasbo, J.P., Herstad, J.: The multidevice paradigm in know-mobile - Does one size fit all? In: Bjørnestad, S., Moe, R.E., Mørch, A.I., Opdahl, A.L. (eds.) Proceedings of the 24th Information System Research Seminar in Scandinavia, pp. 491–504 (2001)
Brusilovsky, P., Vassileva, J.: Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Lifelong Learning 13(1,2), 75–94 (2003)
Wang, H., Holt, P.: The design of an integrated course delivery system for Web-based distance education. In: Proceedings of the IASTED International Conference on Computers and Advanced Technology in Education (CATE 2002), pp. 122–126 (2002)
Wenger, E.: Artificial intelligence and tutoring systems. In: Computational and cognitive approaches to the communication of knowledge, pp. 13–25. Morgan Kaufmann, Los Altos (1987)
Vassileva, J.: A new approach to authoring of Adaptive Courseware for Engineering domains. In: Proceedings of the International Conference on Computer Assisted Learning in Science and Engineering (CALISCE 1994), pp. 241–248 (1994)
Specht, M., Oppermann, R.: ACE-Adaptive Courseware Environment. The New Review of Hypermedia and Multimedia 4, 141–161 (1998)
Vouk, M.A., Bitzer, D.L., Klevans, R.L.: Workflow and end-user quality of service issues in Web-based education. IEEE Trans. on Knowledge and Data Engineering 11(4), 673–687 (1999)
Lin, F.O.: Designing Distributed Learning Environments with Intelligent Software Agents. Information Science Publishing (2004)
Dale, J., Ceccaroni, L., Zou, Y., Agam, A.: Implementing agent-based Web services, challenges in open agent systems. In: Autonomous Agents and Multi-Agent Systems Conference in Melbourne, Australia, July 14–17 (2003)
Huhns, M.N.: Agents as Web Services. IEEE Internet Computing, 93–95 (July/August 2002)
O’Hare, G.M.P., Jennings, N.R.: Foundations of distributed artificial intelligence. John Wiley & Sons, New York (1996)
Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Scientific American, 34–43 (2001)
Jafari, A.: Conceptualizing intelligent agents for teaching and learning. Educause Quarterly (3), 28–34 (2002)
IEEE LTSC, http://ltsc.ieee.org
Gang, Z., ZongKai, Y., Kun, Y.: Design and implementation of a distributed learning resource registry system. In: The Fourth International Conference on Computer and Information Technology CIT 2004, pp. 333–338 (2004)
Blackmon, W.H., Rehak, D.R.: Customized learning: A Web services approach. In: Proceedings: Ed-Media (2003)
Ong, J., Ramachandran, S.: Intelligent tutoring systems: The what and the how, http://www.learningcircuits.org/feb2000/ong.html
Thomas, E.: Intelligent tutoring systems, http://coe.sdsu.edu/eet/Articles/tutoringsystem/start.htm
Maia, R.F., Netto, M.L.: Work in Progress - A Distributed Approach to a Learning Management System using Multi-Agent Technology. In: Frontiers in Education (2005)
Wiley, D.A.: Connecting learning objects to instructional design theory. In: Wiley, D.A. (ed.) A definition, a metaphore and a taxonomy (2000)
Garruzzo, S., Rosaci, D., Sarne, G.M.L.: ISABEL: A Multi Agent e-Learning System That Supports Multiple Devices. In: IEEE/WIC/ACM International Conference on Intelligent Agent Technology, pp. 85–88 (2007)
Gladun, A., Rogushina, J., Garcıa-Sanchez, F., Martínez-Béjar, R., Fernández-Breis, J.T.: An application of intelligent techniques and semantic web technologies in e-learning environments. Expert Syst. Appl. 36(2), 1922–1931 (2009)
Gaeta, M., Orciuoli, F., Ritrovato, P.: Advanced ontology management system for personalised e-Learning. Know-Based Syst. 22(4), 292–301 (2009)
Aslan, B.G., Inceoglu, M.M.: Machine Learning Based Learner Modeling for Adaptive Web-Based Learning. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part I. LNCS, vol. 4705, pp. 1133–1145. Springer, Heidelberg (2007)
Beckstein, C., Denzler, J., Fothe, M., König-Ries, B., Sack, H., Vogel, J.: A Reactive Architecture for Ambient E-Learning. In: Proc. of Towards Ambient Intelligence: Methods for Cooperating Ensembles in Ubiquitous Environments (2007)
Shadbolt, N.: Ambient intelligence. IEEE Intelligent Systems 18(4), 2–3 (2003)
Paraskakis, I.: Ambient learning: a new paradigm for e-learning. In: Proc. 3rd Int. Conf. on multimedia and Information & Communication Technologies in Education (m-ICTE 2005), Recent Research developments in Learning Technologies, Caceres, Spain (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ciloglugil, B., Inceoglu, M.M. (2010). Exploring the State of the Art in Adaptive Distributed Learning Environments. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12165-4_44
Download citation
DOI: https://doi.org/10.1007/978-3-642-12165-4_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12164-7
Online ISBN: 978-3-642-12165-4
eBook Packages: Computer ScienceComputer Science (R0)