The Architecture of a Learner Adviser Service

Dumitru Dan Burdescu, Marian Cristian Mihaescu, Costel Marian Ionaşcu


One of the most important challenges faced by institutions that deploy e-Learning activities is to prove beneficiaries that the learning process is effective. This paper proposes a structure for a Service Oriented Architecture (SOA) in which a Learner Adviser Service (LAS) will run. The proposed architecture enables different e-Learning platforms to access the service through published and discoverable interfaces. The LAS will provide feedback for each learner according with the setup that has been done between the e-Learning platform and LAS. The feedback refers to actions that are recommended to be performed by the learner. LAS uses machine learning algorithms for classifying learners according with their performed activities. The ultimate goal of LAS is to provide an overall activity measurement for the student’s activity in such a way to increase the trust into the effectiveness of the e-Learning platform.


  1. Jini Network Technology.
  2. JiniTM Technology Starter Kit v2.0 API
  3. D. Tidwell. Web Services - The Web's next revelotion. developerworks/education/wsbasics/wsbasic%s-ltr.pdf.
  4. Web Services Architecture Requirements, October 2002.
  5. [3] Extensible Markup Language (XML) 1.0, October 2000.
  6. Universal Description, Discovery and Integration (UDDI), 2001.
  7. Web Services Description Language (WSDL) 1.1, March 2001.
  8. Simple Object Access Protocol (SOAP) 1.1, 200.
  9. [10]Business Process Execution Language for Web Services (BPEL), 2002.
  10. Web Services Transaction, August 2002. /ws-transpec/
  11. Common Object Request Broker Architecture, 2002.
  12. OSF Distributed Computing Environment, 2002.
  13. Manchester 1999Philip, Manchester, “Survey - Knowledge Management” Financial Times, 8 April,1999.
  14. Olivia Parr Rud, Data Mining Cookbook - Modeling Data for Marketing, Risk, and Customer Relationship Management (Wiley Computer Publishing, 2001).
  15. I. H. Witten, E. Frank, Data Mining - Practical Machine Learning Tools and Techniques with Java Implementations (Morgan Kaufmann Publishers, 2000).
  16. R. Agrawal and R. Srikant, Fast algorithms for mining association rules. Proc. of the 20th VLDB Conference, Santiago, Chile, 1994, pp. 487-499.
  17. R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.
  18. Jiawei Han, Micheline Kamber, Data Mining - Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
  19. Weka,
  20. Garner S.R., Cunningham S.J., Holmes G., Nevill-Manning C.G. and Witten I.H., "Applying a Machine Learning Workbench: Experience with Agricultural Databases," Proc Machine Learning in Practice Workshop, Machine Learning Conference, Tahoe City, CA, USA, pp. 14-21., 1995.
  21. Holmes, G., Donkin, A., and Witten, I.H., “Weka: a machine learning workbench.” Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia, pp. 357- 361, 1994.

Paper Citation

in Harvard Style

Burdescu D., Mihaescu M. and Ionaşcu C. (2009). The Architecture of a Learner Adviser Service . In Proceedings of the 3rd International Workshop on Enterprise Systems and Technology - Volume 1: I-WEST, ISBN 978-989-674-015-3, pages 57-70. DOI: 10.5220/0004463200570070

in Bibtex Style

author={Dumitru Dan Burdescu and Marian Cristian Mihaescu and Costel Marian Ionaşcu},
title={The Architecture of a Learner Adviser Service},
booktitle={Proceedings of the 3rd International Workshop on Enterprise Systems and Technology - Volume 1: I-WEST,},

in EndNote Style

JO - Proceedings of the 3rd International Workshop on Enterprise Systems and Technology - Volume 1: I-WEST,
TI - The Architecture of a Learner Adviser Service
SN - 978-989-674-015-3
AU - Burdescu D.
AU - Mihaescu M.
AU - Ionaşcu C.
PY - 2009
SP - 57
EP - 70
DO - 10.5220/0004463200570070