An architecture for e‐learning system with computational intelligence

Author:

El Alami Marc,Casel Nicolas,Zampunieris Denis

Abstract

PurposeThe purpose of this paper is to introduce a new kind of learning management system: proactive LMS, designed to improve the users' online (inter)actions by providing programmable, automatic and continuous intelligent analyses of the users' behaviours, augmented with appropriate actions initiated by the LMS itself.Design/methodology/approachProactive systems adhere to two premises: working on behalf of, or pro, the user, and acting on their own initiative, without the user's explicit command. The proactive part of the LMS is implemented as a dynamic rules‐based system, and is added next to the initial LMS. They both use the same database as their source of information on the users, their activities, the available resources and the current state of the whole system.FindingsHow the proactive part of the LMS was implemented on the basis of a dynamic expert system is shown. Also how it looks like from a user's point of view is sketched. Finally, examples of intelligent analysis of users' behaviours coded into proactive rules are given.Research limitations/implicationsFuture work should include the design and the implementation of sets of rules (packages) dedicated to common users' needs, enabling useful proactivity on the basis of elaborated intelligent analysis.Originality/valueCurrent learning management systems (virtual educational and/or training online environments) are fundamentally limited tools. Indeed, they are only reactive software: these tools wait for an instruction and then react to the user's request. Students using these online systems could imagine and hope for more help and assistance tools: LMS should tend to offer some personal, immediate and appropriate support as teachers offer in classrooms. The proactive LMS can, for example, automatically and continuously help and take care of e‐learners with respect to previously defined procedures rules, and even flag other users, like e‐tutors, if something wrong is detected in their behaviour.

Publisher

Emerald

Subject

Library and Information Sciences,Computer Science Applications

Reference5 articles.

1. Brusilovsky, P. (2003), “A component‐based distributed architecture for adaptive web‐based education”, in Hoppe, U., Vardejo, F. and Kay, J. (Eds), Artificial Intelligence in Education: Shaping the Future of Learning through Intelligent Technologies, Proceedings of AI‐ED 2004, Sydney, Australia, July 20‐24, 2004, OIS Press, Amsterdam, pp. 386‐8.

2. Salovaara, A. and Oulasvirta, A. (2004), “Six modes of proactive resource management: a user‐centric typology for proactive behaviors”, Proceedings of the Nordic Conference on Human‐Computer Interaction, ACM International Conference Proceedings Series 82 (2004), pp. 57‐60.

3. Tennenhouse, D. (2000), “Proactive computing”, Communications of the ACM, Vol. 43 No. 5, pp. 43‐50.

4. Zampunieris, D. (2006a), “Implementation of a proactive learning management system”, in Reeves, Th.C. and Yamashita, Sh.F. (Eds), Proceedings of E‐Learn World Conference on E‐Learning in Corporate, Government, Healthcare, & Higher Education, Hawaii, USA, October 2006.

5. Zampunieris, D. (2006b), “Implementation of efficient proactive computing using lazy evaluation in a learning management system”, paper presented at m‐ICTE – International Conference on Multimedia and Information and Communication Technologies in Education, Seville, Spain, 2006.

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