Intelligent agent-assisted decision support for personalized virtual learning environment

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Intelligent agent-assisted decision support for personalized virtual learning environment

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Abstract

In the delivery of online learning, Virtual Learning Environments (VLEs) have been growing quickly and research on intelligent agent supported eLearning systems has developed rapidly in the last decade. However, some online learning programs have not been successful, and one of the main reasons could be that those online learning programs supported by VLEs have not fully considered learners' differences. VLEs developed under constructivism and embedded personalization learning functions have a potential to meet different requirements of different learners. In order to provide decision support for personalization decisions in VLEs, we formulate a conceptual model for personalization by following Simon's decision-making process model. Based on this model, in order for a more adaptive, intelligent and flexible solution for personalized virtual learning environment (PVLE), the intelligent agent technology is applied in this research. Intelligent agent technologies with features such as autonomy, pre-activity, pro-activity and co-operativity, facilitate the interaction between students and the systems. By applying intelligent agents in PVLEs, individual learners can be uniquely identified, with content specifically presented for them, and progress can be individually monitored, supported, and assessed. Several types of agents are proposed and a novel and open multi-agent architecture is presented for PVLE. A prototype system for PVLE is also developed to demonstrate the advances of the proposed system architecture.

Abstract
Introduction
Background
1. Personalized e-learning
2. Herbert A. Simon's model of the decision-making process
3. Intelligent agent-assisted decision support systems
Decision-making process model for personalized virtual learning environment
Design and development of intelligent personalized virtual learning environment
1. System design architecture
2. System operation
Conclusions
References

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[...] Gao, S.J., Wang, H.Q., Xu, D.M., and Wang, Y.F., intelligent agent-assisted decision support system for family financial planning”, Decision Support Systems, in press [12]. Hiltz, S.R., and Turoff, M., “What Makes Learning Networks Effective”, Communications of the ACM, Vol No pp. 56-59. [13]. Jennings, N.R., agent-based software engineering”, Artificial Intelligence, Vol pp. 227-296. [14]. Jennings, N.R., and Wooldridge, M., Agent technology: foundations, applications, and markets, Berlin, New York, Springer-Verlag [15]. Jennings, N.R., Faratin, P., Norman, T.J., O'Brien, P., and Odgers, B., “Autonomous agents for business process management”, International Journal of Applied Artificial Intelligence, Vol No pp. [...]

[...] In this study, we proposed a decision-making process model for personalization in VLEs by applying Simon's classical model of a decision process. Based on this conceptual model, a novel and open multi-agent-based PVLE is designed and implemented, in which various classes of intelligent agents are proposed to provide a set of functionalities for PVLE. In sum, the main contribution of this study to the research literature can be summarized as follows: The personalization decision-making process model of VLEs: This is a conceptual model that identifies the specific activities involved in each decision-making phase for personalization in VLEs. [...]

[...] Furthermore, we propose a PVLE whose design and implementation architectures are organized by the phases of Simon's model Intelligent Agent-Assisted Decision Support Systems 3 The development of intelligent agents (IAs) and multi-agent systems (MASs) has recently gained popularity among IS researchers [15]. Although there is no universally accepted definition of the term and indeed there is a good deal of ongoing debate and controversy on this very subject, the central point of agents is that they are autonomous: capable of acting independently, exhibiting control over their internal state. [...]

[...] [27]. Wang, H.Q., Liao, S., and Liao, L., “Modeling constraint-based negotiating agents”, Decision Support Systems, Vol No pp. 201-217. [28]. Whinston, A., “Intelligent agents as a basis for decision support systems”, Decision Support Systems, Vol No pp [29]. Wilson, B.G., Constructivist Learning Environments: Case Studies in Instructional Design, Educational Technology Publications, Englewood Cliffs, NJ [30]. Wooldridge, M., and Jennings, N.R., “Intelligent agents: theory and practice”, Knowledge Engineering Review, Vol No pp. 115-152. [31]. Wooldridge, M., “Intelligent agents”, in: G. Weiss, [...]

[...] Therefore, PVLEs can support individual learning styles and characteristics, provide personalized features for each individual online learner who has experience with hypothesizing and predicting, manipulating objects, posing questions, researching answers, imaging, and investigating, in order for knowledge construction to occur. Therefore, PVLEs can be viewed as learner-centred, two-way interactive and active learning process of knowledge construction [16]. The personalization process is viewed as consisting of three main stages: learning, matching, and evaluation. The learning process starts with the collection of data from learners. [...]

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