Electronic learning- virtual
N. Saberi; Gh.A. Montazer
Abstract
Environmental monitoring for extraction of learner’s features and accurately learner modeling is a time and cost consuming task. This paper proposes a flexible framework using agent architecture capabilities in learners’ monitoring and also in optimization of Intelligent Tutoring subsystems ...
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Environmental monitoring for extraction of learner’s features and accurately learner modeling is a time and cost consuming task. This paper proposes a flexible framework using agent architecture capabilities in learners’ monitoring and also in optimization of Intelligent Tutoring subsystems interactions. In this research learner’s learning approaches have been recognized by learning style theory then their uncertainty has been reduced by Bayesian Network. This framework prepares recommendations by combination of learners’ learning style and learner’s abilities (that are computed by Item Response Theory (IRT)) for three groups: learners, tutors and system designer. The architecture of proposed tutoring system has been presented in three layers. In middle layer there are four agents that monitor learners, create learner’s model, update it and prepare some recommendations based on courseware features, learner’s abilities and his/her learning style. A study was conducted on 30 Computer Engineering students during one semester. The implementation of the proposed system on participants indicates an increase in all evaluation factors, for example it doubles educational success rate and learner’s satisfaction rate.
