Abstract

In this paper, we present SEMAG - a novel semantic-agent learning recommendation mechanism which utilizes the advantages of instructional Semantic Web rules and multi-agent technology, in order to build a competitive and interactive learning environment. Specifically, the recommendation-making process is contingent upon chapter-quiz results, as usual; but it also checks the students' understanding at topic-levels, through personalized questions generated instantly and dynamically by a knowledge-based algorithm. The learning space is spread to the social network, with the aim of increasing the interaction between students and the intelligent tutoring system. A field experiment was conducted in which the results indicated that the experimental group gained significant achievements, and thus it supports the use of SEMAG.

Keywords

MSC

68Q55, 68T30, 68U35

Cite this article

@article{cai-2950,
  author  = {Cuong Dinh Hoa Nguyen and Ngamnij Arch-int and Somjit Arch-int},
  title   = {SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction},
  journal = {Computing and Informatics},
  volume  = {36},
  number  = {6},
  pages   = {1312--1334},
  year    = {2017},
  issn    = {1335-9150},
  doi     = {10.4149/cai_2017_6_1312}
}
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