SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction
Keywords:Intelligent tutoring system, multi-agent system, personalized learning recommendation, instructional semantic web rules
AbstractIn 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.
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How to Cite
Nguyen, C. D. H., Arch-int, N., & Arch-int, S. (2018). SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction. COMPUTING AND INFORMATICS, 36(6), 1312–1334. Retrieved from https://www.cai.sk/ojs/index.php/cai/article/view/2017_6_1312