SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction

Rok, strany: 2017, 1312 - 1334

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.

ISO 690:

Nguyen, C., Arch-Int, N., Arch-Int, S. 2017. SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction. In Computing and Informatics, vol. 36, no.6, pp. 1312-1334. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_6_1312

APA:

Nguyen, C., Arch-Int, N., Arch-Int, S. (2017). SEMAG: A Novel Semantic-Agent Learning Recommendation Mechanism for Enhancing Learner-System Interaction. Computing and Informatics, 36(6), 1312-1334. 1335-9150. DOI: https://doi.org/10.4149/cai_2017_6_1312