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

Authors

  • Cuong Dinh Hoa Nguyen Department of Computer Science, Faculty of Science, Khon Kaen University
  • Ngamnij Arch-int Department of Computer Science, Faculty of Science, Khon Kaen University
  • Somjit Arch-int Department of Computer Science, Faculty of Science, Khon Kaen University

Keywords:

Intelligent tutoring system, multi-agent system, personalized learning recommendation, instructional semantic web rules

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.

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Author Biographies

Cuong Dinh Hoa Nguyen, Department of Computer Science, Faculty of Science, Khon Kaen University

Cuong Dinh Hoa NGUYEN received PhD degree of Computer Science in 2016 from Khon Kaen University, Thailand. His research interests include e-Learning, intelligent tutoring systems, Semantic Web technologies, data mining and recommender systems. Now he is a lecturer at Department of Business Information Systems, College of Economics, Hue University, Viet Nam.

Ngamnij Arch-int, Department of Computer Science, Faculty of Science, Khon Kaen University

Ngamnij Arch-int received the PhD degree in computer science from Chulalongkorn University, Thailand in 2003. She is currently an assistant professor in the Department of Computer Science at Khon Kaen University, Thailand. Her research interests include the semantic web, web services, semantic web services, and heterogeneous information integration. Contact her at ngamnij@kku.ac.th.

Somjit Arch-int, Department of Computer Science, Faculty of Science, Khon Kaen University

Somjit Arch-int received the PhD degree in computer science from the Asian Institute of Technology, Thailand in 2002. He is currently an associate professor in the Department of Computer Science, Khon Kaen University, Thailand. His previous experiences include the development of several industry systems and consulting activities. His research interests are business component-based software development, object-oriented metrics, ERP/Logistics and supply chain, traceability system and semantic Web. He is a member of IEEE Computer Society.

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Published

2018-02-09

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 http://147.213.75.17/ojs/index.php/cai/article/view/2017_6_1312