Adaptive Tutoring System With Application of Intelligent Agents

Adaptive Tutoring System With Application of Intelligent Agents

Jaroslav Meleško (Vilnius Gediminas Technical University, Vilnius, Lithuania) and Eugenijus Kurilovas (Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania & Vilnius Gediminas Technical University, Vilnius, Lithuania)
Copyright: © 2018 |Pages: 11
DOI: 10.4018/IJSEUS.2018040101
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In this article, the authors suggest a methodology to adapt learning units to the needs and talents of individual students using an intelligent learning system. Learning personalisation is done based on several factors. Felder and Silverman Learning Styles model is used to create student's profile with conjunction of data mining technologies and previously recorded behaviour of the student. Firstly, the authors perform systematic review of application of intelligent software agents in teaching throughout Clarivate Analytics Web of Science database. Secondly, they present methodologies to personalise learning by means of intelligent technologies. They analyse preferences of students according to Soloman-Felder Learning Styles questionnaire. The resulting model of a student is used in the creation of a personalised learning unit. The model of an adaptive intelligent teaching system based on application of aforementioned technologies is presented in more detail.
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2. Systematic Review

In order to identify the latest results in application of intelligent multi-agent systems in education, basic systematic literature review method has been used (Kitchenham, 2004). The intention of the scientific review was to answer the question: “What are the latest contributions to application of intelligent agents in education?” Systematic literature review was performed on 25 September 2016 in Clarivate Analytics (former Thomson Reuters) Web of Science database. Search history can be seen in Figure 1. In the last two years (2014-2016), thirty-four Articles were published on the topic “intelligent multi-agent* system AND learning”. The main factor for choosing papers for the review from the search results was their relevance to education, as multi-agent systems have a variety of applications. After applying systematic review methodology, on the last stage, ten suitable papers were identified for further analysis of the topic.

Figure 1.

Search history in Thomson Reuters Web of Science database


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