A comparison of machine learning techniques in building an intelligent tutoring system

Mingqian Chen

doi:10.54254/2755-2721/5/20230673

Research Article

Open access

Published on 31 May 2023

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Chen,M. (2023). A comparison of machine learning techniques in building an intelligent tutoring system. Applied and Computational Engineering,5,662-666.

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A comparison of machine learning techniques in building an intelligent tutoring system

Mingqian Chen *^,1,

¹ College of Software Engineering, Sichuan University, Chengdu, Sichuan, 610207, China

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/5/20230673

Abstract

An Intelligent Teaching System (ITS) integrates Artificial Intelligence (AI) to the field of education, in order to dynamically adapts to users with different background and provide optimal teaching methods. Recent advancements in intelligent tutoring have proved its effectiveness in enhancing the achievement and abilities of learners. At the same time, with the rapid development of AI technology, various AI and machine learning algorithms have been applied to the design of ITS, optimizing their performance to varying degrees. This paper provides an overall review of previous ITS research using various techniques of artificial intelligence and machine learning (ML) and provides an overview of ITS and its architecture. In addition, it discusses and summarizes current research efforts and barriers to ITS using AI, as well as some future opportunities. This paper provides an overall comparison of various machine learning techniques that have previously been applied to ITS and an overview of ITS and its architecture. In addition, it discusses and summarizes the current barriers to ITS using AI, as well as an expectation of its future development.

Keywords

Machine learning, Education, Intelligent Tutoring System

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References

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Cite this article

Chen,M. (2023). A comparison of machine learning techniques in building an intelligent tutoring system. Applied and Computational Engineering,5,662-666.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.5

ISSN：2755-2721(Print) / 2755-273X(Online)

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