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

Research Article
Open access

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    
  • *corresponding author chen-mingqian@foxmail.com
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

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

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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References

[1]. AlShaikh, F., and Hewahi, N 2021 Ai and machine learning techniques in the development of Intelligent Tutoring System: A review. In 2021 International Conference on innovation and Intelligence for informatics, computing, and technologies (3ICT), 403-410.

[2]. Fradkov, A. L 2020 Early history of machine learning. IFAC-PapersOnLine, 53, 1385-1390.

[3]. Koppisetty, R 2015 Application of Data Mining in Adaptive and Intelligent Tutoring Systems: A Review. International Journal of Computer Science and Information Technologies, 6, 2910-2917.

[4]. Anderson, J. R., Boyle, C. F., and Reiser, B. J 1985 Intelligent tutoring systems. Science, 228, 456-462.

[5]. Graesser, A. C., Conley, M. W., and Olney, A 2012 Intelligent tutoring systems. APA educational psychology handbook, 3, 451-473.

[6]. Burns, H. L., and Capps, C. G 2013 of Intelligent Tutoring Systems: An Introduction. Foundations of intelligent tutoring systems, 1-19.

[7]. Kohilavani, S., Mala, T., and Geetha, T. V 2009 Automatic tamil content generation. In 2009 International Conference on Intelligent Agent & Multi-Agent Systems,1-6.

[8]. Nkambou, R., Mizoguchi, R., and Bourdeau, J. (Eds.) 2010 Advances in intelligent tutoring systems, Springer Science & Business Media, Vol. 308.

[9]. Polson, M. C., and Richardson, J. J 2013 Foundations of intelligent tutoring systems. Psychology Press.

[10]. Almasri, A., Ahmed, A., Almasri, N., Abu Sultan, Y. S., Mahmoud, A. Y., Zaqout, I. S., ... and Abu-Naser, S. S 2019 Intelligent tutoring systems survey for the period, 2000-2018.

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

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. AlShaikh, F., and Hewahi, N 2021 Ai and machine learning techniques in the development of Intelligent Tutoring System: A review. In 2021 International Conference on innovation and Intelligence for informatics, computing, and technologies (3ICT), 403-410.

[2]. Fradkov, A. L 2020 Early history of machine learning. IFAC-PapersOnLine, 53, 1385-1390.

[3]. Koppisetty, R 2015 Application of Data Mining in Adaptive and Intelligent Tutoring Systems: A Review. International Journal of Computer Science and Information Technologies, 6, 2910-2917.

[4]. Anderson, J. R., Boyle, C. F., and Reiser, B. J 1985 Intelligent tutoring systems. Science, 228, 456-462.

[5]. Graesser, A. C., Conley, M. W., and Olney, A 2012 Intelligent tutoring systems. APA educational psychology handbook, 3, 451-473.

[6]. Burns, H. L., and Capps, C. G 2013 of Intelligent Tutoring Systems: An Introduction. Foundations of intelligent tutoring systems, 1-19.

[7]. Kohilavani, S., Mala, T., and Geetha, T. V 2009 Automatic tamil content generation. In 2009 International Conference on Intelligent Agent & Multi-Agent Systems,1-6.

[8]. Nkambou, R., Mizoguchi, R., and Bourdeau, J. (Eds.) 2010 Advances in intelligent tutoring systems, Springer Science & Business Media, Vol. 308.

[9]. Polson, M. C., and Richardson, J. J 2013 Foundations of intelligent tutoring systems. Psychology Press.

[10]. Almasri, A., Ahmed, A., Almasri, N., Abu Sultan, Y. S., Mahmoud, A. Y., Zaqout, I. S., ... and Abu-Naser, S. S 2019 Intelligent tutoring systems survey for the period, 2000-2018.

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