International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

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 Volume 9, Issue 12 (December 2022), Pages: 162-169

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 Original Research Paper

The integration of psychology and artificial intelligence in e-learning systems to guide the learning path according to the learner's style and thinking

 Author(s): Mohammed Elhossiny 1, 2, Rania Eladly 2, Abdelnasser Saber 1, 3, *

 Affiliation(s):

 1Applied College, Northern Border University, Arar, Saudi Arabia
 2Faculty of Specific Education, Mansoura University, Mansoura, Egypt
 3Faculty of Computer and Information Sciences, Mansoura University, Mansoura, Egypt

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-8991-0518

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.12.020

 Abstract:

Traditional e-learning systems fall short in many respects when it comes to delivering content to learners in the most effective way. Research shows that e-learning systems are not accommodative of learners’ thinking and learning styles, which leads to poor performance. This paper proposes a way through which this problem can be addressed. The researcher believes that the technology of Artificial Intelligence can be integrated with the learning and thinking styles (Psychology) of learners in an e-learning system to provide an enriched learning experience. No attempts have been made so far to integrate Artificial intelligence and Psychology in an e-learning environment, making this paper unique. The paper explores this subject by designing a system that will be termed a “smart e-learning system.” The paper sought to propose Artificial Intelligence algorithms that will be applied to the learning and thinking styles of learners to come up with highly adaptive models for each student that enhances their learning experience. The significant difference in the performance of the control group and experimental group confirms that if psychology and AI are integrated, there is a significant improvement in the student learning experience in an e-learning system. This shows that Artificial Intelligence can work well with Psychology to enhance the learning experience in the e-learning environment.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Psychology, Artificial intelligence, Smart e-learning system

 Article History: Received 31 March 2022, Received in revised form 31 July 2022, Accepted 15 September 2022

 Acknowledgment 

The authors gratefully acknowledge the approval and support of this research study by the Grant no. SCI-2019-1-10-F-8216 from the Deanship of Scientific Research in Northern Border University, Arar, KSA.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Elhossiny M, Eladly R, and Saber A (2022). The integration of psychology and artificial intelligence in e-learning systems to guide the learning path according to the learner's style and thinking. International Journal of Advanced and Applied Sciences, 9(12): 162-169

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7

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 References (28)

  1. Alkhasawneh IM, Mrayyan MT, Docherty C, Alashram S, and Yousef HY (2008). Problem-based learning (PBL): Assessing students’ learning preferences using VARK. Nurse Education Today, 28(5): 572-579. https://doi.org/10.1016/j.nedt.2007.09.012   [Google Scholar] PMid:17983691
  2. Arkorful V and Abaidoo N (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12(1): 29-42.   [Google Scholar]
  3. ATD (2022). What is e-learning? Association for Talent Development, Alexandria, USA.   [Google Scholar]
  4. Benhamdi S, Babouri A, and Chiky R (2017). Personalized recommender system for e-learning environment. Education and Information Technologies, 22(4): 1455-1477. https://doi.org/10.1007/s10639-016-9504-y   [Google Scholar]
  5. Casino F, Dasaklis TK, and Patsakis C (2019). A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and Informatics, 36: 55-81. https://doi.org/10.1016/j.tele.2018.11.006   [Google Scholar]
  6. El-Sabagh HA and Yamani HA (2020). Attitudes of faculty members towards using learning management system "desire2learn" in learning. Journal of Educational Research and Reviews, 8(10): 179-191. https://doi.org/10.33495/jerr_v8i10.20.205   [Google Scholar]
  7. Fleming N and Baume D (2006). Learning styles again: VARKing up the right tree! Educational Developments, 7(4): 4-7. https://doi.org/10.1016/S1471-0846(06)70627-8   [Google Scholar]
  8. Hamada M and Hassan M (2017). An enhanced learning style index: Implementation and integration into an intelligent and adaptive e-learning system. Eurasia Journal of Mathematics, Science and Technology Education, 13(8): 4449-4470. https://doi.org/10.12973/eurasia.2017.00940a   [Google Scholar]
  9. Hinton PR (2014). Statistics explained. 3rd Edition, Routledge, London, UK. https://doi.org/10.4324/9781315797564   [Google Scholar]
  10. Isaias P, Reis F, Coutinho C, and Lencastre JA (2017). Empathic technologies for distance/mobile learning: An empirical research based on the unified theory of acceptance and use of technology (UTAUT). Interactive Technology and Smart Education, 14(2): 159-180. https://doi.org/10.1108/ITSE-02-2017-0014   [Google Scholar]
  11. Jaleel S and Thomas AM (2019). Learning styles: Theories and implications for teaching learning. Horizon Research Publishing, San Jose, USA.   [Google Scholar]
  12. Jarrahi MH (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4): 577-586. https://doi.org/10.1016/j.bushor.2018.03.007   [Google Scholar]
  13. Kolekar SV, Pai RM, and Pai MMM (2017). Prediction of learner's profile based on learning styles in adaptive e-learning system. International Journal of Emerging Technologies in Learning, 12(6): 31-51. https://doi.org/10.3991/ijet.v12i06.6579   [Google Scholar]
  14. Mitić V (2019). Benefits of artificial intelligence and machine learning in marketing. In the Sinteza 2019-International Scientific Conference on Information Technology and Data Related Research, Singidunum University, Belgrade, Serbia: 472-477.   [Google Scholar]
  15. Nielsen JL (2019). Educational designs supporting student engagement through problem-oriented project learning supplemented by processes within practices of networked learning: The Roskilde model as inspired by the pragmatist tradition. In the Conference Critical Edge Alliance 2019: Boundary Crossings in Culture, Power, and Experience, Re-imagining Higher Education, New York, USA.   [Google Scholar]
  16. Normadhi NBA, Shuib L, Nasir HNM, Bimba A, Idris N, and Balakrishnan V (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education, 130: 168-190. https://doi.org/10.1016/j.compedu.2018.11.005   [Google Scholar]
  17. Nuankaew P, Nuankaew W, Phanniphong K, Imwut S, and Bussaman S (2019). Students model in different learning styles of academic achievement at the University of Phayao, Thailand. International Journal of Emerging Technologies in Learning, 14(12): 133-157. https://doi.org/10.3991/ijet.v14i12.10352   [Google Scholar]
  18. Raven JC and Court JH (1998). Raven's progressive matrices and vocabulary scales (Vol. 759). Oxford Psychologists Press, Oxford, UK.   [Google Scholar]
  19. Rodrigues H, Almeida F, Figueiredo V, and Lopes SL (2019). Tracking e-learning through published papers: A systematic review. Computers and Education, 136: 87-98. https://doi.org/10.1016/j.compedu.2019.03.007   [Google Scholar]
  20. Signorelli CM (2018). Can computers become conscious and overcome humans? Frontiers in Robotics and AI, 5: 121. https://doi.org/10.3389/frobt.2018.00121   [Google Scholar] PMid:33501000 PMCid:PMC7805878
  21. Sutton RS and Barto AG (2018). Reinforcement learning: An introduction. MIT Press, Cambridge, USA.   [Google Scholar]
  22. Tawafak RM, AlSideir A, Alfarsi G, Al-Nuaimi MN, Malik SI, and Jabbar J (2019). E-learning vs. traditional learning for learners satisfaction. E-learning, 29(3): 388-397.   [Google Scholar]
  23. Tirziu AM and Vrabie C (2015). Education 2.0: E-learning methods. Procedia-Social and Behavioral Sciences, 186: 376-380. https://doi.org/10.1016/j.sbspro.2015.04.213   [Google Scholar]
  24. Willingham DT, Hughes EM, and Dobolyi DG (2015). The scientific status of learning styles theories. Teaching of Psychology, 42(3): 266-271. https://doi.org/10.1177/0098628315589505   [Google Scholar]
  25. Wongupparaj P, Sumich A, Wickens M, Kumari V, and Morris RG (2018). Individual differences in working memory and general intelligence indexed by P200 and P300: A latent variable model. Biological Psychology, 139: 96-105. https://doi.org/10.1016/j.biopsycho.2018.10.009   [Google Scholar] PMid:30392828
  26. Yassin BM and Almasri MA (2015). How to accommodate different learning styles in the same classroom: Analysis of theories and methods of learning styles. Canadian Social Science, 11(3): 26-33.   [Google Scholar]
  27. Zhang H (2017). Accommodating different learning styles in the teaching of economics: With emphasis on Fleming and Mills’s sensory-based learning style typology. Applied Economics and Finance, 4(1): 72-83. https://doi.org/10.11114/aef.v4i1.1921   [Google Scholar]
  28. Zhu HR, Zeng H, Zhang H, Zhang HY, Wan FJ, Guo HH, and Zhang CH (2018). The preferred learning styles utilizing VARK among nursing students with bachelor degrees and associate degrees in China. Acta Paulista de Enfermagem, 31: 162-169. https://doi.org/10.1590/1982-0194201800024   [Google Scholar]