Technology of Education Journal (TEJ)

Quarterly Publication

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Authorship

Aims and Scope

Editorial Staff

Related Links

FAQ

News

Regulations

Publication fees

Paper Preparation Guide

Forms

Peer Review Policy

Reviewers

Publons Membership and Review Guide

The Effect of Personalized Education Based on Learning Styles on Learners' Cognitive Load in E- Learning

Articles in Press

Document Type : Original Research Paper

Authors

1 Department of Artificial Intelligence, Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

2 Center for Cognitive Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran 14588-89694, Iran

Abstract

Background and Objectives: Learning has consistently been one of the aspects of human development since the beginning of human existence on the Earth, encompassing all aspects of human life and holding a special place in human life plans. On the other hand, technological advancements in recent decades have rapidly brought about significant changes in the realm of education and learning. One of the most prominent impacts of technological progress in the field of learning is the emergence of e-learning; a tool that enables learners to access educational resources at any time and place. Regarding the fact that every person has individual traits, preferences, and tendencies, traditional education's "one-size-fits-all" approach can be seen as one of its fundamental flaws. Learning styles, as one of the most important factors influencing learning, represent an individual's preferences and ways of processing and understanding information. Therefore, providing adaptive education based on learners' learning styles, with the aim of enhancing educational efficiency and reducing cognitive load during teaching, is both essential and inevitable. This research aimed to investigate the impact of adaptive education based on learners' learning styles. In order to achieve this, learners' learning styles were identified using an online platform and the Felder-Silverman Learning Style Indicator questionnaire, and educational content was automatically generated and presented to learners accordingly. Finally, the performance of the learners and cognitive load during instruction were examined.
Methods: A total number of 37 male and female undergraduate computer science students with an average age of 20.3, participated in this study. Initially, the participants were divided into two groups, and their learning styles were determined using the Felder-Silverman Learning Style Indicator questionnaire. Subsequently, one group received educational content tailored to their learning styles, while the other group received content not aligned with their learning styles. After studying the provided material, the cognitive load and learning outcomes of the participants were assessed using the NASA Task Load Index questionnaire and a designed performance test, respectively. Finally, the significance level of the results obtained from the two groups was evaluated using an independent t-test.
Findings: Based on the obtained results, no significant difference was observed in the test scores of the two groups' performance. However, when comparing the cognitive load between the two groups, the average cognitive load of the group that received content aligned with their learning style was significantly higher than the group that received incongruent content, with a value of 0.02 (p < 0.05).
Conclusion: According to the research findings, providing educational content based on learners' learning styles significantly reduce cognitive load during learning. Therefore, offering personalized education based on learning styles plays a crucial role as one of the adaptive teaching methods in e-learning, enhancing performance, and reducing cognitive load for learners.

Keywords

Main Subjects

COPYRIGHTS 
© 2024 The Author(s).  This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)  

References
[1] Moore MG, Kearsley G. Distance Education: A Systems View of Online Learning: Cengage Learning; 2011.
[2] Qian Z, Jie L, Guangquan Z. Recommender Systems in E-learning. Recommender Systems in E-learning. 2021;1(2):76-89.
[3] Moatari M, pazouki E, Ebrahimpour R. Intelligent learners' cognitive style detection based on their interaction in the English language teaching system. Technology of Education Journal (TEJ). 2022;17(1):209-32. [In Persian]
[4] Minn S. AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence. 2022;3:100050.
[5] Essalmi F, Ayed LJB, Jemni M, Graf S. A fully personalization strategy of E-learning scenarios. Computers in Human Behavior. 2010;26(4):581-91.
[6] Huang M-J, Huang H-S, Chen M-Y. Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with applications. 2007;33(3):551-64.
[7] Othmane Z, DEROUICH A, TALBI A. A comparative study of the Most influential learning styles used in adaptive educational environments. International Journal of Advanced Computer Science and Applications. 2019;10(11).
[8] Amit K, Ninni S, Jyothi AN. Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001. and 2016. International Journal of Cognitive Research in Science, Engineering and Education. 2017;5(2):83-98.
[9] Lee J, Park O-C. Adaptive instructional systems.  Handbook of research on educational communications and technology: Citeseer; 2008. p. 469-84.
[10] Schwartz L, Clark S, Cossarin M, Rudolph J. 27. Educational Wikis: Features and selection criteria. International Review of Research in Open and Distributed Learning. 2004;5(1):1-6.
[11] Tawafak RM, AlSideir A, Alfarsi G, Al-Nuaimi MN, Malik SI, Jabbar J. E-learning vs. traditional learning for learners satisfaction. E-learning. 2019;29(3):388-97.
[12] Kulaglić S, Mujačić S, Serdarević IK, Kasapović S, editors. Influence of learning styles on improving efficiency of adaptive educational hypermedia systems. 2013 12th International conference on information technology based higher education and training (ITHET); 2013: IEEE.
[13] Liu G-Z, J. Spector, M. Merrill, J.J.G. Van Merrienboer, M. Driscoll, and L. Erlbaum. Handbook of Research on Educational Communications and Technology. Edition r, editor2010.
[14] Fatahi S, Moradian S. An Empirical Study on the Impact of Using an Adaptive e-Learning Environment Based on Learner's Personality and Emotion. International Association for Development of the Information Society. 2018.
[15] Katsaris I, Vidakis N. Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research. 2021;1(2):124-45.
[16] Alshammari M, Anane R, Hendle RJ, editors. An E-Learning Investigation into Learning Style Adaptivity. 2015 48th Hawaii International Conference on System Sciences; 2015 5-8 Jan. 2015.
[17] Griggs SA, Dunn R. Multiculturalism and Learning Style. 1998.
[18] Honey P, Mumford A. The Manual of Learning Styles: Peter Honey; 1986.
[19] De Cecco JP, Crawford WR. The Psychology of Learning and Instruction: Educational Psychology: Prentice-Hall; 1974.
[20] Woolfolk A, Shaughnessy MF. An interview with Anita Woolfolk: The educational psychology of teacher efficacy. Educational psychology review. 2004;16(2):153-76.
[21] Rezaei  A. Relationship between Kolb's learning modes and Honey and Mumford's learning styles with students’ age and academic performance. Educational Psychology. 2010;6(18):1-20. [In Persian]
[22] Felder R. Learning and Teaching Styles in Engineering Education. Journal of Engineering Education -Washington-. 1988;78:674-81.
[23] Hawk TF, Shah AJ. Using learning style instruments to enhance student learning. Decision Sciences Journal of Innovative Education. 2007;5(1):1-19.
https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-4609.2007.00125.x
[24] Fleming ND. I’m different; not dumb. Modes of presentation (VARK) in the tertiary classroom. Research and Development in Higher Education, Proceedings of the Annual Conference of the Higher Education and Research Development Society of Australasia. 1995;18, 308-313.
[25] Kolb AY. The Kolb learning style inventory-version 3.1 2005 technical specifications. Boston, MA: Hay Resource Direct. 2005;200(72):166-71.
[26] Kolb DA. Experiential learning: Experience as the source of learning and development: FT press; 2014.
[27] Graf S, Viola SR, Leo T, Kinshuk n. In-Depth Analysis of the Felder-Silverman Learning Style Dimensions. Journal of Research on Technology in Education. 2007;40(1):79-93.
https://www.tandfonline.com/doi/abs/10.1080/15391523.2007.10782498
[28] Graf S, Liu T-C, Chen N-S, Yang SJ. Learning styles and cognitive traits–Their relationship and its benefits in web-based educational systems. Computers in Human Behavior. 2009;25(6):1280-9.
[29] Truong HM. Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in Human Behavior. 2016;55:1185-93.
[30] Nafea SM, Siewe F, He Y. On Recommendation of Learning Objects Using Felder-Silverman Learning Style Model. IEEE Access. 2019;7:163034-48.
[31] Liyanage MPP, KS LG, Hirakawa M. Detecting learning styles in learning management systems using data mining. Journal of Information Processing. 2016;24(4):740-9.
[32] Schafer JB, Frankowski D, Herlocker J, Sen S. Collaborative filtering recommender systems.  The adaptive web: methods and strategies of web personalization: Springer; 2007. p. 291-324.
[33] IEEE Standard for Learning Object Metadata. IEEE Std 1484121-2020. 2020:1-50.
[34] Sweller J, van Merriënboer JJ, Paas F. Cognitive architecture and instructional design: 20 years later. Educational Psychology Review. 2019;31:261-92.
[35] Hart SG, editor NASA-task load index (NASA-TLX); 20 years later. Proceedings of the human factors and ergonomics society annual meeting; 2006: Sage publications Sage CA: Los Angeles, CA.
https://journals.sagepub.com/doi/abs/10.1177/154193120605000909
[36] Hart SG, Staveland LE. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In: Hancock PA, Meshkati N, editors. Advances in Psychology. 52: North-Holland; 1988. p. 139-83.
[37] Latifzadeh K, Amiri S, Bosaghzadeh A, Rahimi M, Ebrahimpour R. Evaluating cognitive load of multimedia learning by eye-tracking data analysis. Technology of Education Journal (TEJ). 2020;15(1):33-50. [In Persian]
[38] Farkish A, Bosaghzadeh A, Amiri SH, Ebrahimpour R. Evaluating the effects of educational multimedia design principles on cognitive load using EEG signal analysis. Education and Information Technologies. 2023;28(3):2827-43.
[39] Samonte MJ, Acuña GEO, Alvarez LAZ, Miraflores JM. A Personality-Based Virtual Tutor for Adaptive Online Learning System. International Journal of Information and Education Technology. 2023;13(6).
[40] Karagiannis I, Satratzemi M, editors. Implementation of an adaptive mechanism in Moodle based on a hybrid Dynamic User Model. The Challenges of the Digital Transformation in Education: Proceedings of the 21st International Conference on Interactive Collaborative Learning (ICL2018)-Volume 1; 2020: Springer.
[41] Sanjabi T, Montazer GA, editors. Personalization of E-learning environment using the Kolb's learning style model. 2020 6th International conference on web research (ICWR); 2020: IEEE.
[42] Agarwal A, Mishra DS, Kolekar SV. Knowledge-based recommendation system using semantic web rules based on Learning styles for MOOCs. Cogent Engineering. 2022;9(1):2022568.
https://www.tandfonline.com/doi/abs/10.1080/23311916.2021.2022568
[43] Qaffas AA, Idrees AM, Khedr AE, Kholeif SA. A Smart Testing Model Based on Mining Semantic Relations. IEEE Access. 2023;11:30237-46.
[44] Bayounes W, Saâdi IB, Kinshuk. Toward an intentional model aware of learner cognitive traits for pedagogical process guidance. Smart Learning Environments. 2023;10(1):25.
[45] Jayasiriwardene S, Meedeniya D. An adaptive and interactive learning toolkit (iLearn). Software Impacts. 2023;15:100471.
[46] Amiri M, Mantazar G, editors. Homogeneous Grouping of e-Learners based on Felder-Silverman Model of their Learning Style. 2023 10th International and the 16th National Conference on E-Learning and E-Teaching (ICeLeT); 2023 28 Feb.-2 March 2023.
[47] Soloman BA, Felder RM. Index of learning styles questionnaire. NC State University Available online at: http://www.engrncsu.edu/learningstyles/ilsweb html (last visited on 1405 2010). 2005;70.
[48] Ghorbani F. Design a Personalized System Based On Learners Individual Attributes And Behavioral Signs in E-Learning Environment [dissertation]: Tarbiat Modares University; 2015. [In Persian]
[49] Samadi M. Study of Felder and slomon Psychometric Properties Questionnaire of learning styles. New Educational Approaches  2011;6(1):39-60.
[50] Karagiannis I, Satratzemi M. An adaptive mechanism for Moodle based on automatic detection of learning styles. Education and Information Technologies. 2018;23:1331-57.
[51] Borges G, Stiubiener I, editors. Recommending learning objects based on utility and learning style. 2014 IEEE Frontiers in Education Conference (FIE) Proceedings; 2014: IEEE.
[52] Sweta S, Lal K. Personalized adaptive learner model in e-learning system using FCM and fuzzy inference system. International Journal of Fuzzy Systems. 2017;19(4):1249-60.
[53] Mohammadi M, Mazloumi A, Nasl seraji J, Zeraati H. Designing questionnaire of assessing mental workload and determine its validity and reliability among ICUs nurses in one of the TUMS’s hospitals. Journal of School of Public Health and Institute of Public Health Research. 2013;11(2):87-96.
Send comment about this article
CAPTCHA Image
Technology of Education Journal (TEJ)

Articles in Press, Accepted Manuscript
Available Online from 08 January 2025
Files
History
  • Receive Date: 09 April 2024
  • Revise Date: 27 June 2024
  • Accept Date: 09 August 2024
Share
How to cite
Statistics