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Adaptive e-learning systems through learning styles: A review of the literature

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Submitted   Sep 3, 2021
Published   Oct 9, 2021
Issue    Vol 1 No 2 (2021)
DOI   10.25082/AMLER.2021.02.007

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Iraklis Katsaris corresponding author
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, 71410, Greece
Nikolas Vidakis
Department of Electrical and Computer Engineering, Hellenic Mediterranean University, Heraklion, Crete, 71410, Greece

Abstract

The domain of education has taken great leaps by capitalizing on technology and the utilization of modern devices. Nowadays, the established term "one size fits all" has begun to fade. The research focuses on personalized solutions to provide a specially designed environment on the needs and requirements of the learner. The adaptive platforms usually use Learning Styles to offer a more effective learning experience. This review analyzes the learner model, adaptation module, and domain module, originating from the study of 42 papers published from 2015 to 2020. As more modern techniques for adaptation get incorporated into e-learning systems, such techniques must be compliant with educational theories.  This review aims to present the theoretical and technological background of Adaptive E-learning Systems while emphasizing the importance and efficiency of the utilization of Learning Styles in the adaptive learning process. This literature review is designated for the researchers in this field and the future creators and developers of adaptive platforms.

Keywords
learning styles, adaptive e-learning systems, adaptive hypermedia educational systems, intelligent tutoring systems, personalized learning

Article Details

How to Cite
Katsaris, I., & Vidakis, N. (2021). Adaptive e-learning systems through learning styles: A review of the literature. Advances in Mobile Learning Educational Research, 1(2), 124-145. https://doi.org/10.25082/AMLER.2021.02.007
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

References

  1. Abdo, N., & Noureldien, N. A. (2017). Evolution of dynamic user models for adaptive educational hypermedia system (AEHS). Application of Information and Communication Technologies, AICT 2016 - Conference Proceedings. https://doi.org/10.1109/ICAICT.2016.7991810
  2. Abech, M., da Costa, C. A., Barbosa, J. L. V., Rigo, S. J., & da Rosa Righi, R. (2016). A model for learning objects adaptation in light of mobile and context-aware computing. Personal and Ubiquitous Computing, 20(2), 167-184. https://doi.org/10.1007/s00779-016-0902-3
  3. Abueloun, N. N., & Naser, A. (2017). Mathematics intelligent tutoring system. International Journal of Advanced Scientific Research, 2(3), 11-16.
  4. Abyaa, A., Khalidi Idrissi, M., & Bennani, S. (2019). Learner modelling: systematic review of the literature from the last 5 years. In Educational Technology Research and Development (Vol. 67, Issue 5). Springer US. https://doi.org/10.1007/s11423-018-09644-1
  5. Aeiad, E., & Meziane, F. (2019). An adaptable and personalised elearning system applied to computer. Education and Information Technologies, 78, 674-681.
  6. Afini Normadhi, N. B., Shuib, L., Md Nasir, H. N., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of personal traits in adaptive learning environment: Systematic literature review. Computers and Education, 130(March 2018), 168-190. https://doi.org/10.1016/j.compedu.2018.11.005
  7. Ahmad, N., Tasir, Z., Kasim, J., & Sahat, H. (2013). Automatic Detection of Learning Styles in Learning Management Systems by Using Literature-based Method. Procedia - Social and Behavioral Sciences, 103, 181-189. https://doi.org/10.1016/j.sbspro.2013.10.324
  8. Akkila, A. N., & Naser, S. S. A. (2017). Teaching the Right Letter Pronunciation in Reciting the Holy Quran using Intelligent Tutoring System. International Journal of Advanced Research and Development, 2(1), 64-68.
  9. Al-Azawei, A., & Badii, A. (2014). State of The Art of Learning Styles-Based Adaptive Educational Hypermedia Systems (Ls-Baehss). International Journal of Computer Science and Information Technology, 6(3), 1-19. https://doi.org/10.5121/ijcsit.2014.6301
  10. Aleven, V., McLaren, B. M., Sewall, J., Van Velsen, M., Popescu, O., Demi, S., Ringenberg, M., & Koedinger, K. R. (2016). Example-Tracing Tutors: Intelligent Tutor Development for Nonprogrammers. International Journal of Artificial Intelligence in Education, 26(1), 224-269. https://doi.org/10.1007/s40593-015-0088-2
  11. Ali, N. A., Eassa, F., & Hamed, E. (2019). Personalized Learning Style for Adaptive E-Learning System. International Journal of Advanced Trends in Computer Science and Engineering, 8(1), 223- 230. https://doi.org/10.30534/ijatcse/2019/4181.12019
  12. Aljojo, N., Alsaleh, I., & Alshamasi, A. (2016). Difficulties in adapting feedback for individual learning styles in the Arabic Teacher Assisting and Subject Adaptive Material (TASAM) system. International Journal of Management in Education, 10(3), 293-308. https://doi.org/10.1504/IJMIE.2016.077510
  13. Almeida, T. O., VIsta, B., & De Magalhaes Netto, J. F. (2019). Adaptive Educational Resource Model to Promote Robotic Teaching in STEM Courses. Proceedings - Frontiers in Education Conference, FIE, 2019-Octob. https://doi.org/10.1109/FIE43999.2019.9028417
  14. Alshammari, M., Anane, R., & Hendle, R. J. (2015). An E-learning investigation into learning style adaptivity. Proceedings of the Annual Hawaii International Conference on System Sciences, 2015-March, 11-20. https://doi.org/10.1109/HICSS.2015.13
  15. Alshammari, M., Anane, R., & Hendley, R. J. (2014). Adaptivity in E-learning systems. Proceedings - 2014 8th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2014, 79-86. https://doi.org/10.1109/CISIS.2014.12
  16. Alshammari, M., & Qtaish, A. (2019). Effective adaptive e-learning systems according to learning style and knowledge level. Journal of Information Technology Education: Research, 18, 529-547. https://doi.org/10.28945/4459
  17. Alsobhi, A. Y., & Alyoubi, K. H. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53(2), 189-200. https://doi.org/10.1108/DTA-10-2018-0092
  18. Anantharaman, H., Mubarak, A., & Shobana, B. T. (2019). Modelling an Adaptive e-Learning System Using LSTM and Random Forest Classification. 2018 IEEE Conference on E-Learning, e-Management and e-Services, IC3e 2018, 29-34. https://doi.org/10.1109/IC3e.2018.8632646
  19. Andaloussi, K. S., Capus, L., Berrada, I., & Boubouh, K. (2019). Improving smart learning experience quality through the use of extracted data from social networks. International Journal of Intelligent Enterprise, 6(2/3/4), 311. https://doi.org/10.1504/IJIE.2019.101134
  20. Apoki, U. C., Al-Chalabi, H. K. M., & Crisan, G. C. (2020). From Digital Learning Resources to Adaptive Learning Objects: An Overview. Communications in Computer and Information Science, 1126 CCIS, 18-32. https://doi.org/10.1007/978-3-030-39237-6_2
  21. Araújo, R. D., Brant-Ribeiro, T., Ferreira, H. N. M., Dorc¸a, F. A., & Cattelan, R. G. (2020). Using Learning Styles for Creating and Personalizing Educational Content in Ubiquitous Learning Environments. Revista Brasileira de Inform´atica Na Educac¸ ˜ao, 28, 133-149. https://doi.org/10.5753/rbie.2020.28.0.133
  22. Arsovic, B., & Stefanovic, N. (2020a). E-learning based on the adaptive learning model: case study in Serbia. Sadhana - Academy Proceedings in Engineering Sciences, 45(1), 1-13. https://doi.org/10.1007/s12046-020-01499-8
  23. Arsovic, B., & Stefanovic, N. (2020b). E-learning based on the adaptive learning model: case study in Serbia. Sadhana - Academy Proceedings in Engineering Sciences, 45(1), 266. https://doi.org/10.1007/s12046-020-01499-8
  24. Baharudin, A. F., Sahabudin, N. A., & Kamaludin, A. (2017). Behavioral tracking in E-learning by using learning styles approach. Indonesian Journal of Electrical Engineering and Computer Science, 8(1), 17-26. https://doi.org/10.11591/ijeecs.v8.i1.pp17-26
  25. Bendahmane, M., El Falaki, B., & Benattou, M. (2019). Toward a Personalized Learning Path through a Services-Oriented Approach. International Journal of Emerging Technologies in Learning (IJET), 14(15), 52. https://doi.org/10.3991/ijet.v14i15.10951
  26. Bendahmane, M., Falaki, B. El, & Benattou, M. (2019). Paper-Toward a Personalized learning Path through a Services-Oriented Approach. International Journal of Emerging Technologies in Learning (IJET), 14(15), 52-66. https://doi.org/10.3991/ijet.v14i15.10951
  27. Bernard, J., Chang, T. W., Popescu, E., & Graf, S. (2017). Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms. Expert Systems with Applications, 75, 94-108. https://doi.org/10.1016/j.eswa.2017.01.021
  28. Bouneffouf, D. (2013). Towards User Profile Modelling in Recommender System. 1-5. http://arxiv.org/abs/1305.1114
  29. Boussakuk, M. (2020). A Fully individualized Adaptive and Intelligent Educational Hypermedia System: Details of CleverUniversity. International Journal of Emerging Trends in Engineering Research, 8(5), 1497-1502. https://doi.org/10.30534/ijeter/2020/04852020
  30. Bradáč, V. (2020). Personalised English Language Education Through an E-learning Platform. 517- 526.
  31. Bradáč, V., & Smolka, P. (2020). Personalised English Language Education Through an E-learning Platform. In M. Sitek Pawełand Pietranik, M. Kr´otkiewicz, & C. Srinilta (Eds.), Intelligent Information and Database Systems (pp. 517-526). Springer Singapore.
  32. Brajnik, G., Guida, G., & Tasso, C. (1987). User modeling in intelligent information retrieval. Information Processing and Management, 23(4), 305-320. https://doi.org/10.1016/0306-4573(87)90020-3
  33. Brusilovsky, P. (1998). Adaptive Hypertext and Hypermedia. In Adaptive Hypertext and Hypermedia (Issue February 1999). https://doi.org/10.1007/978-94-017-0617-9
  34. Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1-2), 87-110. https://doi.org/10.1023/A:1011143116306
  35. Brusilovsky, P. L. (1992). Intelligent Tutor, Environment and Manual for Introductory Programming. Educational and Training Technology International, 29(1), 26-34. https://doi.org/10.1080/0954730920290104
  36. Brusilovsky, P., & Nejdl, W. (2004). Adaptive hypermedia and adaptive web. The Practical Handbook of Internet Computing, August, 1-1-1-14. https://doi.org/10.1201/9780203507223
  37. Brusilovsky, P., & Peylo, C. (2003). Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education, 13(2-4), 159-172.
  38. Budiyanto, U., Hartati, S., Azhari, S. N., & Mardapi, D. (2017). Intelligent system E-learning modeling according to learning styles and level of ability of students. Communications in Computer and Information Science, 788, 278-290. https://doi.org/10.1007/978-981-10-7242-0_24
  39. Chang, Y. C., Kao, W. Y., Chu, C. P., & Chiu, C. H. (2009). A learning style classification mechanism for e-learning. Computers and Education, 53(2), 273-285. https://doi.org/10.1016/j.compedu.2009.02.008
  40. Christudas, B. C. L., Kirubakaran, E., & Thangaiah, P. R. J. (2018). An evolutionary approach for personalization of content delivery in e-learning systems based on learner behavior forcing compatibility of learning materials. Telematics and Informatics, 35(3), 520-533. https://doi.org/10.1016/j.tele.2017.02.004
  41. Coffield, F., Moseley, D., Hall, E., & Ecclestone, K. (2004). Learning styles and pedagogy in post- 16 learning: a systematic and critical review. National Centre for Vocational Education Research (NCVER), 84. http://www.voced.edu.au/td/tnc_79.72
  42. Dorc¸a, F. A., Ara´ujo, R. D., de Carvalho, V. C., Resende, D. T., & Cattelan, R. G. (2016). An automatic and dynamic approach for personalized recommendation of learning objects considering students learning styles: An experimental analysis. Informatics in Education, 15(3), 45-62. https://doi.org/10.15388/infedu.2016.03
  43. Dorc¸a, F. A., Lima, L. V., Fernandes, M. A., & Lopes, C. R. (2013). Comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: An experimental analysis. Expert Systems with Applications, 40(6), 2092-2101. https://doi.org/10.1016/j.eswa.2012.10.014
  44. Drissi, S., & Amirat, A. (2016). An adaptive e-learning system based on student’s learning styles: An empirical study. International Journal of Distance Education Technologies, 14(3), 34-51. https://doi.org/10.4018/IJDET.2016070103
  45. Dwivedi, P., Kant, V., & Bharadwaj, K. K. (2018). Learning path recommendation based on modified variable length genetic algorithm. Education and Information Technologies, 23(2), 819-836. https://doi.org/10.1007/s10639-017-9637-7
  46. El Guabassi, I., Bousalem, Z., Al Achhab, M., Jellouli, I., & El Mohajir, B. E. (2018). Personalized adaptive content system for context-Aware ubiquitous learning. Procedia Computer Science, 127, 444-453. https://doi.org/10.1016/j.procs.2018.01.142
  47. Elkot, M. A. (2019). Embedding adaptation levels within intelligent tutoring systems for developing programming skills and improving learning efficiency. International Journal of Advanced Computer Science and Applications, 10(12), 82-87. https://doi.org/10.14569/ijacsa.2019.0101211
  48. Entwistle, N. (1997). Introduction: Phenomenography in Higher Education. International Journal of Phytoremediation, 21(1), 127-134. https://doi.org/10.1080/0729436970160202
  49. Fasihuddin, H., Skinner, G., & Athauda, R. (2016). Using learning styles as a basis for creating adaptive open learning environments: An evaluation. International Journal of Learning Technology, 11(3), 198-217. https://doi.org/10.1504/IJLT.2016.079034
  50. Felder, R. M., & Spurlin, J. (2005). Applications, reliability and validity of the index of learning styles. International Journal of Engineering Education, 21(1), 103-112.
  51. Felder, R., & Silverman, L. (1988). Learning and teaching styles and libraries. Journal of Engineering Education, 78(June), 674-681.
  52. Feldman, J., Monteserin, A., & Amandi, A. (2016). Recommending educational video games based on game features and student’s Learning Styles. 2016 IEEE Biennial Congress of Argentina, ARGENCON 2016, 1-6. https://doi.org/10.1109/ARGENCON.2016.7585274
  53. Fleming, N. D. (1995). 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, 18, 308-313.
  54. Gardner, H. (1983). Frames of Mind (New York). Basic Books.
  55. Gauch, S., Speretta, M., Chandramouli, A., & Micarelli, A. (2007). User profiles for personalized information access. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4321 LNCS, 54-89. https://doi.org/10.1007/978-3-540-72079-9_2
  56. Gonz´alez, B. (2012). El modelo VARK y el dise˜no de cursos en l´ınea. Revista Mexicana de Bachillerato a Distancia, 4(8). https://doi.org/10.22201/cuaed.20074751e.2012.8.44282
  57. Graf, S., Kinshuk, & Liu, T. C. (2009). Supporting Teachers in Identifying Students’ Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach. Educational Technology and Society, 12(4), 3-14.
  58. Graf, S., Liu, T. C., & Kinshuk, K. (2008). Interactions between students’ learning styles, achievement and behaviour in mismatched courses. IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2008, 1976, 223-230.
  59. Hafidi, M., & Bensebaa, T. (2015). Architecture for an adaptive and intelligent tutoring system that considers the learner’s multiple intelligences. International Journal of Distance Education Technologies, 13(1), 1-21. https://doi.org/10.4018/ijdet.2015010101
  60. Haider, T. U., Sinha, A. K., & Chaudhary, B. D. (2010). An Investigation of relationship between learning styles and performance of learners. International Journal of Engineering Science and Technology, 2(7), 2813-2819.
  61. Halawa, M. S., Hamed, E. M. R., & Shehab, M. E. (2016). Personalized E-learning recommendation model based on psychological type and learning style models. 2015 IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015, 578-584. https://doi.org/10.1109/IntelCIS.2015.7397281
  62. Hassan, M. M., & Qureshi, A. N. (2018). Disrupting the Rote Learning Loop: CS Majors Iterating Over Learning Modules with an Adaptive Educational Hypermedia (pp. 66-77). https://doi.org/10.1007/978-3-319-91464-0_7
  63. Henning, P. A., Heberle, F., Streicher, A., Zielinski, A., Swertz, C., Bock, J., & Zander, S. (2014). Personalized web learning: Merging open educational resources into adaptive courses for higher education. CEUR Workshop Proceedings, 1181, 55-62.
  64. Hurtado, C., Licea, G., & Garcia-Valdez, M. (2018). Integrating Learning Styles in an Adaptive Hypermedia System with Adaptive Resources. Studies in Systems, Decision and Control, 143, 49-67. https://doi.org/10.1007/978-3-319-74060-7_3
  65. Kanoje, S., Girase, S., & Mukhopadhyay, D. (2014). User Profiling Trends, Techniques and Applications, 1(1). http://arxiv.org/abs/1503.07474
  66. Kaplan, A. M., & Haenlein, M. (2016). Higher education and the digital revolution: About MOOCs, SPOCs, social media, and the Cookie Monster. Business Horizons, 59(4), 441-450. https://doi.org/10.1016/j.bushor.2016.03.008
  67. Karagiannis, I., & Satratzemi, M. (2020). Implementation of an Adaptive Mechanism in Moodle Based on a Hybrid Dynamic User Model. Advances in Intelligent Systems and Computing, 916, 377-388. https://doi.org/10.1007/978-3-030-11932-4_36
  68. Keefe, R. A. O. (1997). Implementing A in Prolog. October, 1-20.
  69. Kinshuk, Liu, T. C., & Graf, S. (2009). Coping with mismatched courses: Students’ behaviour and performance in courses mismatched to their learning styles. Educational Technology Research and Development, 57(6), 739-752. https://doi.org/10.1007/s11423-009-9116-y
  70. Kirschner, P. A. (2017). Stop propagating the learning styles myth. Computers and Education, 106, 166-171. https://doi.org/10.1016/j.compedu.2016.12.006
  71. Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering - A systematic literature review. Information and Software Technology, 51(1), 7-15. https://doi.org/10.1016/j.infsof.2008.09.009
  72. Knutov, E., De Bra, P., & Pechenizkiy, M. (2009). AH 12 years later: A comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia, 15(1), 5-38. https://doi.org/10.1080/13614560902801608
  73. Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2018). Adaptive User Interface for Moodle based E-learning System using Learning Styles. Procedia Computer Science, 135, 606-615. https://doi.org/10.1016/j.procs.2018.08.226
  74. Kolekar, S. V., Pai, R. M., & Manohara Pai, M. M. (2019). Rule based adaptive user interface for adaptive E-learning system. Education and Information Technologies, 24(1), 613-641. https://doi.org/10.1007/s10639-018-9788-1
  75. Kumar, A., Singh, N., & Jyothi, A. (2017). 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, 5(2), 83-97. https://doi.org/10.5937/IJCRSEE1702083K
  76. Kurilovas, E. (2016). Evaluation of quality and personalisation of VR/AR/MR learning systems. Behaviour and Information Technology, 35(11), 998-1007. https://doi.org/10.1080/0144929X.2016.1212929
  77. Kurilovas, E. (2019). Advanced machine learning approaches to personalise learning: learning analytics and decision making. Behaviour and Information Technology, 38(4), 410-421. https://doi.org/10.1080/0144929X.2018.1539517
  78. Lakkah, S. El, Alimam, M. A., & Seghiouer, H. (2017). Adaptive e-learning system based on learning style and ant colony optimization. 2017 Intelligent Systems and Computer Vision, ISCV 2017. https://doi.org/10.1109/ISACV.2017.8054963
  79. Leite,W. L., Svinicki, M., & Shi, Y. (2010). Attempted validation of the scores of the VARK: Learning styles inventory with multitrait-multimethod confirmatory factor analysis models. Educational and Psychological Measurement, 70(2), 323-339. https://doi.org/10.1177/0013164409344507
  80. Liyanage, M. P. P., Lasith Gunawardena, K. S., & Hirakawa, M. (2016). Detecting learning styles in learning management systems using data mining. Journal of Information Processing, 24(4), 740-749. https://doi.org/10.2197/ipsjjip.24.740
  81. Maravanyika, M., Dlodlo, N., & Jere, N. (2017). An adaptive recommender-system based framework for personalised teaching and learning on e-learning platforms. 2017 IST-Africa Week Conference, IST-Africa 2017, 1-9. https://doi.org/10.23919/ISTAFRICA.2017.8102297
  82. Mulwa, C., Lawless, S., Sharp, M., Arnedillo-Sanchez, I., & Wade, V. (2010). Adaptive educational hypermedia systems in technology enhanced learning: A literature review. SIGITE’10 - Proceedings of the 2010 ACM Conference on Information Technology Education, 73-84. https://doi.org/10.1145/1867651.1867672
  83. Nalintippayawong, S., Atchariyachanvanich, K., & Julavanich, T. (2017). DBLearn: Adaptive elearning for practical database course - An integrated architecture approach. Proceedings - 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017, 109-114. https://doi.org/10.1109/SNPD.2017.8022708
  84. Nongkhai, L. N., & Kaewkiriya, T. (2015). Framework for e-Leaming recommendation based on index of learning styles model. Proceedings - 2015 7th International Conference on Information Technology and Electrical Engineering: Envisioning the Trend of Computer, Information and Engineering, ICITEE 2015, April 2016, 587-592. https://doi.org/10.1109/ICITEED.2015.7409015
  85. Özyurt, Ö., & Özyurt, H. (2015). Computers in Human Behavior Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. 52, 349-358. https://doi.org/10.1016/j.chb.2015.06.020
  86. Papadimitriou, A., Grigoriadou, M., & Gyftodimos, G. (2012). MATHEMA: A Learner-controlled Adaptive Educational Hypermedia System. Journal of Information Technology and Application in Education (JITAE) JITAE, 1(2), 47-73.
  87. Papanikolaou, K. A. (2015). Constructing interpretative views of learners’ interaction behavior in an open learner model. IEEE Transactions on Learning Technologies, 8(2), 201-214. https://doi.org/10.1109/TLT.2014.2363663
  88. Papanikolaou, K. A., Grigoriadou, M., Kornilakis, H., & Magoulas, G. D. (2003). Personalizing the Interaction in aWeb-based EducationalHypermediaSystem, 213-267.
  89. Popescu, E. (2010). A unified learning style model for technologyenhanced learning: What, why and how? International Journal of Distance Education Technologies, 8(3), 65-81. https://doi.org/10.4018/jdet.2010070105
  90. Popescu, E., Bǎdicǎ, C., & Moraret, L. (2009). WELSA: An intelligent and adaptive web-based educational system. Studies in Computational Intelligence, 237, 175-185. https://doi.org/10.1007/978-3-642-03214-1_17
  91. Poultsakis, S., Papadakis, S., Kalogiannakis, M., & Psycharis, S. (2021). The management of Digital Learning Objects of Natural Sciences and Digital Experiment Simulation Tools by teachers. Advances in Mobile Learning Educational Research, 1(2), 58-71. https://doi.org/10.25082/amler.2021.02.002
  92. Rani, M., Nayak, R., & Vyas, O. P. (2017). An Ontology-based Adaptive Personalized E-learning System, Assisted by Software Agents on Cloud Storage. ArXiv, 90, 33-48.
  93. Rani, M., Vyas, R., & Vyas, O. P. (2017). OPAESFH: Ontology-based personalized adaptive elearning system using FPN and HMM. IEEE Region 10 Annual International Conference, Proceedings/ TENCON, 2017-Decem, 2441-2446. https://doi.org/10.1109/TENCON.2017.8228271
  94. Sanchez-Martin, J., Alvarez-Gragera, G. J., Davila-Acedo, M. A., & Mellado, V. (2017). Teaching technology: From knowing to feeling enhancing emotional and content acquisition performance through Gardner’S multiple intelligences theory in technology and design lessons. Journal of Technology and Science Education, 7(1), 58-79. https://doi.org/10.3926/jotse.238
  95. Sensuse, D. I., Hasani, L. M., & Bagustari, B. (2020). Personalization Strategies Based on Felder- Silverman Learning Styles and Its Impact on Learning: A Literature Review, 293-298.
  96. Siddique, A., Durrani, Q. S., & Naqvi, H. A. (2019). Developing Adaptive E-Learning Environment Using Cognitive and Noncognitive Parameters. Journal of Educational Computing Research, 57(4), 811-845. https://doi.org/10.1177/0735633118769433
  97. Sunil, & Doja, M. N. (2019). Recommender System for Integrating Tacit Knowledge in E-learning Environment to Enhance Learning Potential of Learners. SSRN Electronic Journal, 1397-1402. https://doi.org/10.2139/ssrn.3446563
  98. Supangat, & Bin Saringat, M. (2020). Development of e-learning system using felder and silverman’s index of learning styles model. International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 8554-8561. https://doi.org/10.30534/ijatcse/2020/236952020
  99. Sweta, S., & Lal, K. (2017). Personalized Adaptive Learner Model in E-Learning System Using FCM and Fuzzy Inference System. International Journal of Fuzzy Systems, 19(4), 1249-1260. https://doi.org/10.1007/s40815-017-0309-y
  100. Tarus, J. K., Niu, Z., & Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72, 37-48. https://doi.org/10.1016/j.future.2017.02.049
  101. Thaker, K., Huang, Y., Brusilovsky, P., & Daqing, H. (2018). Dynamic Knowledge Modeling with Heterogeneous Activities for Adaptive Textbooks. 11th International Conference on Educational Data Mining, September 2019, 592-595. http://d-scholarship.pitt.edu/34939
  102. Tortorella, R. A.W., & Graf, S. (2017). Considering learning styles and context-awareness for mobile adaptive learning. Education and Information Technologies, 22(1), 297-315. https://doi.org/10.1007/s10639-015-9445-x
  103. Triantafillou, E., Pomportsis, A., Demetriadis, S., & Georgiadou, E. (2004). The value of adaptivity based on cognitive style: An empirical study. British Journal of Educational Technology, 35(1), 95-106. https://doi.org/10.1111/j.1467-8535.2004.00371.x
  104. Truong, H. M. (2016). Computers in Human Behavior Integrating learning styles and adaptive elearning system: Current developments , problems and opportunities. Computers in Human Behavior, 55, 1185-1193. https://doi.org/10.1016/j.chb.2015.02.014
  105. Tsortanidou, X., Karagiannidis, C., & Koumpis, A. (2017). Adaptive educational hypermedia systems based on learning styles: The case of adaptation rules. International Journal of Emerging Technologies in Learning, 12(5), 150-168. https://doi.org/10.3991/ijet.v12i05.6967
  106. Vaidya, R., & Joshi, M. (2018). Use of Learning Style Based Approach in Instructional Delivery. Lecture Notes in Networks and Systems, 18, 199-209. https://doi.org/10.1007/978-981-10-6916-1_18
  107. Van Zwanenberg, N., Wilkinson, L. J., & Anderson, A. (2000). Felder and silverman’s index of learning styles and honey and mumford’s learning styles questionnaire: How do they compare and do they predict academic performance? Educational Psychology, 20(3), 365-380. https://doi.org/10.1080/713663743
  108. Vidakis, N., Barianos, A. K., Trampas, A. M., Papadakis, S., Kalogiannakis, M., & Vassilakis, K. (2019). Generating education in-game data: The case of an ancient theatre serious game. CSEDU 2019 - Proceedings of the 11th International Conference on Computer Supported Education, 1(Csedu), 36-43. https://doi.org/10.5220/0007810800360043
  109. Vidakis, N., Barianos, A. K., Trampas, A. M., Papadakis, S., Kalogiannakis, M., & Vassilakis, K. (2020). in-Game Raw Data Collection and Visualization in the Context of the “ThimelEdu” Educational Game. In Communications in Computer and Information Science (Vol. 1220). Springer International Publishing. https://doi.org/10.1007/978-3-030-58459-7_30
  110. Watkins, C. (2016). Developing student-driven learning: The patterns, the context, and the process. Student-Driven Learning Strategies for the 21st Century Classroom, March, 1-9. https://doi.org/10.4018/978-1-5225-1689-7.ch001