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Dec 29, 2019
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Apr 13, 2020
Published
May 13, 2020
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Abstract

During the past few decades, it seems that personalizing and adjusting the e-courses’ content based on individual learning styles is rather important. Indeed, several studies have been carried out throughout the years regarding the a priori personalization and adjustment of e-courses systems. This way modern LMSs (Learning Management Systems) could identify beforehand the learning styles of the e-course attendants and adjust the lesson content flow and type based on personal learning styles. Nevertheless, little bibliography exists on how to assess the compatibility level between educational content and learning styles dimensions of an LMS, in a real-life environment.  With the above thoughts in mind, the current work attempts to introduce and verify an innovative framework for the students' learning styles and e-courses compatibility assessment, based on the content type and volume. The proposed framework is validated through its application at an LMS in a real-life academic environment. Such an approach could be very beneficial for already deployed e-courses on LMSs that aim to differentiate educational content provision based on users’ profiles.

Keywords

LMS e-course Moodle Learning Styles FSLS

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How to Cite
Kouis, D., Kyprianos, K., Ermidou, P., Kaimakis, P., & Koulouris, A. (2020). A framework for assessing LMSs e-courses content type compatibility with learning styles dimensions. Journal of E-Learning and Knowledge Society, 16(2), 73-86. https://doi.org/10.20368/1971-8829/1135204

References

  1. Adetunji, A. & Ademola, A. (2014), A Proposed Architectural Model for an Automatic Adaptive E-Learning System Based on Users Learning Style, International Journal of Advanced Computer Science & Applications, 5(4), 1-5.
  2. 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. doi: 10.1016/J.ESWA.2017.01.021.
  3. Cassidy, S. (2004), Learning styles: An overview of theories, models, and measures, Educational Psychology, 24(4), 419–444. doi: 10.1080/0144341042000228834.
  4. Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M. & Lee, J.-H. (2006), Learning Style Diagnosis Based on User Interface Behavior for the Customization of Learning Interfaces in an Intelligent Tutoring System, the 8th International Conference on Intelligent Tutoring Systems, Lecture Notes in Computer Science. Vol. 4053, 513–524. doi: 10.1007/11774303_51.
  5. Chen, C.M. & Sun, Y.C. (2012), Assessing the effects of different multimedia materials on emotions and learning performance for visual and verbal style learners, Computers & Education, 59(4), 1273–1285. doi: 10.1016/J.COMPEDU.2012.05.006.
  6. Coffield, F., Moseley, D., Hall, E. & Ecclestone, K. (2004), Learning styles and pedagogy in post-16 learning: a systematic and critical review. London: Learning and Skills Research Centre.
  7. Crockett, K., Latham, A. & Whitton, N. (2017), On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees, International Journal of Human-Computer Studies, 97, 98–115. doi: 10.1016/J.IJHCS.2016.08.005.
  8. D'Amore, A., James, S. & Mitchell, E.K.L. (2012), Learning styles of first-year undergraduate nursing and midwifery students: A cross-sectional survey utilising the Kolb Learning Style Inventory, Nurse Education Today, 32(5), 506-515.
  9. Despotović-Zrakić, M., Marković, A., Bogdanović, Z., Barać, D. & Krčo, S. (2012), Providing adaptivity in moodle lms courses, Educational Technology and Society, 15(1), 326–338. doi: 10.1.1.231.7146.
  10. De Jesus, H. P., Almeida, P. & Watts, M. (2004), Questioning styles and students’ learning: Four case studies, Educational Psychology, 24(4), 531–548. doi: 10.1080/0144341042000228889.
  11. Felder, R. M. & Silverman, L. K. (1988), Learning and teaching styles in engineering education, Engineering Education, 78(7), 674–681.
  12. Felder, R.M. & Soloman, B.A. (2017), Learning styles and strategies. Available at: http://www4.ncsu.edu/unity/lockers/users/f/felder/public/ILSdir/styles.htm (Accessed: 20 June 2018).
  13. García, P., Amandi, A., Schiaffino, S. & Campo, M. (2007) Evaluating Bayesian networks’ precision for detecting students’ learning styles, Computers and Education, 49(3), 794–808. doi: 10.1016/j.compedu.2005.11.017.
  14. Graf, S. & Kinshuk, K. (2007), Providing adaptive courses in learning management systems with respect to learning styles, Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, 17(1), 2576–2583. Available at: http://www.editlib.org/p/26739/.
  15. Graf, S., Viola S., R., Lea, T. & Kinshuk (2007), In Depth Analysis of the Felder-SilvermanLearning Style Dimensions, Reseach on Technology in Education, 40(1), 79–93.
  16. Graf, S., Kinshuk, Α. & Ives, C. (2010), A Flexible Mechanism for Providing Adaptivity Based on Learning Styles in Learning Management Systems, 10th IEEE International Conference on Advanced Learning Technologies, 30–34. doi: 10.1109/ICALT.2010.16.
  17. Hattie, J., & Yates, G. (2014). Visible learning and the science of how we learn. London, England: Routledge.
  18. Hung, Y.H., Chang, R. I. & Lin, C.F. (2016), Hybrid learning style identification and developing adaptive problem-solving learning activities, Computers in Human Behavior, 55, 552–561. doi: 10.1016/J.CHB.2015.07.004.
  19. Kirschner, P.A. (2017), Stop propagating the learning styles myth. Computers & Education, 106, 166-171.
  20. Kirschner, P.A., & van Merriënboer, J.J. (2013), Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169-183.
  21. Klašnja-Milićević, A., Vesin, B., Ivanović, M. & Budimac, Z. (2011), E-Learning personalization based on hybrid recommendation strategy and learning style identification, Computers and Education, 56(3), 885–899. doi: 10.1016/j.compedu.2010.11.001.
  22. Kuljis, J. & Liu, F. (2005), A comparison of learning style theories on the suitability for e-learning, in Hamza, M.H. (ed.) Proceedings of the IASTED Conference on Web-Technologies, Applications, and Services. Calgary, Alberta, Canada: ACTA Press, 191–197.
  23. Kumar, A., Singh, N. & Jyothi-Ahuja, N. (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. doi: 10.5937/ijcrsee1702083K.
  24. Labib, A.E., Canós, J.H. & Penadés, M.C. (2017), On the way to learning style models integration: a Learner’s Characteristics Ontology, Computers in Human Behavior, 73, 433–445. doi: 10.1016/J.CHB.2017.03.054.
  25. Limongelli, C., Sciarrone, F. & Vaste, G. (2011), Personalized e-learning in moodle: The moodle_LS system, Journal of E-Learning and Knowledge Society, 7(1), 49–58.
  26. McKenna, L., Copnell, B., Butler, A. E. & Lau, R. (2018), Learning style preferences of Australian accelerated postgraduate pre-registration nursing students: a cross-sectional survey, Nurse Education in Practice, 28, 280–284. doi: 10.1016/J.NEPR.2017.10.011.
  27. Mendéz, N.D.D., Morales, V.T. & Vicari, R.M. (2016), Learning Object Metadata Mapping with Learning Styles as a Strategy for Improving Usability of Educational Resource Repositories, Revista Iberoamericana de Tecnologias del Aprendizaje, 11(2), 101–106. doi: 10.1109/RITA.2016.2554038.
  28. Murphy, R.J., Gray, S.A., Straja, S.R. and Bogert, M.C. (2004), Student Learning Preferences and Teaching Implications, Journal of Dental Education, 68(8), 859-866.
  29. Newton, P.M., & Miah, M. (2017), Evidence-Based Higher Education - Is the Learning Styles 'Myth' Important?, Frontiers in psychology, 8, 444. doi:10.3389/fpsyg.2017.00444
  30. Newton P.M. (2015), The Learning Styles Myth is Thriving in Higher Education, Frontiers in psychology, 6,1908. doi:10.3389/fpsyg.2015.01908
  31. Ocepek, U., Bosnić, Z., Nančovska Šerbec, I. & Rugelj, J. (2013), Exploring the relation between learning style models and preferred multimedia types, Computers & Education. Pergamon, 69, 343–355. doi: 10.1016/J.COMPEDU.2013.07.029.
  32. Özyurt, Ö. & Özyurt, H. (2015), Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014, Computers in Human Behavior, 52, 349–358. doi: 10.1016/J.CHB.2015.06.020.
  33. Radenkovic, B., Despotovic, M., Bogdanovic, Z. & Barac, D. (2009), Creating adaptive environment for e-learning courses, Journal of Information and Organizational Sciences, 33(1), 179–189.
  34. Shuib, M. & Azizan, S.N. (2015), Learning Style Preferences Among Male and Female ESL Students in Universiti-Sains Malaysia, The Journal of Educators Online-JEO, 13 (2), 103-141.
  35. Thomas, L., Ratcliffe, M., Woodbury, J. & Jarman, E. (2002), Learning styles and performance in the introductory programming sequence, Proceedings of the 33rd SIGCSE technical symposium on Computer science education,Cincinnati, Kentucky, February 27 - March 03, 33-37.
  36. Truong, H.M. (2016), Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities, Computers in Human Behavior, 55, 1185–1193. doi: 10.1016/j.chb.2015.02.014.
  37. Tseng, J.C.R., Chu, H.C., Hwang, G.J. & Tsai, C.C. (2008), Development of an adaptive learning system with two sources of personalization information, Computers and Education, 51(2), 776–786. doi: 10.1016/j.compedu.2007.08.002.
  38. Willingham, D.T., Hughes, E.M. & Dobolyi, D.G. (2015), The Scientific Status of Learning Styles Theories, Teaching of Psychology, 42(3), 266–271. doi: 10.1177/0098628315589505.
  39. Yang, T.C., Hwang, G.J. & Yang, S.J.H. (2013), Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles, Educational Technology & Society, 16(4), 185–200. doi: 10.2307/jeductechsoci.16.4.185.
  40. Zervos, S., Kyriaki-Manessi, D., Koulouris, A., Giannakopoulos, G. & Kouis, D.A. (2013), Evaluation of the e-Class Platform of the LIS Dept., TEI of Athens, Procedia - Social and Behavioral Sciences, 73, 727–735. doi: 10.1016/j.sbspro.2013.02.111