9th International Scientific Conference Technics and Informatics in Education – TIE 2022 (2022) стр. 271-277
АУТОР(И): Dragan Zlatković, Nebojša Denić, Miloš Ilić, Aleksandar Zakić
E-learning as a difficult structure contains distance learning, teaching resources in many forms and shapes, group and individual learning procedures, as well as interactive and tuition work. In order to increase the use and efficiency of e-learning systems, it is necessary to consider the individualities of students and their learning styles. Based on data collected in various ways, research methods Felder-Silverman Index of Learning Style Questionary (ILS), using the Moodle LMS, based on the subjective valuation of teachers, as well as based on data from the corporate information system, the affinities of students are determined. Then, based on this information, an adaptation is made, a process that adjusts the work of the LMS based on the learning styles of the students. The major goals that can be achieved by dynamic adaptation the e-learning system are to improve the appearance and effectiveness of the course, support in finding information about the subject, more efficient search and placement of search results in terms of student’s interest, and rise students faithfulness to the educational institution.
e-Learning, ILS Questionary, Learning Style, Moodle LMS, Dynamic Adaptation.
-  Mahieu, R. & Wolming, S. (2013). Motives for Lifelong Learners to Chose Web-based Courses. European Journal of Open, Distance and E-Learning, 16(1),1-10.
-  Khan, K. U. & Iqbal, J. (2015). Strategic plannong of e-learning implementation in higher education sector. in 24th International Conference for the International Association fro Manegment of Technology (IAMOT), Hatfield, England.
-  Nikolic, N., Petkovic, D., Denic, N., Milovancevic, M. & Gavrilovic, S. (2019). Apprisal and review of e-learning and ICT systems in teaching process. Physica A: Statistical Mechanics and its Applications, 513, 456-464.
-  Muruganandam, S. & Srininvasan, N. (2017). Personalised e-leraning systems using learner profile ontology and sequential pattern mining-based recommendation. International Journal of Business Intelligence and Data Mining archive, 12(1), 78-93.
-  Alfonseca, E., Carro, R. M., Martin, E., Ortigosa, A. & Paredes, P. (2006). The impact of learning styles on students grouping for collaborative learning: A case study. User Modeling and User-Adapted Interaction, 16(3), 377-401.
-  Zlatkovic, D., Denic, N., Petrovic, M., Ilic, M., Khorami, M., Safa, A., Wakil, K., Petkovic, D. & Vujacic, S. (2020). Analysys of adaptive e-Learning systems with adjustment of Felder-Silverman model in a Moodle DLS. Computer Applications in Engineering Education, 28(4), 803-813.
-  Felder, R.M. & Silverman, L. K. (1998). Learning and techning Styles in Engineering Education. Engineering Education, 78(7), 674-681.
-  Silverman, L. (2010). The Visual-Spatial Learner. Preventive School Failure, 34(1), 15-20.
-  Feldman, J., Monteserin, A. & Amandi, A. (2015). Automatic detection of learning styles: state of the art. Artif Intell Rev., 44(1), 157-186.
-  King, P. & Mason, A. B. (2020). Myers-Briggs Type Indicator. in The Wiley Encyclopedia of Personality and Individual Differences: Measurement and Assessment, Carducci, J., Nave, S. C., Mio, S. J. & Riggio, E. R. Eds., New Jersey, US, John Wiley & Sons Ltd. 315-319.
-  Felder, R. M. (2020). Option: Uses, Misuses, and Valitity of Learning Styles. Advanced in Engineering Education, 8,(1), 1-14.  Felder, R. M. (1998). Matters of Style. ASSE Prism, 6(4), 1-8.
-  Poon Teng Fatt, J. (2000). Understanding the learning styles of students: implications for educators. International Journal of Sociology and Social Policy, 20(11-12), 31-45.
-  Felder, R. M. & Soloman, B. A. (1997).Index of Learning Styles Questionarie,” 1997. [Online]. Available:https://www.webtools.ncsu.edu/learningstyles/. [Accessed 11 March 2022].
-  Felder R. M. & Spurlin, J. (2005). Applications, Reliability and Validity of the Index of Learning Styles. International Journal on Engineering Education, 21(1), 103-112.
-  Zywno, M. S.(2003). A Contribution to Validation of Score Meaning for Felder-Soloman’s Index of Learning Styles. in American Society for Engineering Education
Annual Conference & Exposition (2003 ELD/ASEE), Nashville, TN.
-  Zwanenberg, N., Wilkinson, L., & 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.
-  Volery, T. & Lord, D. (2000). Critical success factors in online education. Internal Journal of Educational Managment, 14, 216-223.
-  Maria, D., Britto, A. X. & Sagayaraj, S. (2015). A Framework to Formulate Adaptivity for Adaptive e-Learning System Using User Response Theory. I.J. Modern Education and Computer Science, 1(1), 23-30.