Abstract
An adaptive learning content which created specifically based on student learning style has drawn many research interests in the field of e-learning. Initially, the adaptive learning content is started in order to enhance the teaching and learning strategy based on student needs. The main objective of this paper is to map the proper learning content with the student learning style based on Felder Silverman learning style model. When the student logs into the learning management system, their learning style will be identified and the most appropriate learning content which fits their learning style will be automatically assigned to them. The motivation to this research is to enhance the learning performance of the student. A total of 150 students have voluntary participated in the experiments conducted in a laboratory environment. The results of the experiments indicate that when a proper learning material is presented to a student based on their learning styles, it improved their learning performance. The result reveals the system effectiveness for which it appears that the proposed approach may be promising.
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References
Ean Heng, L., Anbuselvan S., Pei Voon W., Manoranjitham M., Koo Yuen, P., Norazira A.J.: Personalizing learning management system using dimension two of felder silverman model. J. Solid State Technol. 63, 2754–2765 (2020)
Education Week: Taking stock of personalized learning. Education Week. Retrieved from http://www.edweek.org/ew/collections/personalized-learning-specialreport-2014/index.html (2014)
Bill & Melinda Gates Foundation: Early progress: Interim research on personalized learning. Retrieved from http://k12education.gatesfoundation.org/wpcontent/uploads/2015/06/Early-Progress-on-Personalized-Learning-Full-Report.pdf (2014)
United States Department of Education, Office of Educational Technology: Reimagining the role of technology in education: 2017 National Education Technology Plan update. Retrieved from https://tech.ed.gov/files/2017/01/NETP17.pdf (2017)
Ghallabi S, Essalmi F, Jemni M, Kinshuk: Toward the reuse of E-Learning personalization systems. IEEE 2013 Fourth International Conference on Information and Communication Technology and Accessibility (ICTA), 2013, pp. 3–5 (2013)
Keeffe, I.O., Staikopoulos, A., Rafter, R., Walsh, E., Yousuf, B., Conlan, O., Wade, V.: Personalized activity based eLearning. In: ACM Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies (2012)
Ghallabi, S., Essalmi, F., Jemni, M., Kinshuk: Toward the reuse of E-learning personalization systems. In: IEEE 2013 Fourth International Conference on Information and Communication Technology and Accessibility (ICTA), pp. 3–5 (2013)
Gamaleldin, S.A.: An intelligent eTutor-Student Adaptive Interaction Framework. ACM INTERACCION ’12 Proceedings of the 13th International Conference on Interacción Persona-Ordenador (2012)
Kodo Survey page. https://kodosurvey.com/blog/blooms-taxonomy-levels-learning-complete-post. Last accessed 2021/04/26
Enamul Hoque, M.: Three domains of learning: cognitive, affective and psychomotor. J. EFL Educ. Res. (JEFLER) 45–52 (2017)
Rytikova, I., Boicu, M.A.: Methodology for personalized competency-based learning in undergraduate courses. In: ACM 15th Annual Conference of Information Technology Education, pp. 81–86 (2014)
Junaid, Q.: Outcome-based (engineering) education (OBE): international accreditation practices. In: 127th ASEE Annual Conference (2020)
Sarwat, M., Norman, R.: The measurement of motivation with science students. Eur J Educ Res 3(3), 129–144 (2014)
Sfenrianto: Pendekatan Tipe Belajar Triple-Factor dalam Proses E-Learning Sebagai Basis Rekomendasi dan Personalisasi Pembelajaran (An Approach of triple-factor learning types in e-learning process as a basis for learning recommendation and personalization). Universitas Indonesia (2014)
Darwish, A.Y.: Learning style preferences of postgraduate students: the case of the British University in Dubai, the United Arab Emirates. J. Int. Educ. Bus. 291–309 (2018)
Bourkoukou, O., El Bachari, E., El Adnani, M.: A personalized e-learning based on recommender system. Int. J. Learn. Teach. 2, 99–103 (2016)
Musyarrof, A.F., et al.: Diversities of students learning style in discovery learning method and their ability in physic problem solving. Unnes Sci. Educ. J. (2020)
Miha, M.: Differences in learning styles: a comparison of Slovenian Universities. Procedia Soc. Behav. Sci. 197, 175–183 (2015)
Moushir, M.E.: Use of Felder and Silverman learning style model for online course design. Educ. Technol. Res. Dev. 67(3) (2018)
Ilango, S.: Blooms taxonomy—application in exam papers assessment. Int. J. Multidisc. Sci. Eng. 6(9), 5–8 (2015)
Moushir, M.E.: Use of Felder and Silverman Learning Style Model for Online Course Design. Association for Educational Communications and Technology (2018)
David, N., Sreenivas S., Tirumala, Nilufar, B.: Personalized learning: current status and potential. IEEE Conference on E-Learning, E-Management and E-Services (2014)
Theobald, C.: Changing our behaviours as teachers in order to meet the needs of our culturally diverse students: a thesis presented inpartial fulfilment of the requirements for the degree of master of education (teaching and learning) at Massey University, New Zealand. Ph.D. dissertation, Massey University, New Zealand (2013)
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Heng, L.E., Yuen, P.K., Fui, Y.T., Muniandy, M., Sangodiah, A., Ping, Y.Y. (2022). Adaptive Learning Content Based on Learning Styles in Learning Management System. In: Saeed, F., Al-Hadhrami, T., Mohammed, E., Al-Sarem, M. (eds) Advances on Smart and Soft Computing. Advances in Intelligent Systems and Computing, vol 1399. Springer, Singapore. https://doi.org/10.1007/978-981-16-5559-3_1
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