Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3251
Title: Adaptivo: A Personalized Adaptive E-Learning System based on Learning Styles and Prior Knowledge
Authors: Rishard, M.A.M
Jayasekara, S.L
Ekanayake, E.M.P.U
Wickramathilake, K.M.J.S
Reyal, S
Manathunga, K
Wickramarathne, J
Keywords: Adaptivo
Personalized Adaptive
E-Learning System
Learning Styles
Prior Knowledge
Issue Date: 9-Dec-2022
Publisher: IEEE
Citation: M. A. M. Rishard et al., "Adaptivo: A Personalized Adaptive E-Learning System based on Learning Styles and Prior Knowledge," 2022 Seventh International Conference on Informatics and Computing (ICIC), Denpasar, Bali, Indonesia, 2022, pp. 1-9, doi: 10.1109/ICIC56845.2022.10007006.
Series/Report no.: 2022 Seventh International Conference on Informatics and Computing (ICIC);
Abstract: The rapid advancement of technology and the internet has resulted in an increase in the number of learners seeking e-learning. Though E-Learning is widely used most e-learning systems provide the same set of learning resources and learning paths to each student, regardless of their personal preferences. In recent years there has been increasing attention towards the characteristics of learners such as the learning styles and the knowledge level of the learner. This research paper proposes a personalized adaptive E-learning system called “Adaptivo” that provides a personalized learning experience to the learners based on their learning style and knowledge level. To make the learning process more efficient and engaging, Adaptivo takes into account the specific differences between learners in terms of time, online interactions and learning duration. It then builds a personalized learning path depending on each learner's learning style and knowledge level. The main aim of this study is to investigate the impact of the proposed adaptive learning approach on learners. The results show that the students appreciate the approach, are highly satisfied, and performed better when content is personalized according to their learning style and prior knowledge.
URI: https://rda.sliit.lk/handle/123456789/3251
ISBN: 979-8-3503-4571-1
Appears in Collections:Department of Computer Science and Software Engineering
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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