Enhancing Online Learning: Designing Adaptive and Personalised E-Learning System Design using VARK and Open-Source Applications
DOI:
https://doi.org/10.56919/usci.2432.025Keywords:
Adaptive learning system, Personalised learning, learning style, E-Learning, Open-SourceAbstract
Study’s Excerpt/Novelty
- This study pioneers the development of an adaptive e-learning platform that tailors educational content to individual learning preferences using VARK (Visual, Auditory, Read/Write, Kinesthetic) questions, optimizing material extraction through ontology-based methods.
- By integrating user-specific models that consider learning style theories and leveraging open-access resources, the system offers a personalized and efficient learning experience.
- The innovative approach of providing customized learning materials and incorporating real-time feedback mechanisms significantly advances the effectiveness of adaptive e-learning systems in diverse educational contexts.
Full Abstract
This study investigates the adaptability of e-learning platforms by developing a flexible learning system that evaluates users' learning preferences using VARK (Visual, Auditory, Read/Write, Kinesthetic) questions. The system extracts materials from freely accessible online sources, focusing on usability and diverse criteria. Optimisation with ontology enhances data extraction procedures. Various models are developed to incorporate adaptability and personalisation, notably, the learner's model, which integrates student needs and study domains based on learning style theories. The study significantly enhances adaptive e-learning systems by delivering personalised learning materials efficiently sourced from open-access apps and search engines. Evaluation and feedback mechanisms at the start of each session tailor the learning experience to individual styles, thereby improving learning effectiveness in diverse educational settings.
References
Acuna, G. E., Alvarez, L. A., Miraflores, J., & Samonte, M. J. (2021, June 4). Towards the Development of an Adaptive E-learning System with Chatbot Using Personalised E-learning Model. https://doi.org/10.1145/3473141.3473236
Aeiad, E., & Meziane, F. (2018, November 28). An adaptable and personalised E-learning system applied to computer science Programmes design. Education and Information Technologies. https://doi.org/10.1007/s10639-018-9836-x
Amin, S., Uddin, M. I., Alarood, A. A., Mashwani, W. K., Alzahrani, A., & Alzahrani, A. O. (2023). Smart E-Learning Framework for Personalised Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning. IEEE Access, 11, 89769–89790. https://doi.org/10.1109/access.2023.3305584
Chang, Y. C., Li, J. W., & Huang, D. Y. (2022). A Personalised Learning Service Compatible with Moodle E-Learning Management System. Applied Sciences (Switzerland), 12(7). https://doi.org/10.3390/app12073562
Diaz, F. S., Rubilar, T. P., Figueroa, C. C., & Silva, R. M. (2018, March). An Adaptive E-Learning Platform with VARK Learning Styles to Support the Learning of Object Orientation. 2018 IEEE World Engineering Education Conference (EDUNINE). https://doi.org/10.1109/edunine.2018.8450990
El-Sabagh, H. A. (2021). Adaptive e-learning environment based on learning styles and its impact on development students’ engagement. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00289-4
Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023, December 6). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences. https://doi.org/10.3390/educsci13121216
Lihua, B. (2021, July 1). Personalised adaptive online learning analysis model based on feature extraction and its implementation. Journal of Physics: Conference Series, 1982(1), 012180. https://doi.org/10.1088/1742-6596/1982/1/012180
Liu, R. (2024, May). Simulation of E-learning in English personalised learning recommendation system based on Markov chain algorithm and adaptive learning algorithm. Entertainment Computing, 100719. https://doi.org/10.1016/j.entcom.2024.100719
M.A.M Rishard, S.L Jayasekara, E.M.P.U Ekanayake, K.M.J.S Wickramathilake, Shyam Reyal, Kalpani Manathunga. Adaptivo: A Personalised Adaptive E-Learning System based on Learning Styles and Prior Knowledge. (2022, December 8). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/document/10007006
Maiia Marienko, Yulia Nosenko, Mariya Shyshkina. Papers with Code - Personalization of learning using adaptive technologies and augmented reality. (2020, November 8). https://cs.paperswithcode.com/paper/personalization-of-learning-using-adaptive
Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-Based Personalised E-Learning Systems: Issues, Challenges, and Solutions. IEEE Access, 10, 81323–81342. https://doi.org/10.1109/access.2022.3193938
Ristić, I., Runić-Ristić, M., Savić Tot, T., Tot, V., & Bajac, M. (2023). The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and Performance of Students. International Journal of Cognitive Research in Science, Engineering and Education, 11(1), 77–92. https://doi.org/10.23947/2334-8496-2023-11-1-77-92
Sabeima, M., Lamolle, M., & Nanne, M. F. (2022). Towards Personalised Adaptive Learning in e-Learning Recommender Systems. International Journal of Advanced Computer Science and Applications, 13(8). https://doi.org/10.14569/ijacsa.2022.0130803
Saunders, M., Lewis, P., & Thornhill, A. (2009, January 1). Research Methods for Business Students. Pearson Education. http://books.google.ie/books?id=u-txtfaCFiEC&pg=PA95&dq=0273716867&hl=&cd=1&source=gbs_api
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2015, July 15). Research Methods for Business Students PDF eBook. Pearson Higher Ed. http://books.google.ie/books?id=vUdOCgAAQBAJ&dq=1292016647&hl=&cd=1&source=gbs_api
Saunders, M., Lewis, P. and Thornhill, A. (2016) Research Methods for Business Students. 7th Edition, Pearson, Harlow. http://books.google.ie/books?id=vUdOCgAAQBAJ&dq=1292016620&hl=&cd=1&source=gbs_api
Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2022, June 29). AI-based adaptive personalised content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82(3), 3303–3333. https://doi.org/10.1007/s11042-022-13076-8
Soler Costa, R., Tan, Q., Pivot, F., Zhang, X., & Wang, H. (2021, September 14). Personalised and adaptive learning. Texto Livre: Linguagem E Tecnologia, 14(3), e33445. https://doi.org/10.35699/1983-3652.2021.33445
Taylor, D. L., Yeung, M., & Bashet, A. Z. (2021, January 1). Personalised and Adaptive Learning. Springer Briefs in Statistics. https://doi.org/10.1007/978-3-030-58948-6_2
Tsolis, D., Stamou, S., Christia, P., Kampana, S., Rapakoulia, T., Skouta, M., & Tsakalidis, A. (2010). An adaptive and personalised open-source e-learning platform. Procedia - Social and Behavioural Sciences, 9 (May 2014), 38–43. https://doi.org/10.1016/j.sbspro.2010.12.112
Vagale, V., Niedrite, L., & Ignatjeva, S. (2020). Implementation of Personalised Adaptive E-learning System. Baltic Journal of Modern Computing, 8(2). https://doi.org/10.22364/bjmc.2020.8.2.06
Xian, H., & Meng-Lewis, Y. (2018, March 19). Business Research Methods for Chinese Students. SAGE. http://books.google.ie/books?id=ObpBDwAAQBAJ&printsec=frontcover&dq=9781473926660&hl=&cd=1&source=gbs_api
Yi-Chun Chang, Jian-Wei Li, and De-Yao Huang (2022). A Personalised Learning Service Compatible with Moodle E-Learning Management System. Applied Sciences (Switzerland), 12(7). https://doi.org/10.3390/app12073562
Zikmund, W. G., Babin, B. J., Griffin, M., & Carr, J. C. (2010, January 1). Business Research Methods. Cengage Learning. http://books.google.ie/books?id=pexMPgAACAAJ&dq=1111826927&hl=&cd=1&source=gbs_api
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 UMYU Scientifica
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
&
