Enhancing user experience in e-learning systems: A new user-centric RESTful web services approach
The development of e-learning platforms often focuses on designing system requirements rather than users’ functional requirements, which negatively impacts user behavior, interaction, and the overall e-learning experience. This paper aims to bridge that gap by introducing a new approach to designing and developing e-learning systems based on user-centric design principles. Specifically, it proposes a user-centric web services architecture for e-learning systems. The approach involves designing RESTful web service APIs tailored to the specific functionalities required by various e-learning users, including learners, tutors, teachers, and administrators. The effectiveness of the proposed approach is demonstrated through a comprehensive overview of the implementation process of an e-learning prototype built using the Django REST framework. The e-learning system's user experience was rigorously evaluated using the well-known User Experience Questionnaire (UEQ). Evaluation results revealed high performance across all user experience dimensions, surpassing standard benchmarks, particularly in efficiency and attractiveness scales. The proposed user-centric RESTful web services approach contributes to improving the design and development of e-learning systems, offering a foundation for future exploration of microservices architecture and AI integration to further personalize the learning experience and enhance system performance.
Authors
- Tarek Boutefara (ORCID: https://orcid.org/0000-0002-7222-9387)
- Belkacem Mostefai
- Sahraoui Dhelim (ORCID: https://orcid.org/0000-0002-3620-1395)
- Nabila Bousbia
- Amar Balla
- A.B.D.E.R.R.A.H.M.A.N.E. HAMMIA
Institutions
- University College Dublin (IE)
- University of Jijel (DZ)
- Higher National Veterinary School (DZ)
- Ziane Achour University of Djelfa (DZ)
- Universitat Rovira i Virgili (ES)
Publication Details
- Journal
- Computers in Human Behavior Reports
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1016/j.chbr.2025.100643
- Citations
- 4
- Primary Topic
- E-Learning and Knowledge Management
- Type
- article
- Field-Weighted Citation Impact
- 6.77