- Open Access
- Article
Content Recommendation E-learning System for Personalized Learners to Enhance User Experience using SCORM
by Pasindu Udugahapattuwa 1,* 
and Shantha Fernando 2
1 Department of Electrical, Electronic and Telecommunication Engineering, General Sir John Kotelawala Defence University, Sri Lanka
2 Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 4, Issue 9, Page # 30-46, 2025; DOI: 10.55708/js0409004
Keywords: E-learning, Personalized Content Recommendation, User Experience, SCORM
Received: 03 July 2025, Revised: 31 August 2025, Accepted: 03 September 2025, Published Online: 19 September 2025
(This article belongs to the Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2025) & Section Information Systems – Computer Science (ISC))
APA Style
Udugahapattuwa, P., & Fernando, S. (2025). Content recommendation E-learning system for personalized learners to enhance user experience using SCORM. Journal of Engineering Research and Sciences, 4(9), 30–46. https://doi.org/10.55708/js0409004
Chicago/Turabian Style
Udugahapattuwa, Pasindu, and Shantha Fernando. 2025. “Content Recommendation E-learning System for Personalized Learners to Enhance User Experience using SCORM.” Journal of Engineering Research and Sciences 4, no. 9: 30–46. https://doi.org/10.55708/js0409004
IEEE Style
P. Udugahapattuwa and S. Fernando, “Content Recommendation E-learning System for Personalized Learners to Enhance User Experience using SCORM,” Journal of Engineering Research and Sciences, vol. 4, no. 9, pp. 30-46, Sep. 2025, doi: 10.55708/js0409004.
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Abstract
E-learning is a main field used to improve learners’ learning environment. It would be more useful if the E-learning systems were improved by getting interactions and focusing on user experience. This research suggests increasing the user experience of students towards E-learning environments by recommending content according to their preferences. This research aims to make personalized content recommendations by identifying user interactions, trends, and patterns. Finally, this research provides a model that could help to create an intelligent E-learning system. Then the student engagement towards E-learning and user performance level can be enhanced using this research. After developing the model, there is a 73.99% accuracy in initial training and 63.16% accuracy in initial testing. After retraining and retesting, there was 85.58% accuracy for retraining and 78.90% accuracy for retesting.
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References
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