Content Recommendation E-learning System for Personalized Learners to Enhance User Experience using SCORM
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
(This article belongs to the Special Issue on SP7 (Special Issue on Multidisciplinary Sciences and Advanced Technology (SI-MSAT 2025)) and the Section Information Systems – Computer Science (ISC))
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Udugahapattuwa, P. and 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
Pasindu Udugahapattuwa and Shantha Fernando. "Content Recommendation E-learning System for Personalized Learners to Enhance User Experience using SCORM." Journal of Engineering Research and Sciences 4, no. 9 (September 2025): 30–46. https://doi.org/10.55708/js0409004
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.
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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