Enhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies

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dc.contributor.authorLiu, Chunmao
dc.contributor.authorTuntiwongwanich, Somkiat
dc.contributor.authorKantathanawat, Thiyaporn
dc.date.accessioned2026-08-06T10:43:17Z
dc.date.available2026-08-06T10:43:17Z
dc.date.issued2024-01-01
dc.description.abstractThe study investigates the impact of integrated adaptive learning systems and recommender technologies on the improvement of online education. A component-level quantitative evaluation was conducted, which involved measuring user interaction, content applicability, knowledge acquisition, and system usability, with support from surveys and interviews. The findings indicate that recommendation systems enhance active user participation, content relevance, and learning outcomes, while maintaining high usability rates that positively influence learners’ perceptions. However, certain limitations were identified, including the system’s less-than-ideal suitability for advanced learners and the absence of contextual information. The study concludes that, when appropriately implemented as suggested by existing literature, adaptive learning systems possess significant potential to transform online education by offering personalised and efficient learning methods. Recommendations for future developments include the integration of third-generation machine learning, ensuring equal opportunities for learners, and further refining the system to address small learner differences.
dc.identifier.citationEurasian Journal of Educational Research, 2024(112), 362-377, 2024
dc.identifier.doi10.14689/ejer.2024.112.020
dc.identifier.issn1302597X
dc.identifier.other2-s2.0-85216394827
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14891
dc.sourceEurasian Journal of Educational Research
dc.subjectAdaptive Learning
dc.subjectE-Learning
dc.subjectEdtech
dc.subjectLearner Interaction
dc.subjectLearning Achievements
dc.titleEnhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies
dc.typeArticle

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