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

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Bibliographic Details
Title:	Enhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies.
Authors:	Chunmao Liu¹ liuchunmao@hnpi.edu.cn, Tuntiwongwanich, Somkiat¹ somkiat.tu@kmitl.ac.th, kantathanawat, Thiyaporn¹ thiyaporn.ka@kmitl.ac.th
Source:	Eurasian Journal of Educational Research (EJER). 2024, Issue 112, p362-377. 16p.
Subject Terms:	Instructional systems, Integrated learning systems, Educational technology, Digital learning, *Online education, Recommender systems
Abstract:	The 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. [ABSTRACT FROM AUTHOR]
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Database:	Education Research Complete

Description
Abstract:	The 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. [ABSTRACT FROM AUTHOR]
ISSN:	1302597X
DOI:	10.14689/ejer.2024.112.020