Enhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies.
Saved in:
| 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] |
| Copyright of Eurasian Journal of Educational Research (EJER) is the property of Eurasian Journal of Educational Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: ehh DbLabel: Education Research Complete An: 182541298 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Enhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chunmao+Liu%22">Chunmao Liu</searchLink><relatesTo>1</relatesTo><i> liuchunmao@hnpi.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tuntiwongwanich%2C+Somkiat%22">Tuntiwongwanich, Somkiat</searchLink><relatesTo>1</relatesTo><i> somkiat.tu@kmitl.ac.th</i><br /><searchLink fieldCode="AR" term="%22kantathanawat%2C+Thiyaporn%22">kantathanawat, Thiyaporn</searchLink><relatesTo>1</relatesTo><i> thiyaporn.ka@kmitl.ac.th</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Eurasian+Journal+of+Educational+Research+%28EJER%29%22">Eurasian Journal of Educational Research (EJER)</searchLink>. 2024, Issue 112, p362-377. 16p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Integrated+learning+systems%22">Integrated learning systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Digital+learning%22">Digital learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Eurasian Journal of Educational Research (EJER) is the property of Eurasian Journal of Educational Research and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=182541298 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.14689/ejer.2024.112.020 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 362 Subjects: – SubjectFull: Instructional systems Type: general – SubjectFull: Integrated learning systems Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Digital learning Type: general – SubjectFull: Online education Type: general – SubjectFull: Recommender systems Type: general Titles: – TitleFull: Enhancing Personalized Learning in Online Education: The Impact of Adaptive Learning Systems and Recommendation Technologies. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chunmao Liu – PersonEntity: Name: NameFull: Tuntiwongwanich, Somkiat – PersonEntity: Name: NameFull: kantathanawat, Thiyaporn IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1302597X Numbering: – Type: issue Value: 112 Titles: – TitleFull: Eurasian Journal of Educational Research (EJER) Type: main |
| ResultId | 1 |
