A Swarm Intelligence Framework for Mobile Collaborative Learning.
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| Title: | A Swarm Intelligence Framework for Mobile Collaborative Learning. |
|---|---|
| Authors: | Sun, Junju1 sunjunju@xyvtc.edu.cn, Mei, Yan1 meiyan@xyvtc.edu.cn |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 12, p4-18. 15p. |
| Subjects: | Swarm intelligence, Mobile learning, Collaborative learning, Particle swarm optimization, Synchronization, Educational planning, Distributed computing |
| Abstract: | The digital transformation of education driven by mobile technologies urgently requires bridging the collaborative gap between classroom and extracurricular learning. Existing systems face critical challenges, including fragmented applications of swarm intelligence, insufficient depth of mobile interaction collaboration, and limited evaluation frameworks. To address these issues, this study proposes an integrated classroom-extracurricular digital learning ecosystem that combines swarm intelligence with mobile interaction collaboration mechanisms. The system adopts a three-tier cloud-edge-mobile distributed architecture and introduces four intelligent modules optimized for mobile scenarios: (1) a dynamic learner profiling module based on an improved particle swarm optimization (PSO) algorithm, (2) a cross-scenario learning path generation module driven by hybrid swarm intelligence, (3) a decentralized collaborative scheduling module supported by a lightweight distributed consensus algorithm, and (4) a collective knowledge evolution graph construction module driven by mobile data. Through multimodal mobile data collection and edge intelligence deployment, the system achieves low-latency, low-power, and precise collaborative learning. To evaluate system performance, a multidimensional assessment framework encompassing both mobile technology performance and learning collaboration effectiveness was designed. A controlled experiment involving 200 university students simulated real-world conditions, including network fluctuations and heterogeneous devices. The proposed system outperformed the control system in both technological performance and collaborative learning effectiveness: under normal network conditions, average response latency decreased by 41.8%, mobile energy consumption decreased by 31.2%, and task completion rate in low-bandwidth scenarios increased by 39.7%. In terms of learning collaboration, cross-scenario task completion improved by 32.7% and collaborative report scores increased by 17.5%. Limitations include adaptation to low-end devices, privacy protection, and insufficient validation in complex outdoor scenarios. This study provides both theoretical support and practical paradigms for the technological innovation and large-scale deployment of mobile learning ecosystems while expanding the application boundaries of swarm intelligence in resource-constrained mobile environments. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194824135 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Swarm Intelligence Framework for Mobile Collaborative Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sun%2C+Junju%22">Sun, Junju</searchLink><relatesTo>1</relatesTo><i> sunjunju@xyvtc.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Mei%2C+Yan%22">Mei, Yan</searchLink><relatesTo>1</relatesTo><i> meiyan@xyvtc.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 12, p4-18. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Swarm+intelligence%22">Swarm intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Collaborative+learning%22">Collaborative learning</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Synchronization%22">Synchronization</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+planning%22">Educational planning</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The digital transformation of education driven by mobile technologies urgently requires bridging the collaborative gap between classroom and extracurricular learning. Existing systems face critical challenges, including fragmented applications of swarm intelligence, insufficient depth of mobile interaction collaboration, and limited evaluation frameworks. To address these issues, this study proposes an integrated classroom-extracurricular digital learning ecosystem that combines swarm intelligence with mobile interaction collaboration mechanisms. The system adopts a three-tier cloud-edge-mobile distributed architecture and introduces four intelligent modules optimized for mobile scenarios: (1) a dynamic learner profiling module based on an improved particle swarm optimization (PSO) algorithm, (2) a cross-scenario learning path generation module driven by hybrid swarm intelligence, (3) a decentralized collaborative scheduling module supported by a lightweight distributed consensus algorithm, and (4) a collective knowledge evolution graph construction module driven by mobile data. Through multimodal mobile data collection and edge intelligence deployment, the system achieves low-latency, low-power, and precise collaborative learning. To evaluate system performance, a multidimensional assessment framework encompassing both mobile technology performance and learning collaboration effectiveness was designed. A controlled experiment involving 200 university students simulated real-world conditions, including network fluctuations and heterogeneous devices. The proposed system outperformed the control system in both technological performance and collaborative learning effectiveness: under normal network conditions, average response latency decreased by 41.8%, mobile energy consumption decreased by 31.2%, and task completion rate in low-bandwidth scenarios increased by 39.7%. In terms of learning collaboration, cross-scenario task completion improved by 32.7% and collaborative report scores increased by 17.5%. Limitations include adaptation to low-end devices, privacy protection, and insufficient validation in complex outdoor scenarios. This study provides both theoretical support and practical paradigms for the technological innovation and large-scale deployment of mobile learning ecosystems while expanding the application boundaries of swarm intelligence in resource-constrained mobile environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i12.62260 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 4 Subjects: – SubjectFull: Swarm intelligence Type: general – SubjectFull: Mobile learning Type: general – SubjectFull: Collaborative learning Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Synchronization Type: general – SubjectFull: Educational planning Type: general – SubjectFull: Distributed computing Type: general Titles: – TitleFull: A Swarm Intelligence Framework for Mobile Collaborative Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sun, Junju – PersonEntity: Name: NameFull: Mei, Yan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 06 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 12 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
| ResultId | 1 |
