Mobile Interaction Behavior Analysis for Optimizing Learning Engagement Prediction in Higher Education.
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| Title: | Mobile Interaction Behavior Analysis for Optimizing Learning Engagement Prediction in Higher Education. |
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| Authors: | Mengnan Li1 limengnan790929@163.com |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 16, p104-117. 14p. |
| Subjects: | Mobile learning, Student engagement, Transformer models, Intelligent tutoring systems, Higher education, Multisensor data fusion, Behavioral assessment |
| Abstract: | Mobile terminals serve as the primary carriers of self-directed mobile learning among university students. Interaction data generated from these terminals can authentically reflect learners' dynamic engagement levels. Existing research, however, inadequately accommodates the non-uniform temporal characteristics of behavioral sequences, struggles to capture long-range dependencies, suffers from poor fusion quality of heterogeneous features, and exhibits limited model interpretability--thereby hindering practical deployment in instructional intervention contexts. To address these issues, a unified framework for behavioral analysis and engagement prediction was proposed. Relying on non-intrusive sensing techniques, multimodal learning behavior data were collected, and learning engagement was quantified through the fusion of subjective and objective evaluation metrics. Temporal behavioral topological associations were uncovered by constructing horizontal visibility graphs integrated with a graph attention network. A cross-modal attention mechanism was employed to adaptively fuse multi-source heterogeneous features, while multitask learning was introduced to enhance model generalization, enabling accurate prediction of learning engagement trajectories. Furthermore, a gradient-weighted attribution strategy was incorporated to identify key influencing factors, and a mobile intervention mechanism was designed to optimize learners' behavioral states. This study provides a technical reference for mobile learning behavior mining, learning state prediction, and the development of intelligent educational applications. [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: 196325382 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mobile Interaction Behavior Analysis for Optimizing Learning Engagement Prediction in Higher Education. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mengnan+Li%22">Mengnan Li</searchLink><relatesTo>1</relatesTo><i> limengnan790929@163.com</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 16, p104-117. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Mobile+learning%22">Mobile learning</searchLink><br /><searchLink fieldCode="DE" term="%22Student+engagement%22">Student engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Transformer+models%22">Transformer models</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mobile terminals serve as the primary carriers of self-directed mobile learning among university students. Interaction data generated from these terminals can authentically reflect learners' dynamic engagement levels. Existing research, however, inadequately accommodates the non-uniform temporal characteristics of behavioral sequences, struggles to capture long-range dependencies, suffers from poor fusion quality of heterogeneous features, and exhibits limited model interpretability--thereby hindering practical deployment in instructional intervention contexts. To address these issues, a unified framework for behavioral analysis and engagement prediction was proposed. Relying on non-intrusive sensing techniques, multimodal learning behavior data were collected, and learning engagement was quantified through the fusion of subjective and objective evaluation metrics. Temporal behavioral topological associations were uncovered by constructing horizontal visibility graphs integrated with a graph attention network. A cross-modal attention mechanism was employed to adaptively fuse multi-source heterogeneous features, while multitask learning was introduced to enhance model generalization, enabling accurate prediction of learning engagement trajectories. Furthermore, a gradient-weighted attribution strategy was incorporated to identify key influencing factors, and a mobile intervention mechanism was designed to optimize learners' behavioral states. This study provides a technical reference for mobile learning behavior mining, learning state prediction, and the development of intelligent educational applications. [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.v20i16.62749 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 104 Subjects: – SubjectFull: Mobile learning Type: general – SubjectFull: Student engagement Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Intelligent tutoring systems Type: general – SubjectFull: Higher education Type: general – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Behavioral assessment Type: general Titles: – TitleFull: Mobile Interaction Behavior Analysis for Optimizing Learning Engagement Prediction in Higher Education. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mengnan Li IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 16 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
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