Apriori Algorithm-Based Learning Behavior Mining for Mobile Education Platforms.
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| Title: | Apriori Algorithm-Based Learning Behavior Mining for Mobile Education Platforms. |
|---|---|
| Authors: | Hong, Mei1 (AUTHOR), Alwadain, Ayed2 (AUTHOR) aalwadain@ksu.edu.sa, Alzahrani, Ahmed Ibrahim2 (AUTHOR) ahmed@ksu.edu.sa |
| Source: | Mobile Networks & Applications. Dec2025, Vol. 30 Issue 5/6, p1123-1135. 13p. |
| Subjects: | Apriori algorithm, Mobile apps in education, Resampling (Statistics), Acquisition of data, Data structures, Data mining, Ensemble learning |
| Abstract: | In order to promote learners' learning effectiveness and improve the accuracy of learning behavior mining, this paper conducts research on the Apriori algorithm based learning behavior mining on mobile education platforms. Firstly, web crawler technology is used to capture the behavioral information of learners during the learning process on the mobile education platform to construct learner profiles, and preprocess the sub-network set data of learning behaviors. Secondly, a Hash table is constructed to improve the Apriori algorithm to extract the learning behavior characteristics of learners on the mobile education platform. Then, a Stacking ensemble learning model is built to determine four base learners for model training. Finally, the Stacking ensemble learning model is improved with a chain rule, and conducting k-fold cross-validation to achieve data mining of learning behaviors on the mobile education platform. Comparative experiments have proven that when using the method proposed in this paper for data mining of learning behaviors on the mobile education platform, the normalized difference precision is always above 90%, the mAP value is always above 93%, the mining scope coverage rate is maintained above 90%, and the comprehensiveness is kept above 90%. This indicates that applying the method proposed in this paper to the data mining of learning behaviors on the mobile education platform can improve the accuracy of data mining and has a good mining effect. [ABSTRACT FROM AUTHOR] |
| Copyright of Mobile Networks & Applications is the property of Springer Nature 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194576086 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Apriori Algorithm-Based Learning Behavior Mining for Mobile Education Platforms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hong%2C+Mei%22">Hong, Mei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alwadain%2C+Ayed%22">Alwadain, Ayed</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> aalwadain@ksu.edu.sa</i><br /><searchLink fieldCode="AR" term="%22Alzahrani%2C+Ahmed+Ibrahim%22">Alzahrani, Ahmed Ibrahim</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> ahmed@ksu.edu.sa</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mobile+Networks+%26+Applications%22">Mobile Networks & Applications</searchLink>. Dec2025, Vol. 30 Issue 5/6, p1123-1135. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+apps+in+education%22">Mobile apps in education</searchLink><br /><searchLink fieldCode="DE" term="%22Resampling+%28Statistics%29%22">Resampling (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In order to promote learners' learning effectiveness and improve the accuracy of learning behavior mining, this paper conducts research on the Apriori algorithm based learning behavior mining on mobile education platforms. Firstly, web crawler technology is used to capture the behavioral information of learners during the learning process on the mobile education platform to construct learner profiles, and preprocess the sub-network set data of learning behaviors. Secondly, a Hash table is constructed to improve the Apriori algorithm to extract the learning behavior characteristics of learners on the mobile education platform. Then, a Stacking ensemble learning model is built to determine four base learners for model training. Finally, the Stacking ensemble learning model is improved with a chain rule, and conducting k-fold cross-validation to achieve data mining of learning behaviors on the mobile education platform. Comparative experiments have proven that when using the method proposed in this paper for data mining of learning behaviors on the mobile education platform, the normalized difference precision is always above 90%, the mAP value is always above 93%, the mining scope coverage rate is maintained above 90%, and the comprehensiveness is kept above 90%. This indicates that applying the method proposed in this paper to the data mining of learning behaviors on the mobile education platform can improve the accuracy of data mining and has a good mining effect. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Mobile Networks & Applications is the property of Springer Nature 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: 10.1007/s11036-024-02438-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1123 Subjects: – SubjectFull: Apriori algorithm Type: general – SubjectFull: Mobile apps in education Type: general – SubjectFull: Resampling (Statistics) Type: general – SubjectFull: Acquisition of data Type: general – SubjectFull: Data structures Type: general – SubjectFull: Data mining Type: general – SubjectFull: Ensemble learning Type: general Titles: – TitleFull: Apriori Algorithm-Based Learning Behavior Mining for Mobile Education Platforms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hong, Mei – PersonEntity: Name: NameFull: Alwadain, Ayed – PersonEntity: Name: NameFull: Alzahrani, Ahmed Ibrahim IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1383469X Numbering: – Type: volume Value: 30 – Type: issue Value: 5/6 Titles: – TitleFull: Mobile Networks & Applications Type: main |
| ResultId | 1 |
