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Research on Adaptive Learning Mode of Folk Education Driven by Big Data.

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Τίτλος: Research on Adaptive Learning Mode of Folk Education Driven by Big Data.
Συγγραφείς: Qin, Yimeng1 (AUTHOR) qinym8199@126.com, Mo, Shanyun2 (AUTHOR) 13558328199@163.com
Πηγή: International Journal of High Speed Electronics & Systems. Sep2026, Vol. 35 Issue 4, p1-25. 25p.
Θεματικοί όροι: *Big data, *Resource allocation, Individualized instruction, Long short-term memory, Apriori algorithm, Nonformal education, Instructional systems, Cultural transmission
Περίληψη: In response to the problems of low resource utilization, short learning time and poor test scores in the adaptive learning mode of folk education, this study proposes a big data-driven design method aimed at improving the efficiency and effectiveness of folk education, while exploring its potential applications in personalized education and cultural inheritance. By improving the Apriori algorithm to mine folk education data, combined with stepwise regression analysis to complete data statistics and using long short-term memory networks to predict learners' levels. On this basis, a personalized resource recommendation model including entity embedding, graph attention, learner interest modeling and DDDQN module is constructed and an adaptive learning mode is designed. The experimental results show that this method can significantly improve the utilization rate of folk education resources to 98.2%, with an average learning time of 67.3 h and an average test score of 86.2 points, proving that it not only optimizes the learning process, but also has the potential to be widely applied in practical educational environments and cultural inheritance. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company 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.)
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  Data: Research on Adaptive Learning Mode of Folk Education Driven by Big Data.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+High+Speed+Electronics+%26+Systems%22">International Journal of High Speed Electronics & Systems</searchLink>. Sep2026, Vol. 35 Issue 4, p1-25. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Big+data%22">Big data</searchLink><br />*<searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+instruction%22">Individualized instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Apriori+algorithm%22">Apriori algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Nonformal+education%22">Nonformal education</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Cultural+transmission%22">Cultural transmission</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: In response to the problems of low resource utilization, short learning time and poor test scores in the adaptive learning mode of folk education, this study proposes a big data-driven design method aimed at improving the efficiency and effectiveness of folk education, while exploring its potential applications in personalized education and cultural inheritance. By improving the Apriori algorithm to mine folk education data, combined with stepwise regression analysis to complete data statistics and using long short-term memory networks to predict learners' levels. On this basis, a personalized resource recommendation model including entity embedding, graph attention, learner interest modeling and DDDQN module is constructed and an adaptive learning mode is designed. The experimental results show that this method can significantly improve the utilization rate of folk education resources to 98.2%, with an average learning time of 67.3 h and an average test score of 86.2 points, proving that it not only optimizes the learning process, but also has the potential to be widely applied in practical educational environments and cultural inheritance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company 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:
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    Identifiers:
      – Type: doi
        Value: 10.1142/S0129156425404590
    Languages:
      – Code: eng
        Text: English
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        PageCount: 25
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      – SubjectFull: Big data
        Type: general
      – SubjectFull: Resource allocation
        Type: general
      – SubjectFull: Individualized instruction
        Type: general
      – SubjectFull: Long short-term memory
        Type: general
      – SubjectFull: Apriori algorithm
        Type: general
      – SubjectFull: Nonformal education
        Type: general
      – SubjectFull: Instructional systems
        Type: general
      – SubjectFull: Cultural transmission
        Type: general
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      – TitleFull: Research on Adaptive Learning Mode of Folk Education Driven by Big Data.
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            NameFull: Qin, Yimeng
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            NameFull: Mo, Shanyun
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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              Value: 35
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            – TitleFull: International Journal of High Speed Electronics & Systems
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