View in EDS HTML Full Text PDF Full Text

Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours.

Saved in:
Bibliographic Details
Title: Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours.
Authors: Song, Wei1 (AUTHOR) songwei@ncut.edu.cn, Zhang, Qihao1 (AUTHOR), Fong, Simon2 (AUTHOR), Li, Tengyue1 (AUTHOR)
Source: Expert Systems. May2025, Vol. 42 Issue 5, p1-18. 18p.
Subjects: Massive open online courses, Sequential learning, Recommender systems, Prior learning, Educational outcomes
Abstract: Learning path recommendation is crucial for guiding learners through a series of courses in a logical sequence based on their previous learning experiences. This is particularly important for improving learning outcomes in massive open online courses (MOOCs) for diverse learners. Because both the historical learning courses and recommended learning paths can be represented as sequential patterns (SPs); it is reasonable to approach this problem through SP mining (SPM). In addition to support, we incorporate three factors, that is, course learning days, grades and engagement, to model frequent high‐utility SPs (FHUSPs). When recommending a learning path, FHUSPs that align with the target user's learning history and are common among successful learners, while rare among less successful ones, are prioritised. If there are insufficient matching FHUSPs, we address this by recommending additional courses based on the joint competency and complementarity of learners similar to the target learner. Experimental results on a real‐world dataset demonstrate that our method provides highly accurate and relevant recommendations. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems is the property of Wiley-Blackwell 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
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 184494880
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Song%2C+Wei%22">Song, Wei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> songwei@ncut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Qihao%22">Zhang, Qihao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fong%2C+Simon%22">Fong, Simon</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Tengyue%22">Li, Tengyue</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Expert+Systems%22">Expert Systems</searchLink>. May2025, Vol. 42 Issue 5, p1-18. 18p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Massive+open+online+courses%22">Massive open online courses</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+learning%22">Sequential learning</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+learning%22">Prior learning</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+outcomes%22">Educational outcomes</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Learning path recommendation is crucial for guiding learners through a series of courses in a logical sequence based on their previous learning experiences. This is particularly important for improving learning outcomes in massive open online courses (MOOCs) for diverse learners. Because both the historical learning courses and recommended learning paths can be represented as sequential patterns (SPs); it is reasonable to approach this problem through SP mining (SPM). In addition to support, we incorporate three factors, that is, course learning days, grades and engagement, to model frequent high‐utility SPs (FHUSPs). When recommending a learning path, FHUSPs that align with the target user's learning history and are common among successful learners, while rare among less successful ones, are prioritised. If there are insufficient matching FHUSPs, we address this by recommending additional courses based on the joint competency and complementarity of learners similar to the target learner. Experimental results on a real‐world dataset demonstrate that our method provides highly accurate and relevant recommendations. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Expert Systems is the property of Wiley-Blackwell 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=egs&AN=184494880
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/exsy.70034
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 1
    Subjects:
      – SubjectFull: Massive open online courses
        Type: general
      – SubjectFull: Sequential learning
        Type: general
      – SubjectFull: Recommender systems
        Type: general
      – SubjectFull: Prior learning
        Type: general
      – SubjectFull: Educational outcomes
        Type: general
    Titles:
      – TitleFull: Recommendation of Learning Resources for MOOCs Based on Historical Sequential Behaviours.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Song, Wei
      – PersonEntity:
          Name:
            NameFull: Zhang, Qihao
      – PersonEntity:
          Name:
            NameFull: Fong, Simon
      – PersonEntity:
          Name:
            NameFull: Li, Tengyue
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 05
              Text: May2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 02664720
          Numbering:
            – Type: volume
              Value: 42
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Expert Systems
              Type: main
ResultId 1