Academic Journal
Προβολή σε EDS

Optimizing Formative Assessment with Learning Analytics.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Optimizing Formative Assessment with Learning Analytics.
Συγγραφείς: Banihashem, Seyyed Kazem, Gašević, Dragan, Noroozi, Omid, Jarodzka, Halszka, Joosten-ten Brinke, Desiree, Drachsler, Hendrik
Πηγή: Review of Educational Research; Oct2026, Vol. 96 Issue 5, p1925-1973, 49p
Θεματικοί όροι: Learning analytics, Formative evaluation, Academic support programs, Educational technology, Science education, Peer review of students, Evidence synthesis, Self-evaluation
Περίληψη: Formative assessment is a key element of effective education. Recently, a growing body of evidence has highlighted the potential of learning analytics to support formative assessment. However, this evidence is sparse, and concerns have been raised about the connection between learning analytics results and formative assessment models. When learning analytics results do not align well with formative assessment models, teachers may be hesitant to place trust in, interpret, and act upon the insights provided by learning analytics for formative assessment. In this study, we address this concern by adopting a well-established formative assessment model and conducting a systematic review to explore how learning analytics can support formative assessment. A total of 93 relevant articles, published between 2011 and 2023 and retrieved from Web of Science (WOS), Scopus, IEEE Xplore, and ACM, were analyzed using a coding scheme grounded in a formative assessment model. Drawing on the model of formative assessment, we report how learning analytics can support formative assessment by connecting the three key actors, teachers, students as peers, and students as self-assessors with the three critical stages of the process: (a) where the learner is going, (b) where the learner is now, and (c) how the learner is getting there. This study extends our understanding of the alignment between learning analytics results and theoretical concepts and further contributes to the advancement of the application of learning analytics for formative assessment. [ABSTRACT FROM AUTHOR]
Copyright of Review of Educational Research is the property of Sage Publications Inc. 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.)
Βάση Δεδομένων: Complementary Index
FullText Links:
  – Type: other
Text:
  Availability: 0
CustomLinks:
  – Url: http://www.jstor.org/openurl?issn=00346543&date=2026&volume=96&issue=5&spage=1925
    Name: JSTOR (all content) (s7799221)
    Category: fullText
    Text:
    Icon: https://imageserver.ebscohost.com/branding/jstor/iconJSTOR.gif
    MouseOverText: Full Text from JSTOR
Header DbId: edb
DbLabel: Complementary Index
An: 197008900
RelevancyScore: 1082
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1082.43029785156
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optimizing Formative Assessment with Learning Analytics.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Banihashem%2C+Seyyed+Kazem%22">Banihashem, Seyyed Kazem</searchLink><br /><searchLink fieldCode="AR" term="%22Gašević%2C+Dragan%22">Gašević, Dragan</searchLink><br /><searchLink fieldCode="AR" term="%22Noroozi%2C+Omid%22">Noroozi, Omid</searchLink><br /><searchLink fieldCode="AR" term="%22Jarodzka%2C+Halszka%22">Jarodzka, Halszka</searchLink><br /><searchLink fieldCode="AR" term="%22Joosten-ten+Brinke%2C+Desiree%22">Joosten-ten Brinke, Desiree</searchLink><br /><searchLink fieldCode="AR" term="%22Drachsler%2C+Hendrik%22">Drachsler, Hendrik</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Review of Educational Research; Oct2026, Vol. 96 Issue 5, p1925-1973, 49p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Formative+evaluation%22">Formative evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+support+programs%22">Academic support programs</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br /><searchLink fieldCode="DE" term="%22Science+education%22">Science education</searchLink><br /><searchLink fieldCode="DE" term="%22Peer+review+of+students%22">Peer review of students</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+synthesis%22">Evidence synthesis</searchLink><br /><searchLink fieldCode="DE" term="%22Self-evaluation%22">Self-evaluation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Formative assessment is a key element of effective education. Recently, a growing body of evidence has highlighted the potential of learning analytics to support formative assessment. However, this evidence is sparse, and concerns have been raised about the connection between learning analytics results and formative assessment models. When learning analytics results do not align well with formative assessment models, teachers may be hesitant to place trust in, interpret, and act upon the insights provided by learning analytics for formative assessment. In this study, we address this concern by adopting a well-established formative assessment model and conducting a systematic review to explore how learning analytics can support formative assessment. A total of 93 relevant articles, published between 2011 and 2023 and retrieved from Web of Science (WOS), Scopus, IEEE Xplore, and ACM, were analyzed using a coding scheme grounded in a formative assessment model. Drawing on the model of formative assessment, we report how learning analytics can support formative assessment by connecting the three key actors, teachers, students as peers, and students as self-assessors with the three critical stages of the process: (a) where the learner is going, (b) where the learner is now, and (c) how the learner is getting there. This study extends our understanding of the alignment between learning analytics results and theoretical concepts and further contributes to the advancement of the application of learning analytics for formative assessment. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Review of Educational Research is the property of Sage Publications Inc. 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=edb&AN=197008900
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3102/00346543251370753
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 49
        StartPage: 1925
    Subjects:
      – SubjectFull: Learning analytics
        Type: general
      – SubjectFull: Formative evaluation
        Type: general
      – SubjectFull: Academic support programs
        Type: general
      – SubjectFull: Educational technology
        Type: general
      – SubjectFull: Science education
        Type: general
      – SubjectFull: Peer review of students
        Type: general
      – SubjectFull: Evidence synthesis
        Type: general
      – SubjectFull: Self-evaluation
        Type: general
    Titles:
      – TitleFull: Optimizing Formative Assessment with Learning Analytics.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Banihashem, Seyyed Kazem
      – PersonEntity:
          Name:
            NameFull: Gašević, Dragan
      – PersonEntity:
          Name:
            NameFull: Noroozi, Omid
      – PersonEntity:
          Name:
            NameFull: Jarodzka, Halszka
      – PersonEntity:
          Name:
            NameFull: Joosten-ten Brinke, Desiree
      – PersonEntity:
          Name:
            NameFull: Drachsler, Hendrik
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 10
              Text: Oct2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 00346543
          Numbering:
            – Type: volume
              Value: 96
            – Type: issue
              Value: 5
          Titles:
            – TitleFull: Review of Educational Research
              Type: main
ResultId 1