View in EDS

A Bibliographic Analysis of Digital Learning Platforms as Research Infrastructure

Saved in:
Bibliographic Details
Title: A Bibliographic Analysis of Digital Learning Platforms as Research Infrastructure
Language: English
Authors: Zak Risha, Jeremy Roschelle, Digital Promise, Empirical Education Inc., Institute of Education Sciences (ED)
Source: Digital Promise. 2026.
Availability: Digital Promise. 1001 Connecticut Avenue NW Suite 935, Washington DC 20036. Tel: 202-450-3675; e-mail: contact@digitalpromise.org; Web site: https://digitalpromise.org/
Peer Reviewed: N
Page Count: 16
Publication Date: 2026
Document Type: Information Analyses
Descriptors: Electronic Learning, Educational Research, Educational Technology, Technology Uses in Education
Abstract: Modern learning at scale relies on data at scale. Methods for using learning platform data have been evolving, particularly to allow comparative or experimental research within existing digital learning platforms (DLPs). By providing researchers with large-scale data from active learners, DLPs can reduce costs to collect data, pilot new interventions, and run A/B comparisons, enabling Learning Engineering to yield improvements that benefit student learning. This potential has given rise to an emerging DLPs-as-research-infrastructure subfield, with support from multiple funders. Progress depends on community-building and collaboration, as strong relationships are necessary to develop sound methods and approaches as well as to utilize the available data well. This subfield does not yet have a dedicated journal, conference, or scholarly society that serves a broader community. And yet the DLP-as-research-infrastructure subfield is aware of broader connections and precedents for its work. To understand these connections and precedents, a bibliometric analysis was conducted. Reporting on the bibliometric analysis yields two contributions: first, the immediate findings can advance the work of those who attend Learning @ Scale; second, the combined bibliometric approach could help other emerging subfields to better locate their more expansive community and related literature. Studying such newer subfields is difficult because clear communities around a single journal or conference have not yet formed, so we must rely on other sources and methods. Our analyses use the proceedings of the Annual Workshop on A/B Testing and Platform-Enabled Learning Research (which occurs at the ACM Learning @ Scale Conference each year) as a seed because submissions are open (whereas the set of funded projects are more restrictive). By analyzing a combination of social ties (co-authorship), shared intellectual foundations (bibliographic coupling), and semantic similarities (embeddings) analyses, we map the people, publications, and ideas that help shape this emerging area. In doing so, we demonstrate how bibliometric techniques can be used in service of solidifying and growing emerging subfields of research.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2026
Accession Number: ED682371
Database: ERIC
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED682371
    Name: ERIC Full Text
    Category: fullText
    Text: Full Text from ERIC
Header DbId: eric
DbLabel: ERIC
An: ED682371
AccessLevel: 3
PubType: Report
PubTypeId: report
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Bibliographic Analysis of Digital Learning Platforms as Research Infrastructure
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zak+Risha%22">Zak Risha</searchLink><br /><searchLink fieldCode="AR" term="%22Jeremy+Roschelle%22">Jeremy Roschelle</searchLink><br /><searchLink fieldCode="AR" term="%22Digital+Promise%22">Digital Promise</searchLink><br /><searchLink fieldCode="AR" term="%22Empirical+Education+Inc%2E%22">Empirical Education Inc.</searchLink><br /><searchLink fieldCode="AR" term="%22Institute+of+Education+Sciences+%28ED%29%22">Institute of Education Sciences (ED)</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Digital+Promise%22"><i>Digital Promise</i></searchLink>. 2026.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Digital Promise. 1001 Connecticut Avenue NW Suite 935, Washington DC 20036. Tel: 202-450-3675; e-mail: contact@digitalpromise.org; Web site: https://digitalpromise.org/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: N
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Information Analyses
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Research%22">Educational Research</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Modern learning at scale relies on data at scale. Methods for using learning platform data have been evolving, particularly to allow comparative or experimental research within existing digital learning platforms (DLPs). By providing researchers with large-scale data from active learners, DLPs can reduce costs to collect data, pilot new interventions, and run A/B comparisons, enabling Learning Engineering to yield improvements that benefit student learning. This potential has given rise to an emerging DLPs-as-research-infrastructure subfield, with support from multiple funders. Progress depends on community-building and collaboration, as strong relationships are necessary to develop sound methods and approaches as well as to utilize the available data well. This subfield does not yet have a dedicated journal, conference, or scholarly society that serves a broader community. And yet the DLP-as-research-infrastructure subfield is aware of broader connections and precedents for its work. To understand these connections and precedents, a bibliometric analysis was conducted. Reporting on the bibliometric analysis yields two contributions: first, the immediate findings can advance the work of those who attend Learning @ Scale; second, the combined bibliometric approach could help other emerging subfields to better locate their more expansive community and related literature. Studying such newer subfields is difficult because clear communities around a single journal or conference have not yet formed, so we must rely on other sources and methods. Our analyses use the proceedings of the Annual Workshop on A/B Testing and Platform-Enabled Learning Research (which occurs at the ACM Learning @ Scale Conference each year) as a seed because submissions are open (whereas the set of funded projects are more restrictive). By analyzing a combination of social ties (co-authorship), shared intellectual foundations (bibliographic coupling), and semantic similarities (embeddings) analyses, we map the people, publications, and ideas that help shape this emerging area. In doing so, we demonstrate how bibliometric techniques can be used in service of solidifying and growing emerging subfields of research.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: CodeSource
  Label: IES Funded
  Group: SrcInfo
  Data: Yes
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: ED682371
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=ED682371
RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
    Subjects:
      – SubjectFull: Electronic Learning
        Type: general
      – SubjectFull: Educational Research
        Type: general
      – SubjectFull: Educational Technology
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
    Titles:
      – TitleFull: A Bibliographic Analysis of Digital Learning Platforms as Research Infrastructure
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Digital Promise
      – PersonEntity:
          Name:
            NameFull: Empirical Education Inc.
      – PersonEntity:
          Name:
            NameFull: Institute of Education Sciences (ED)
      – PersonEntity:
          Name:
            NameFull: Zak Risha
      – PersonEntity:
          Name:
            NameFull: Jeremy Roschelle
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Type: published
              Y: 2026
          Titles:
            – TitleFull: Digital Promise
              Type: main
ResultId 1